Patent Description:
Three-dimensional ("3D") scanners are widely used in different industries such as additive and subtractive manufacturing, aerospace, automotive, consumer goods, industrial goods, orthodontics, orthopedics and related sectors and industries. In addition, three-dimensional scanners are commonly used in Augmented Reality ("AR") and Virtual Reality ("VR") devices. Each application requires certain features and qualities in the 3D model that is developed. For example, in orthopedics and orthodontics, accuracy and precision are important parameters for the medical specialists. In AR and VR, the color data (red, green, blue ("RGB") point cloud) of the model is often more important than other factors.

A patient's arm or foot is immobilized by circulating roles of plaster, resin or fiberglass around the impacted anatomy when casting the patient's injured arm, foot, leg or other body part. Although it can be applied in a very short time, there are shortcomings with prior art casting and methods. Conventional casts are not resistant to water and lose their properties over time, which can have a negative impact on patient outcomes. The conventional plaster casts severely limit activity of the patient by requiring avoidance of water, limiting sweating and otherwise avoiding any activity that could introduce foreign substances into the space between the cast and the patient's skin. Another problem is that there is no lattice or holes in the cast to let air flow though the cast to avoid or limit sweating and subsequently avoid or limit skin itching, as well as to monitor and treat the patient's skin. The lack of lattice or holes also prevents foreign substances from being removed from the patient's skin and can cause severe irritation by rubbing against the patient's skin. The patient's skin may require or benefit from treatment as the result of the event causing the patient's injury or as a result of foreign object irritation while wearing the cast. In the conventional casting system and process, the patient has to keep his or her arm or foot still for a long time before the cast dries and hardens, whereas the scanning process may be completed in shorter amounts of time.

Laser scanners may be constructed or adapted for portability or may be constructed or adapted for hand-held use. The laser scanner may be coupled to a robotic arm or Articulated Arms Coordinate Measuring Machines (AACMM) for automated processing. For these scanners, the operator or the robotic arm turns or moves the object around to capture all or most of the points on the surface of the object. In these rotating 3D scanners, the process of capturing all or most points for the hand or foot takes between one to five minutes (<NUM>-<NUM>), which is a significant period of time for a typical patient to hold the arm, foot or other body part in a generally immobile position without movement. There are other methods of 3D scanning such as photogrammetry in which ten to twenty (<NUM>-<NUM>) digital single-lens reflex ("DSLR") cameras are set at fixed positions around the object to capture two-dimensional ("2D") images and the 2D images are merged into a 3D point cloud or to facilitate development of a 3D model. Although this method is relatively quick during processing, it is not a cost-effective process for medical applications. This particular system and method also require a designated space for the set-up, which may not be possible in all medical facilities. There is another type of photogrammetric 3D-scanning in which one camera is turned around the object to provide twenty to forty (<NUM>-<NUM>) photos of the object. This method is cost-effective but lacks speed, accuracy, precision and is mostly used for in-home applications. The preferred 3D scanner and related methods address the shortcomings of the prior art devices and methods to provide an accurate scan of a patient's anatomy, transform the scan into a 3D model and define a cast or splint based on the 3D model. Document <CIT> discloses a contactless optical inspection system for measuring the roundness and straightness of pipes, consisting of a support structure, a portal with multiple optical sensors for detecting pipe contours, adjustable positioning mechanisms, and an evaluation unit to determine the full circumference profile and straightness along the pipe's length.

Briefly stated, the preferred apparatus for anatomic three-dimensional ("3D") scanning and automated three dimensional cast and splint design may be comprised of any capturing device that can be used as a capturing system, preferably for capturing a 3D image of a patient's anatomy. The camera is used as a general word in this description that will not be limited specifically to the camera devices, but may also encompass scanners, probes, x-ray imagers and related systems that are able to acquire data to develop a 3D image or model preferably of the patient's anatomy. Any similar names to this such as webcam, imager, scanner and related features are possible to be used for operation of the preferred system. The number of cameras may vary in this process based on the complexity of the object geometry input.

The process of casting an injured body limb may comprise 3D scanning, 3D model creation, designing process, 3D printing, and post-printing processing.

The preferred 3D scanner machine relates generally to a scanning apparatus that emits laser beams on the object and captures the reflections by a number or array of cameras. The lasers and the cameras move on a linear track relative to a patient's anatomy to cover different parts of the object or to develop the 3D model, preferably of the patient's anatomy that the physician is treating. During this scanning or imaging process, the camera(s) preferably captures the two-dimensional ("2D") images of the object and sends the raw image data to the computer, controller or central server. The images may also be otherwise captured, such as by stereoscopic imaging techniques or stereoscopic photography. The software of the computer, controller or central server processes the captures or acquired images and constructs the three-dimensional digital file of the object, preferably the impacted or injured portion of the patient's anatomy.

Physicians, providers, and techs use the 3D digital file, preferably a model of the hand, arm, foot, torso, hips, shoulder and other parts of the limbs or body of the patient, to design and 3D-print custom-made cast, splint, orthopedic brace, and related medical device. The process of designing is performed by creating a surface over the <NUM>-D scanned part which is used to design the cast, splint, brace or other medical device for patient treatment. Moreover, this process includes merging other elements such as locks, lattice engraving, attachments such as smart devices/microchips/sensors, access ports, stimulation ports or any identity documents for printing on the surface of the medical device and related features.

An accurate and a high precision resolution output model is preferred in orthopedics. In addition to accurate and high precision, the process of capturing images should be as quick as possible to avoid anomalies or errors resulting from the patients' arm, foot or other anatomy moving or shaking during scanning. It is also more convenient for the patient to be scanned in the shortest possible time which enhances the ability to produce an accurate scan as the body remains immobile during scanning.

Scanning and adaptable casting, as is described herein makes it possible for the patient to wear the cast, brace, splint and etc. on his/her own. The preferred lattice and locks on the computer-based and designed casts or splints alongside with resistance of the 3D printing materials to water and other potential contaminants, make the 3D printed casts and splints a better alternative to traditional casting, especially for patients that require a longer time of immobilization or temporary immobilization over predefined time periods over a long timeframe, such as wearing the case, brace or splint only at night.

In another aspect, the preferred invention is directed to a scanner system for capturing and making a 3D digital file of an object. The captured object can be any object fitting inside the area of the scanner, such as an arm, leg, foot, joint, torso, shoulder or other anatomical feature of a patient. The preferred scanner system is able to scan the healthy or injured body limbs or other object and develop a 3D model of the scanned object or anatomy. However, the preferred system and method of this 3D scanning system is not limited to the embodiments and methods described herein but is able to take on variations that would be apparent to one having ordinary skill in the art based on a review of the present disclosure.

The three-dimensional scanner of the preferred embodiments is designed to capture points on the surface of an object with a high precision and fast operation, preferably less than ten seconds (<NUM>). This preferred apparatus uses at least one laser beam as a projector and reflecting mirrors to cover the bottom, top, left and right side of the object. Three laser beams, for example, may illuminate the different slices of the object and preferably at least four Complementary Metal Oxide Semiconductor ("CMOS") cameras close to the lasers capture the reflected points from the object. The preferred embodiments of the present invention are not limited to inclusion of the three laser beams and the four CMOS cameras for operation and may utilize alternative imaging and sensing systems, mechanisms and methods for capturing the data for creation or development of the 3D model.

The preferred scanning machine includes three (<NUM>) main cameras. Backup cameras, however, may be used to cover blind spots of the preferred main cameras in complex geometries or for capture of additional details of the object. The backup cameras are preferably active in situations where the main cameras miss or could potentially miss a blind spot on the object and the central processor may direct the main and backup cameras to take images, based on initial images from the main cameras, input from a technician or user or based on other factors related to the object or the particular scanning situation. If the central processor or technician determines that the initial scan does not capture portions of the object or there is a potential blind spot, the backup camera preferably verifies the same coordinates to complete the point cloud of the object. This process preferably eliminates the need to repeat the scanning when there are backup cameras, because the backup cameras are able to address potential blind spots or limits to resolution of the object. The raw image data acquired from the cameras are preferably sent to the central processor for further processing in the 3D-scanner customized software. After analyzing the raw 2D images, the preferred software converts those images to the point cloud and constructs the 3D digital file in the preferred format for the user. It is then possible to use the end model to design custom-made casts, splints, braces, medical devices and similar orthoses or prostheses with this software automatically or manually, engraving shapes, locking mechanism insertions and related features on the casts and/or splints.

The preferred 3D scanning mechanism can generate an orthopedic cast, brace, and splint (orthoses) with the included software in an automated or manual method based on the input features, such as wounds, deformities, irregularities and related features of the object, also with 2D images based on the extracted features. Soft computing techniques are utilized in offline and online methods to generate custom-made casts with inserted lock, size, lattice and patients' desirable shapes and textures, as well as potentially additional features. Appendicular and built-in parts (such as sensors, clasp, locks, etc.) are also placed through the user interface or by artificial intelligence ("A. ") algorithms or computation methods. The preferred system also predicts the mechanical properties and optimizes the 3D-printing parameters in the cast design under the pre-defined constraints. Augmented reality is preferably utilized in the process to automatically visualize and guide the customer, technician or user.

The preferred invention is directed to a virtual fitting of prefabricated orthoses, such as braces, splints, neck collars, boots, knee immobilizers and related prefabricated splints, braces and casts. The orthoses may be designed and developed by third parties or may be designed and developed by the manufacturer or designer of the preferred apparatus for anatomic three-dimensional scanning and automated three-dimensional casts and splints.

In another aspect, the preferred invention is directed to a method of processing and making a custom splint, cast, braces or other orthopedic support based on a pre-printed model, such as a hand model, or pre-printed base orthotic model. A Machine learning algorithm finds the closest 3D base model of the pre-printed base models with classification and parameter estimation. An application on a mobile device or a website page runs a process for capturing images/videos of the patient's body part and generating 2D models with estimated parameters for the virtual 3D model that is utilized to select the appropriate base model from the inventory of base models. The process then utilizes any material that is used in making casts, braces, or splints through cutting, machining or otherwise using additive or subtractive manufacturing techniques to obtain the proper shape for the specific patient's body part. The template or selected base model is then shaped on the generated patient model or inventory model template to produce a final cast or splint for application to the patient's body part. Post-production shaping and modification may be automated by heating the model or other means of affecting the properties of the orthoses, such as 3D printing, machining or otherwise manipulating the 3D base model to define the final cast or splint.

In another aspect, the preferred invention is directed to a scanner system for capturing a three-dimensional model of an object, preferably a body part of a patient, including lasers providing a stripe of light to illuminate the object, capturing devices to capture 2D images of the object, a central processor configured to receive data collected from the lasers and capturing devices and send commands and data, an actuation mechanism including a motor and encoders configured to move the capturing devices and the lasers, a graphical user interface to process the 2D images and construct a 3D model, and a nozzle that builds the generated 3D model in a scanner chamber which has a heat-bed on a scanning plane. The scanner system also includes mechanical elements, including a belt, ball bearings, a roller bearing, position sensors and a coupling component to attach the belt to moving planes and three planes to fix the cameras and lasers while moving on a track. The capturing devices include cameras. The graphical user interface is configured to navigate over a 3D model of the object. The scanner system utilizes 3D-printing parameters optimization, mechanical properties prediction, mesh post-processing, and the data for orthopedic applications such as designing cast, splint, braces and orthoses.

In a preferred scanner system, the cameras are configured to capture main points of the object. The cameras include a backup camera and main cameras. The backup camera is configured to cover blind spots of the object hidden from the main cameras. The nozzle of the preferred system is configured to construct the 3D model with photogrammetry techniques based on the images captured by the cameras. The central processor of the system preferably detects missing points of the data related to the object and actuates a backup camera of the cameras to automatically perform a task, such as collecting data related to a hidden area of the object. The number of cameras utilized in the preferred system is not limiting and the number of cameras can be decreased or increased without significantly impacting the performance of the scanner system in appropriate design circumstances. The lasers and cameras are preferably configured to scan a side of the object with a main camera of the cameras and a backup camera. The preferred central processor is configured to conduct post-processing including an outer base design such as a cast, a splint, a brace, lock insertions, a mesh inspection and basic operators. The central processor is preferably configured to attain a faster scanning operation utilizing an additional main camera and a backup camera. The number of lasers utilized in the preferred system is not limiting and the number of lasers can be decreased or increased. The preferred lasers and cameras are configured to scan a side of the object utilizing a first laser of the lasers. The lasers preferably include additional lasers to switch the scanner system into a multi-laser operation mode. The central processor is configured in the preferred embodiments to acquire valid models of the object with possible movement or shake of the object during scanning by decreasing a scanning period to five (<NUM>) seconds or a reasonable time period that limits model errors resulting from movement of the object during scanning. The preferred scanner may alternatively include post-processing software corrections designed to account for movement or shaking of the object during scanning. The scanner system of the preferred embodiments further includes an automated uniform-making process to reconstruct incomplete surfaces of the object during scanning by utilizing a main camera and a backup camera of the cameras.

The scanner system of the preferred embodiments may include a rate of photo capturing of the cameras, wherein the rate of photo capturing is approximately eighty (<NUM>) frames per second. The rate of photo capturing of the preferred scanner system facilitates capture with a step-size of approximately one millimeter for the object. A speed of scanning of the scanner system can preferably be increased based on the actuation mechanism speed. A scanning size of the preferred scanner system can be varied based on a size of the scanner chamber.

The preferred central processor includes an algorithm that reconstructs orthopedic casts, splints, and braces automatically based on a prescribed size, application, features such as deformities, ulcers, sores, wounds, and related features automatically or manually. The preferred algorithm of the scanner system is utilized to optimize 3D-printing parameters such as infill percent, lattice shape, shell size, speed, raster angle, orientation, and related features based on desired mechanical output properties including shear, compressive strength, flexural strength, surface roughness, and 3D-printing time-cost model. The scanner system of the preferred embodiments may also include a machine that is trained with soft computing techniques to predict mechanical properties of the 3D model including shear, compressive strength and flexural strength. The algorithm of the preferred embodiments in the central processor predicts a size of the object based on previously trained data with features of limbs, body parts, age, sex, and any curves in the object. The algorithm also preferably generates a 3D file of an orthopedic cast, splint, or brace based on predicted features of the object, wherein the object is comprised of a hand or other body part of the patient and the predicted featured is based on hybrid soft computing techniques. The preferred algorithm is comprised of a learning algorithm to classify input object features including wounds, deformities, sores, and related features to reconstruct the 3D model. The 3D model produced from the preferred algorithm is comprised of an orthopedic cast, brace or splint. The preferred algorithm is configured to locate electric pads or medical transducers in the 3D model based on predicted features. The algorithm is comprised of a preferred decision-making algorithm to decide final 3D-printing parameter sets in a Pareto-front optimal solution with many-objective optimization. The algorithm preferably includes online or offline learning methods to fit complex curves automatically on the 3D model based on unseen or trained features of a trained dataset. The preferred algorithm is configured to develop the 3D model based on a predicted cast, splint, or brace based on paired limbs and body parts. The algorithm is preferably configured to engrave the 3D model with a patient desirable texture or shape or to engrave the 3D model for any deformities, sores or wounds automatically or manually based on a user selection. The preferred algorithm is configured to insert markers into the 3D model for any injuries, medical records and/or prescribed notes with decision-making and natural language processing to customize the 3D model in size, shape, engravement, pattern, length, markers, and/or related features. The algorithm preferably includes AR and VR configured to visualize the 3D model and guide a scanning technique for the patient in a scanning process. The algorithm is preferably configured for feature selection, and any customizable casting parameters including lattice shape, engravements, markers and/or related features. The algorithm integrates a preferred scanning process with additive manufacturing G-code to print the 3D model in the scanning chamber.

Capturing devices of the preferred scanner system include an X-ray integration to overlay a skin surface of the object for feature extraction, bone 3D model and a parametrization system. The capturing devices of the preferred scanner system include feature detection and body part pattern recognition, wherein the capturing devices capture keypoints and an algorithm conducts probability mapping to find features for automated mesh processing including drawing cutting lines, making contours, curvatures, skeleton mapping and/or related features. The preferred algorithm is configured for visualizing a corrected position of the object, wherein the object comprised of a limb of the patient and the algorithm is configured to adjust limb position. The algorithm includes a modified training machine in the preferred embodiments that learns and updates a network, customers' keypoints modification for a cutting line and mesh-processing to improve accuracy for further keypoints detection and probability function. The central processor of the preferred scanner system includes telemedicine capability with a direct uplink of one of images and radiographic image overlay to an x-ray image. A patient's medical records are preferably integrated into the central processor when the scanner system is being utilized to construct a cast, splint, brace or other support for the patient.

The capturing devices of the preferred scanner system include at least one of an x-ray machine, an x-ray generator, an x-ray detector, a medical imager, a radiography machine, a computed tomography ("CT") scanner, a positron emission tomography ("PET") scanner, a single-photon emission computed tomography ("SPECT") scanner, an x-ray tomographer, and/or a backscatter x-ray scanner. The data utilized with the preferred scanner system includes data from the x-ray machine, wherein the data from the x-ray is overlayed by the data from the cameras and lasers to define the 3D model. The central processor is preferably configured to develop an augmented reality file of the 3D model, wherein the 3D model is comprised of a joint of the patient's anatomy. The preferred augmented reality file is configured to facilitate visualization of a corrected position of the injured limb and permits adjustment of the position of the limb. The central processor of the preferred scanner system is configured to facilitate telemedicine by providing direct uplink of the 3D model and data for review by a remote medical professional.

In yet another aspect, the preferred invention is directed to an apparatus to provide multi-entries for a patient's limbs in a scanner system having a scanning chamber. The preferred apparatus includes a transparent glass on a surface of the scanning chamber, a cloth and zippers on a housing surrounding the scanning chamber, a table and a chair designed for the patient to allow inserting the patient's limbs into the scanning chamber from the entries, a hydraulic or a mechanical leveling bed to level a height of the object based on a height of the patient and a rigid cover over the scanning chamber to prevent an ambient light from entering into the scanning chamber. The zippers are configured to function as entries for the patient's limbs into the scanning chamber. The preferred apparatus also includes a moving plane, a first plane connected to the moving plane and a second plane connected to the moving plane. The first and second planes extend generally perpendicularly relative to the moving plane and the moving plane, first plane and second plane are positioned in the scanning chamber. The first and second planes are movable relative to the moving plane to adjust the positioning of cameras and lasers attached to the first and second planes.

A three-dimensional laser scanner device specialized for all objects including body parts such as a finger, hand, forearm, elbow, arm, foot, leg, knee, thigh, shoulder, torso, etc. The laser scanner machine preferably includes a number of capturing devices, lasers, mechanical actuators to move the scanning mechanism, three moving planes containing capturing devices, stationary capturing devices, such as an array of cameras and lasers, shafts, ball bearings, micro switches and related equipment. The laser scanner machine preferably collects the data in the form of 2D photos reflected from the laser beams onto the objects and the correspondent software collects the image data and analyzes it to construct a 3D model of the object or the body part based on software and computing methods with online and offline learning techniques. The included computing method can generate orthopedic casts, braces, splints, molds, templates and related items based on the trained dataset with various related features as deformities, wounds, sores, and patient-related features to reconstruct and visualize the final 3D-model with an appropriate size, type, specific engravements and related features. The physician and technician may also input information to the controller of the laser scanner machine, such as condition of the patient's skin on the scanned body part, location of a bone break or crack and related physiological or conditional features. The preferred laser scanner device is designed to construct the 3D model of the body part in a very short time, limiting involuntary body movements that may damage the real size of a 3D reconstructed body part, which can also be used for designing a customized cast, splint, or brace, or other medical or data collection purpose. The 3D laser scanner device may model an opposite body part, such as the left hand for a right-hand cast or splint, if the subject body part has suffered trauma and has an irregular shape or is swollen. The high speed of the preferred 3D scanner makes the scanning process fast and convenient for the patient while he/she holds the body or body part still for a relatively short time. The preferred 3D scanner can be applied for children where staying still for a relatively short time allows capturing enough reliable data to construct the 3D model of the body part. This high-speed scanning method has the advantage to finish the process before any tremor, movement or vibration in the body part can damage and distort the model. Automated orthosis design, cloud/electronic medical record interface for recording, body part analysis based on patient's clinical features, patient convenience, high speed, mechanical properties output prediction, 3D-printing parameters optimization, cost-effective scanning, and data reliability are preferred features of 3D scanner and method described herein.

In a further aspect, the preferred invention is directed to a scanner system for capturing a 3D model of an object. The scanner system includes a laser and a camera to capture 2D images of the object. The system also includes a tube mounted to a rail, a central processor configured to receive data collected from the laser and the camera and an actuation mechanism configured to move the tube along the rail. The tube is configured to move generally along a travel axis of the rail. The tube includes open first and second tube ends. The laser and camera are mounted inside the tube between the first and second tube ends. The first tube end includes a first continuous ring, and the second tube end includes a second continuous ring. A channel extends through the tube between the first and second rings positioned adjacent the rail in an assembled configuration. The movement of the tube relative to the patient's body part facilitates relatively accurate scanning of the patient's body part, as the body part remains still while the tube moves relative to the body part.

In an additional aspect, the preferred invention is directed to a method of constructing a custom splint, cast or brace based on a pre-printed model, such as a pre-printed hand model. The method includes the steps of storing a plurality of 3D base models, receiving images of a body part of a patient, analyzing the images of the body part with a machine learning algorithm, selecting a fitting 3D base model from the plurality of 3D base models based on the analysis of the images of the body part and manipulating the fitting 3D base model based on the analysis of the images of the body part.

The foregoing summary, as well as the following detailed description of preferred embodiments of the scanner system, instrument, implant and method of the present disclosure, will be better understood when read in conjunction with the appended drawings. For the purposes of illustrating scanner system, 3D scanning and automated 3D cast and splint design and related methods, there are shown in the drawings preferred embodiments. It should be understood, however, that the application is not limited to the precise arrangements and instrumentalities shown. In the drawings:.

Certain terminology is used in the following description for convenience only and is not limiting. Unless specifically set forth herein, the terms "a", "an" and "the" are not limited to one element but instead should be read as meaning "at least one. " The words "right," "left," "lower" and "upper" designate directions in the drawings to which reference is made. The words "inwardly" or "distally" and "outwardly" or "proximally" refer to directions toward and away from, respectively, the patient's body, or the geometric center of the preferred anatomic three-dimensional scanning and automated 3D cast and splint design, 3D scanner system and related parts thereof. The words "anterior," "posterior," "superior," "inferior," "lateral" and related words and/or phrases designate preferred positions, directions and/or orientations in the human body to which reference is made and are not meant to be limiting. The terminology includes the above-listed words, derivatives thereof and words of similar import.

It should also be understood that the terms "about," "approximately," "generally," "substantially" and like terms, used herein when referring to a dimension or characteristic of a component of the preferred invention, indicate that the described dimension/characteristic is not a strict boundary or parameter and does not exclude minor variations therefrom that are functionally the same or similar, as would be understood by one having ordinary skill in the art. At a minimum, such references that include a numerical parameter would include variations that, using mathematical and industrial principles accepted in the art (e.g., rounding, measurement or other systematic errors, manufacturing tolerances, etc.), would not vary the least significant digit.

Referring to <FIG>, a first preferred 3D scanner or machine, generally designated <NUM>, and the described actuation mechanism that may be utilized with the scanner <NUM> is an exemplary description and not meant to be limiting. The first preferred 3D scanner or machine <NUM> may include nearly any actuation mechanism with a different type of control mechanism, processing units, motors, mechanical elements and other features that facilitate performance of the preferred functions, operation in the normal operating conditions of the first preferred scanner <NUM> and functioning within the preferred size and shape of the scanner <NUM>. The interface between components of the first preferred scanner <NUM> that communicate with each other to support operation of the scanner <NUM> can be in a cable, wireless, Bluetooth, or any similar technologies.

Referring to <FIG>, the 3D scanner <NUM> of the first preferred embodiment includes an interior scanning mechanism, which is preferably placed inside a scanning chamber to be in a dark space, away from the ambient lights. The 3D scanner <NUM> is not so limited and may be operated without a dark space without significantly impacting the operation of the preferred 3D scanner <NUM>. The interior scanning mechanism preferably includes a first or left laser <NUM>, a second or right laser <NUM> and a third or bottom laser <NUM> that operate to illuminate the object from multiple angles. The first, second and third lasers <NUM>, <NUM>, <NUM> radiate a narrow beam to the object positioned within the scanning chamber to reveal points on the object surface in a line by line manner. In addition, in the first preferred embodiment, there are three main cameras <NUM>, <NUM>, <NUM> to capture points of the object in addition to a backup camera <NUM>. The main cameras <NUM>, <NUM>, <NUM> are placed in a way that they have preferably the least intersection in their captured set of points. These cameras include a first or right camera <NUM>, a second or left camera <NUM> and a third or bottom camera <NUM>. The backup camera <NUM> has a major intersection in the set of captured points with all the main cameras <NUM><NUM>, <NUM>. The purpose of using the preferred backup camera <NUM> is to cover blind spots that might be hidden from the view of one or two of the main cameras <NUM>, <NUM>, <NUM> in some places on the object. The lasers <NUM>, <NUM>, <NUM> and cameras <NUM>, <NUM>, <NUM> preferably move together at the same distance from each other during operation based on the functioning of the internal scanning mechanism. The bottom camera <NUM> and bottom laser <NUM> are preferably placed on a moving plane <NUM>. In the first preferred embodiment, the moving plane <NUM> is constructed of a generally C-shaped structural member that supports the cameras <NUM>, <NUM>, <NUM>, <NUM> and the lasers <NUM>, <NUM>, <NUM>. The moving plane <NUM> is preferably moved relative to a frame <NUM> of the scanner <NUM>.

Referring to <FIG>, <FIG>, <FIG>, the interior scanning mechanism includes an actuation mechanism or actuators <NUM> that drive or control the movement of the moving plane <NUM>, as well as the attached lasers <NUM>, <NUM>, <NUM> and cameras <NUM>, <NUM>, <NUM>, <NUM>. The actuation mechanism <NUM> of the first preferred embodiment includes a motor <NUM>, such as a stepper motor <NUM>, that operates after receiving an initiation command from a processing unit or central processor <NUM>. The moving plane <NUM> is preferably connected to a belt or driving mechanism <NUM> that is driven by the motor <NUM>. As the motor <NUM> is actuated by the central processor <NUM>, the belt <NUM> starts to turn around a roll bearing <NUM> as the stepper motor <NUM> starts to work. The belt <NUM> is coupled to the moving plane <NUM> with a coupling component <NUM>, such as a clamp, magnet, clip or other fastening mechanism or assembly that secures the belt <NUM> to the moving plane <NUM>. Thus, when the stepper motor <NUM> starts to work, the moving plane <NUM> preferably moves on a linear track guided by the shafts <NUM>(a), <NUM>(b), although the moving plane <NUM> is not so limited and may otherwise move, such as in rotation or other moving paths to capture images of the object in the interior of the scanner <NUM>. The actuation mechanism <NUM> also preferably includes ball bearings or fittings <NUM>(a), <NUM>(b), <NUM>(c), <NUM>(d) that are separately shown in <FIG>. The ball bearings <NUM>(a), <NUM>(b), <NUM>(c), <NUM>(d) are connected to the moving plane <NUM> and move along shafts <NUM>(a), <NUM>(b) that are attached to the frame <NUM> to guide the preferred linear movement of the bottom plane <NUM>. The bottom plane <NUM> preferably includes a first or right plane <NUM>, a second or left plane <NUM> and a third or base plane <NUM>. The base plane <NUM> is preferably connected to the coupling component <NUM> to drive movement of the bottom plane <NUM>. The third or bottom laser <NUM> and the third or bottom camera <NUM> are preferably connected to the third or base plane <NUM>, the second or left camera <NUM>, the backup camera <NUM> and the first or left laser <NUM> are connected to the second or left plane <NUM> and the first or right camera <NUM> and the second or right laser <NUM> are attached to the first or right plane <NUM> in the first preferred embodiment. A microswitch <NUM> is preferably placed at an end of the scanner <NUM> or at an end of the frame <NUM> relative to the movement of the moving plane <NUM> to control the movement of the moving plane <NUM>. A lid <NUM>, which may be constructed of a transparent structural member, such as a Plexiglas or generally transparent sheet, is preferably placed at a top of the scanner <NUM> and defines a portion of the frame <NUM>. The lid <NUM> preferably permits visualization of the anatomical body part or object that is placed into the scanner <NUM> during use.

Referring to <FIG>, the mechanical components of the scanner <NUM>, excluding the moving plane <NUM> and the first or right and second or left planes <NUM>, <NUM> and the upper portion of the frame <NUM>, includes the motor <NUM>, which may be comprised of the stepper motor <NUM>, the roll bearing <NUM> and the two shafts <NUM>(a), <NUM>(b). The belt <NUM> is driven by the stepper motor <NUM> and guided by the roll bearing <NUM>. The ball bearings or fittings <NUM>(a), <NUM>(b), <NUM>(c), <NUM>(d) guide the linear movement of the moving plane <NUM> on the shafts <NUM>(a), <NUM>(b). The frame <NUM> supports the stepper motor <NUM>, the shafts <NUM>(a), <NUM>(b) and the roll bearing <NUM>.

Referring to <FIG>, during operation of the scanner <NUM> in a preferred first step <NUM>, the user enters the patient's specifications such as the left or right foot or arm, age, name, and related patient information into the software of the scanner <NUM>, which is preferably housed in the processing unit or central server <NUM>. After pressing a scan button, the scanning process starts. The processing unit or central server <NUM> of the computer <NUM> sends commands to the scanner <NUM>. The processing unit or central server <NUM> may be comprised of a wireless microcontroller that is connected to the scanner <NUM> and drives the stepper motor <NUM>. The cameras <NUM>, <NUM>, <NUM>, <NUM> and the lasers <NUM>, <NUM>, <NUM> start to move in the scanning area around the patient's limb or the object and along the linear track defined by the shafts <NUM>(a), <NUM>(b). This actuation mechanism startup <NUM> step is driven by the processing unit or central processor <NUM>. The lasers <NUM>, <NUM>, <NUM> illuminate the object with laser beams and the images are captured using the cameras <NUM>, <NUM>, <NUM>, <NUM>. An interface <NUM> then sends the collected data to the processing unit <NUM> and the scanner software. In the software, the raw data is processed in an image processing or raw data step <NUM> so that the 2D images are converted to the 3D coordinates and the point cloud of the object. Following the initial scan, the central server <NUM> analyzes the collected data to determine if there are potential missing or underdeveloped areas of the object. If the central server <NUM> determined there are missing points in the point cloud, the software analyzes and constructs the parts of the missing points on the object. The central server or processor <NUM> then refers to collected data from the backup camera <NUM> and preferably covers the missing parts of the point cloud of the object with the data of the backup camera <NUM> to edit the point cloud in the point cloud reconstruction step <NUM>. In an analysis of the object model or qualified step <NUM>-<NUM>, if the output does not satisfy predetermined constraints, potentially including resolution, object size and related constraint, an error will notify the user control to perform the scanning again and the central server <NUM> will direct the scanner <NUM> back to the capturing and laser projection system step <NUM>. After this potential point cloud modification, the constructed file is converted to a 3D mesh from point cloud in a point cloud step <NUM>, the mesh is further processed in a mesh post-processing step <NUM>-<NUM> and the 3D mesh is finalized in a 3D digital file output step <NUM> so that the mesh can be exported in the desired format to the user. The mesh file is then saved in the central server <NUM> and classified automatically considering the specifications of the patient. The mesh post-processing step <NUM>-<NUM> is to perform the mesh processing including smoothing, outer base design to make orthopedic casts on the specified surface on the mesh, locking mechanism insertions, engraving shapes, and basic processing as mesh subtractions, intersections, and related steps. This mesh post-processing step <NUM>-<NUM> can be performed automatically or manually. The sample Pseudo Code for this process is described in the followings:.

A preferred and exemplary pseudocode for the 3D scanner <NUM> may include the following steps:.

Referring to <FIG> and <FIG> the scanner <NUM> interfaces with the central server or processor <NUM> initially after the user depresses the start button or by the start command. The start command is sent to the central server <NUM>, which may include a microcontroller, through a specified protocol of communication from the computer <NUM> to the central server <NUM> in a communication step <NUM>. The central server <NUM> drives the stepper motor <NUM> or the mechanical actuation system <NUM> in a driver step <NUM>. The stepper motor <NUM> starts running, the lasers <NUM>, <NUM>, <NUM> and cameras <NUM>, <NUM>, <NUM>, <NUM> are activated through a synchronization command step <NUM> that is driven by the central server <NUM>. After capturing 2D images of the object, the cameras <NUM>, <NUM>, <NUM>, <NUM> send the raw data of the acquired data to the computer <NUM> and central server <NUM> through a communication mechanism or method <NUM>, such as cables, wireless communication or other communication systems or methods.

Referring to <FIG> and <FIG>, the scanner <NUM> may include a housing <NUM> constructed of a relatively transparent material, although not so limited and the housing <NUM> may be opaque, for viewing the body part or object during the capturing and laser projection systems step <NUM> and any other steps where the object is within the housing <NUM> and the user may want to observe the body part or object. The housing <NUM> may include zippers or portholes <NUM>(a), <NUM>(b), <NUM>(c), <NUM>(d), <NUM>(e), <NUM>(f) from which the object, such as an arm, foot or any other object is entered into the scanning chamber. The zippers <NUM>(a), <NUM>(b), <NUM>(c), <NUM>(d), <NUM>(e), <NUM>(f) preferably make it comfortable for the patients to enter their arm, foot or any other body part or object from any entry into the housing <NUM>. A specialist, the user or the patient can choose the most convenient entry based on the desired scanning body limb and patient comfort. By positioning and orienting all of the zippers <NUM>(a), <NUM>(b), <NUM>(c), <NUM>(d), <NUM>(e), <NUM>(f), the user has a significant set of options to decide where to enter the limb or the object into the scanning chamber. The zippers <NUM>(a), <NUM>(b), <NUM>(c), <NUM>(d), <NUM>(e), <NUM>(f) and their location are not limited to the zippers <NUM>(a), <NUM>(b), <NUM>(c), <NUM>(d), <NUM>(e), <NUM>(f) shown in <FIG> and may be otherwise designed and configured for relatively convenient insertion of the body part or object into the housing <NUM> for scanning. For example, a front side zipper <NUM>(e) may be the best option for entering a patient's foot into the scanning chamber, in order to guarantee the full-size scanning of the patient's foot. The zippers <NUM>(a), <NUM>(b), <NUM>(c), <NUM>(d), <NUM>(e), <NUM>(f) may be incorporated into or attached to the housing <NUM> by a cloth <NUM> that makes the entry into the housing <NUM> more flexible and accommodates the use of the zippers <NUM>(a), <NUM>(b), <NUM>(c), <NUM>(d), <NUM>(e), <NUM>(f). A processing unit is preferably comprised of the central server <NUM>, a driver for the stepper motor <NUM>, camera cables and other electrical components. The zippers <NUM>(a), <NUM>(b), <NUM>(c), <NUM>(d), <NUM>(e), <NUM>(f) of the first preferred embodiment include a left side zipper <NUM>(a), a left side top zipper <NUM>(b), a top zipper <NUM>(c), a right side top zipper <NUM>(d), the front side zipper <NUM>(e) and a right side zipper <NUM>(f), although the housing <NUM> is not limited to these preferred zippers <NUM>(a), <NUM>(b), <NUM>(c), <NUM>(d), <NUM>(e), <NUM>(f) and may include less or more zippers or portholes <NUM>(a), <NUM>(b), <NUM>(c), <NUM>(d), <NUM>(e), <NUM>(f), as well as alternative access structures, systems or mechanisms, such as a self-sealing port, swinging door or other access systems, mechanisms or methods without significantly impacting the structure or function of the preferred scanner <NUM>.

Referring to <FIG>, the scanner <NUM> may utilize a trained deep Convolutional Neural Network ("CNN") or related system or method that predicts the 3D-Model based on the collected data from the 2D-images. The preferred CNN is utilized to reconstruct an orthopedic cast, brace, and/or splint with the 2D images captured from the lasers <NUM>, <NUM>, <NUM> and cameras <NUM>, <NUM>, <NUM><NUM>. In addition, the central server <NUM> may also use the input images to detect wounds, sores, deformities, anomalies and related features of the object, preferably a patient's body part, which may then be utilized to reconstruct the orthopedic casts in a customized manner to conform to the input object features. The wounds, sores or deformities may be comprised of swelling, burns, lacerations or other anomalies of the object. The algorithm of the central server <NUM> is preferably used to train the preferred scanner <NUM>, as is described below. Activation function, loss function, model type and array sizes can be varied based on the input features:.

In the next preferred step, based on the input image or 3D-model the following code is preferably processed:.

Referring to <FIG>, a stride <NUM> is a layer including human features of deformities, sores, wounds and related features and a max-pooling <NUM> is a discretization downsampling process to reduce the dimensionality of input data from the cameras <NUM>, <NUM>, <NUM>, <NUM> and the lasers <NUM>, <NUM>, <NUM> to make features contained in the sub-regions binned. The preferred scanner <NUM> includes three dense networks <NUM>, although the scanner <NUM> is not so limited, as described herein that preferably generate the output which is preferably an array of output classes <NUM>.

Referring to <FIG>, the connection of four main parts including decision-making, parametrization, visualization, and 3D-printing is utilized with the preferred augmented reality/artificial intelligence of the preferred scanner <NUM>. The process is preferably controlled by an Artificial Intelligence and Augmented Reality core <NUM>. Parameterization <NUM> is preferably utilized and is comprised of a technique used to set-up the orthopedic cast or splint, such as length, mechanical properties, 3D printing parameters with multi-objective optimization, many-objective optimization, mass customization, reinforcement learning, and optimal control theories.

A decision making algorithm <NUM> is preferably used to adjust the 3D cast setting as length, mechanical properties, medical records, 3D-printing parameters, shape engravements and related features for each designed cast with the fuzzy system and reinforcement learning to update its parameters.

Casting factors as the place of engravements, lattice shapes, length, shape, type of the splint, cast or brace, lock insertions, boundary surfaces on the cast are each, preferably pre-defined based on the medical records, prescription and patient-specific customization on the physical shapes. A fuzzy system is known as a robust algorithm in decision making with a hybrid of a neural network to update its rules. Adaptive Network-Based Fuzzy Inference System ("ANFIS") may be utilized with the preferred scanner <NUM> to predict the above parameters with a nonlinear mapping. The challenge with ANFIS algorithm is the selection of the inputs, membership functions, inference engine to make a satisfied predictive performance for this system. This can be done in the following steps:.

The following code is a preferred python implementation for the convergence of this ANFIS that may be utilized with the scanner <NUM>:
<IMG>.

The backpropagation may be used to update the neural network rules based on the output errors for the preferred scanner <NUM>, as follows:
<IMG>
<IMG>.

In a preferred final stage, it is used to update the membership functions of the fuzzy system, including the variance and average of each gaussian membership function. The relation of the parameterization <NUM> of the objects and the decision making algorithm <NUM> are the gaussian membership functions which are transformed <NUM> into the fuzzy engine.

In order to conduct the visualization <NUM>, the output of construction <NUM> of the cast or splint, an Augmented Reality ("AR") is preferably utilized. In the following steps it guides and visualizes the output of the AI reconstructing machine, preferably as follows:.

Referring to <FIG>, <FIG> and <FIG>, patients are preferably able to sit on a chair <NUM> and scan their feet from an entry through the front side zipper <NUM>(e) or insertion of other body parts through the other zippers <NUM>(a), <NUM>(b), <NUM>(c), <NUM>(d), <NUM>(f). For patients with difficulties, the scanner <NUM> may be placed on a long table or other support structure.

The described scanner <NUM> is preferably configured for fast, high-precision, cost-effective, reliable, and convenient use. The scanner <NUM> of the first preferred embodiment is configured for use in orthopedic applications to scan body limbs and other anatomy of a patient. Most scanners are designed to have one or two of the mentioned advantages, but the described preferred scanner <NUM> in the present invention is preferably configured to cover orthopedic applications in body limb scanning. The described parameters are preferred parameters and features of an orthopedic 3D scanner.

The preferred scanner <NUM> is configured for scanning the arm, forearm, foot, fingers, torso and other body limbs of the body to construct a 3D model of the scanned object so that clinicians can design and 3D print a costume-made cast, splint or orthopedic brace. However, the application is not confined to scanning body limbs. In addition, it is possible to scan any other object as long as the object can fit inside the scanning chamber of the scanner <NUM> and the scanner <NUM> is otherwise able to scan the object.

Referring to <FIG>, the scanner <NUM> preferably includes an optimization step <NUM> and mechanical properties prediction algorithms. This algorithm is utilized to optimize 3D-printing parameters as infill, lattice, shell size, raster angle, and other factors that are preferred for the scanner <NUM>. In addition, optimization step <NUM> preferably predicts mechanical properties such as flexure, shear, compressive strength, roughness and related features. The description code is an instance of optimization and mechanical prediction of infill and lattice-flexural strength (3D-printing parameters). The functions and algorithms are not limited to the specific function, loss function, optimization, online or offline learning methods. The general soft computing techniques in this algorithm can be fuzzy, genetic, neural network, probabilistic reasoning, and any hybrid methods, such as the soft computing learning method step <NUM> or the parameterization step <NUM>.

Measuring a distance is a common criteria utilized in the field of optimal fitting. Numerical methods are typically utilized for these criteria minimization. Curve fitting is preferably used to find a fit for the collected data. Due to the collected data both in Infill and Lattice groups, two figures are proposed to illustrate the force-infill and force-lattice curves in a non-linear least square model infill/lattice strength step <NUM>. There are various non-linear models to find the best fit such as power series, Gaussian, polynomial, Fourier, exponential and related techniques. A power series with two terms is used in this curve-fitting, preferably defined by the following function: <MAT>.

Based on the above functions, two objective functions are preferably formulated to minimize Genetic Multi-Objective Optimization ("MOO") loss and satisfy the problem constraints in this algorithm which can be different based on the defined problems. Nondominated Sorting and Genetic Algorithm ("NSGA") may be utilized in Evolutionary Algorithms ("EA") that are preferably utilized to solve multi Pareto-optimal without converting to single-objective problems. However, disadvantages of this algorithm can be mentioned as high computational complexity of nondominated sorting, lack of elitism, and the need for specifying the sharing parameter ∂share. The NSGA-II algorithm, therefore, was developed to solve the criticisms in the first version of NSGA. This technique, however, can be replaced by any other optimization algorithms for the genetic multi-objective optimization step <NUM>.

The preferred algorithm for NSGA-II MOO is described in the followings:.

One of the soft computing methods which may be used with the scanner <NUM> of the preferred invention as a learning method is a Neural Network, preferably in a soft computing learning method step <NUM>. An Artificial Neural Network ("ANN") is a soft computing method inspired by the biological neural network. In ANNs, different layers including the different number of interconnected neurons perform special functions. This network consists of three layers of the input, hidden, and output layers. Hidden layers aim to relate the input and output nodes. The process of building the ANN structure is to update the initial weights of neurons. The widely used mathematical chain method for updating neuron's weights is called backpropagation that computes layers gradient iteratively. The number of layers, activation function, regression or classification can be varied based on the type of the desired output and input data features.

In this proposed neural network architecture of the soft computing learning method step <NUM>, it preferably has five layers including one input with two nodes (infill and lattice percentage), one output with one node (flexural strength), and three hidden layers, although this configuration is preferred the specific configuration not limiting. The type of classifier is preferably "sequentially" performed with the activation function for the input layer in a "relu" function with a uniform kernel initialization. The second layer preferably utilizes the relu activation function and a uniform kernel initialization. The third layer is preferably a dropout layer to cut-off to reduce the overfitting in this regularization network. This layer increases the network structure robustness of the inputs.

The preferred last layer, which is an output layer, is preferably a softmax activation function. This structure may utilize an ADAM optimizer and a categorical cross-entropy function to generate multi classes in this network. This transforms the regression to logistic regression for the output flexural strength prediction.

Cross entropy can be used in machine learning algorithms as a performance measure, as well as with the preferred scanner <NUM>. It is used based on the input probability and the given probability distribution to predict the true value of output. Logistic regression is used to classify the observed data into the possible classes. This can be categorized into two possible methods of binary classification and categorical classification. The categorical classification is used for more than two labels in the output. In this problem, three categories of low, medium, and high shear strength categories may be considered as a preferred categorization but is not limiting. In multi-class classification, a hot encoder is used to convert the multiple output labels into binaries. Afterward, the categorical cross-entropy performance measure is placed in the last neural network layer.

In this proposed structure shown as the non-linear least square model infill/lattice strength step <NUM>, two non-linear least square models are fitted with power series for the input mechanical data. Those functions including infill-force and lattice-force are optimized by the genetic multi-objective algorithm. In the parallel network, mechanical data is used to train the classification neural network. Then, the network is preferably validated with a K-fold cross-validation algorithm in a K-fold cross-validation step <NUM>.

This hybrid algorithm is used with the preferred scanner <NUM> to predict the output strength of new infill and lattice percent with PLA material in a predicted mechanical properties value step <NUM>. A transform model step <NUM> on MOO is preferably utilized to find the infill or lattice percent equivalents of the new data. Therefore, the solution space in the preferred embodiments may be represented, as follows: <MAT>.

In the above preferred array, Hinfillequivalent and Hlatticeequivalent are the substantially the same as infill or lattice percent respectively which are preferably determined via genetic MOO. The classes of similar strength are preferably automatically predicted by the trained neural network to find similar classes. The decision-making algorithm <NUM> uses the most voted class between the four above classes (H) to predict an output strength class of mechanical propertied output <NUM>.

Referring to <FIG>, as a non-limiting example utilizing the preferred scanner <NUM>, hand features are detected by the preferred scanner <NUM> in a preferred hand scanning technique, which is trained by the prescribed CNN network. The OpenCV and datasets of COCO Keypoints challenge, MPII Human Pose Dataset, VGG Pose Dataset, and local datasets are collected to train the network for feature detection with the CNN network. The detection can be performed with any other computational intelligence techniques to predict the features from a 3D/2D digital file, such that the preferred scanner <NUM> is not limited to the specific techniques, methods and systems described herein.

The present identified hand features <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM> are hand keypoint features collected utilizing the scanner <NUM> and directed by the central processor <NUM>. Confidence and affinity maps are preferably parsed by greedy inference to produce the 2D keypoints for a majority of patients in the image or digital file as 3D keypoint within following preferred codes:
<IMG>
<IMG>.

Detected preferred keypoints of the hand assist the scanner <NUM> to make the probable cutting lines for the 3D cast splint, brace, and related medical devices that may be constructed utilizing the model of scanned digital file. The probability of points preferably draws the cutting lines for the 3D scanned file. The cutting lines and keypoints usage (short cast, the specific splint, cast, brace or other medical device) are prescribed, preferably before the scanning process begins. An interactive module is preferably applied to the scanner machine <NUM> that helps the customer to modify the key points which are not in the correct position. In this case, the scanner machine <NUM> learns how the customer corrects the key points to improve its probability function and update its weight to gain better accuracy in the final digital file and mapping features. <IMG>
<IMG>.

Referring to <FIG>, keypoints proximate the distal end of the ulna <NUM>, <NUM> may be comprised of features of the X-ray shown for the fractures and dislocated bones and also the 3D scan file. This keypoint identification of the ulna <NUM>, <NUM> assists the scanner machine <NUM> to locate the deformation, dislocation and related features for further decision making, parametrization as engravements, pressure configuration, thickness, lock insertion, and related decisions.

Following a traumatic event or other event resulting in orthopedic damage or injury, swelling may occur to the body part, such as the hand <NUM>, making scanning with the 3D machine or scanner <NUM> and creation of a printed case or splint difficult and inaccurate. Where the physician, technician or 3D machine or scanner <NUM> detects significant swelling of the body part <NUM> that may impact the accuracy of the digitized base cast or splint, the opposing or contralateral body part may be scanned using the 3D machine or scanner <NUM> and the digitized base cast may be electrically manipulated to create a mirror image of the digitized base cast for application to the impacted body part once the swelling dissipates. The collected data may be manipulated by the processing unit <NUM> to create the 3D splint or cast of the contralateral body part and the processing unit <NUM> is then able to create the mirror image of the 3D splint or cast by manipulating the collected data. In addition, the processing unit <NUM> may identify swelling or injury based on the collected data and define a bone stimulation port between proximal and distal ends of shell portions of the 3D splint or cast. In addition, the technician or physician operating the 3D machine or scanner <NUM> may place marking tape on the patient's body part at a location for venting holes, a logo, interfacing edges of the 3D splint or cast, locations for flex areas, locations for first or second engagement mechanisms, locations for reinforcement portions, locations for stimulation ports, locations for added padding or coatings over prominences or areas of concern or injury, markings for a targeted pathology or treatment zone and other features of the 3D splint or cast. The 3D splint or cast may further be designed as an initial version with additional space to accommodate the swelling and a final version for application when the swelling subsides, as well as a plurality of intermediate versions for application at different stages of swelling or deformation.

The base cast <NUM> constructed by the 3D machine or scanner <NUM> may include the bone stimulation port formed on one or both shell portions of the splint or cast. The bone stimulation port is formed during the 3D printing process with the 3D machine or scanner <NUM> and is configured to receive a bone stimulator for treatment of the patient's body part. The bone stimulation port may be comprised of two bone stimulation ports with one positioned on a front portion of the splint or cast and one positioned on a back portion of the splint or cast. The bone stimulation ports may be positioned near the wrist of the patient in the mounted configuration and may be otherwise positioned or arranged based on the patient's condition or physician requirements. The bone stimulation port may also be defined on the first shell portion proximate the base of the metacarpal of the thumb in the mounted configuration. The cast or splint may be configured to treat scaphoid fractures, carpal bone fractures and conditions related to the radial styloid. When utilized as a splint, the first shell portion <NUM> may be secured or mounted to the patient's arm with straps by itself to substantially immobilize the thumb. In addition, the bone stimulation port may be otherwise positioned on the first or second shell portions to promote healing or otherwise stimulate bones or other tissue. The bone stimulation port may alternatively be positioned over the fourth and fifth metacarpals on the first and/or second shell portions of the 3D splint or case for application of bone stimulation. The bone stimulation port may alternatively be positioned proximate the second and third metacarpals in the mounted configuration on the first and second shell portions, but are not so limited and the base cast may include only a single bone stimulation port on one of the first and second shell portions or may be constructed without the bone stimulation port, without significantly impacting the design and construction of the preferred cast or splint produced by the 3D machine or scanner <NUM>. The 3D cast or splint <NUM> is not limited to inclusion of the bone stimulation port or to the location of the bone stimulation ports described herein. The preferred cast or splint <NUM> may be constructed without the bone stimulation port and may be configured having the bone stimulation port in nearly any location on the first and second shell portions splint or cast. The bone stimulation port is preferably sized and configured for receipt of a physician preferred bone stimulator. The cast or splint <NUM> and the 3D printing process for constructing the cast or splint <NUM> is particularly adaptable for positioning the bone stimulation port at nearly any location on the cast or splint <NUM>. The bone stimulation port is preferably integrated into the digitized base cast by the designer and printed into one or both of the second shell portions of the splint or cast <NUM>. Accordingly, the bone stimulation port can be moved to various locations and quickly produced with the 3D machine or scanner <NUM>.

The 3D machine or scanner <NUM> may also be designed and configured such that the acquired data is utilized by the processing unit <NUM> to construct a splint or cast <NUM> having a relatively stiff and strong base material and a relatively flexible coating on the external surfaces of the base material. The cast or splint <NUM> preferably includes a first shell portion and a second shell portion that comprises the base cast or splint <NUM>. The first and second shell portions preferably include the coating applied to the external surfaces. The base cast <NUM> is not limited to including the coating and the base cast or splint may be mounted to the patient's body part to immobilize or limit motion to a joint in a mounted configuration. The coating may alternatively only be applied to surfaces of the base cast or splint <NUM> facing the patient's skin for additional protection of the skin to limit irritation or treatment of wounds. The coating may be constructed of a breathable material. The coating is preferably comprised of an inert polymeric material, such as silicone, which has preferred properties for direct contact with the patient's skin, particularly when placed on scars to promote skin healing. The coating is not limited to silicone coatings and may be comprised of any material that may be adhered to the first and second shell portions, withstand the normal operating conditions of the cast or splint <NUM> and is able to take on the size and shape of the preferred coating. In addition, the inert polymeric coating is preferably flexible to accommodate changes to the patient's anatomy, such as swelling or reduction of swelling to maintain the relative form and custom fit around and on the patient's body part for a limited period of time after application of the cast to the body part, such as at least six to eight (<NUM>-<NUM>) weeks. The coating is also not limited to inert polymeric materials or to specifically polymeric materials. The coating may be comprised of nearly any material applied to the base cast <NUM> in nearly any manner that is able to take on the general size and shape of the coating, withstand the normal operating conditions of the coating and perform the described, preferred functions of the coating. For example, the coating may be comprised of a non-polymeric material that is applied to the base cast <NUM> to promote healing of a body part to which the cast or splint <NUM> is applied. In addition, the cast or splint <NUM> may be constructed and deployed as only the base cast without the coating, such as for temporary immobilization while the patient is assessed or temporarily immobilized for subsequent treatment.

The 3D machine or scanner <NUM> of the first preferred embodiment is able to fabricate a splint or cast <NUM> with a custom fit, breathability, and durability with affordable materials. The scanning of the patient's anatomy may be collected by the patient themselves, such as by utilizing their own camera or cameras <NUM>, <NUM>, <NUM>, <NUM> and transmitting the acquired data to the processing unit or central server <NUM>. This remote scanning by the patient or a caregiver promotes social distancing and provides additional convenience for the patient, caregiver and physician. The patient or caregiver may collect videos and/or photographs and transmit the data to the processing unit or central server <NUM>. The processing unit or central server <NUM> processes the collected data to define the digital splint or cast and the digital splint or cast is transmitted to a 3D scanner for manufacture of the 3D splint or cast <NUM>. The manufactured 3D splint or cast <NUM> is then delivered to the patient or the patient visits the physician for final fitting and application to the patient. The splint or cast <NUM> may be updated by relatively quick reprocessing by the processing unit or central server <NUM> and the 3D printer or scanner.

Referring to <FIG>, a dataset of 3D digital files <NUM> is used as an input for training a machine learning module <NUM> of the 3D machine or scanner <NUM>. The machine learning module <NUM> may be positioned within or be a part of the processing unit or central server <NUM>. Geometrical and clinical features, as explained herein, see specifically <FIG>, are extracted with an image processing module <NUM> and are sent to the machine learning module <NUM>. Geometrical features can be a diameter of the patient's wrist, length of the thumb, and clinical data is the place of swelling, fractures, wounds, and other clinician or patient determined marks etc. The preferred 3D machine <NUM> is trained based on unsupervised learning and optimized by a hyperparameter optimization to classify/cluster the extracted features into multiple sizes and/or a fully customized shape. This will not limit the 3D machine <NUM> to unsupervised learning and the 3D machine <NUM> may utilize an alternative AI training algorithm that can cluster or classify the input data for manipulation and manufacture, preferably by 3D printing, of a 3D splint or cast <NUM>.

New input 2D images <NUM>, which are preferably calibrated by the measurement unit (such as physical or radiographic marker, calibration background or other sizer), may also be used as an input to generate the cast, splint or brace <NUM> based on the extracted features. These input 2D images <NUM> can be from a mobile device camera with the assistance of an app (on multiple platforms), cloud based web upload or other acquisition platforms housed on Health Insurance Portability and Accountability Act ("HIPAA") compliant routing and servers. Significant features are preferably identified with a pre-knowledge of expert, machine hyper optimization, etc. The preferred 3D machine <NUM> generates the 3D casts, splint, brace, or other digital models <NUM> based on the trained features as <FIG> or other clinical identifiers. The 3D digital file, which is preferably stored in the central server <NUM> is segmented into multiple parts based on the variation on extracted features as circumferences, curvatures, and straight-line or key points explained in <FIG>. Based on the minimal energy strategy and entropy of the machine learning theory in this preferred system, the number of blocks, features, and other parameterized identifiers are optimized. The number of output blocks can be varied based on the body part; therefore, the algorithm parametrizes the number of sizes for each block of the cast or splint <NUM> and preferably generates the optimized number of sizes based on the input and trained features. The following algorithm is part of the hyper parametrization for the size of casts <NUM> to find the most optimal number for sizing of the cast, brace and splint <NUM> (such as <NUM> preferred sizes):
<IMG>.

With the most common sizes of the parts premanufactured, some pre-fabricated parts may be substituted by the machine learning environment to complement the custom generated parts. The algorithm may route the production of the orthoses based on temporal, inventory and geographic optimization. Scanned data can feed directly into the electronic medical record of the patient for automatic documentation and reproducibility of the orthosis allowing faster turnaround and improving the ease of modification.

Referring to <FIG>, in a preferred process, an algorithm in the processing unit or central server or processor <NUM> can correlate an appropriate size of the orthoses for the scanned patient within the 2D or 3D images (digital photo, capture, radiograph, or 3D scan or advanced medical imaging) captured using the 3D machine or scanner <NUM> or an alternative imager or camera. In a preferred method, a patient takes 2D images (or uploads medical imaging or 3D images, potentially from the 3D machine or scanner <NUM>), and the algorithm calibrates with or without manual input the required measurements for fitting the orthoses, cast, splint or brace to the patient and the patient's body part, such as wrist circumference, forearm length, leg length, ankle size and shape, knee size and shape and related dimensions for the best fit model to create the preferred orthoses. The best fit model for the orthoses is communicated by the processing unit or the central server <NUM> with the 3D machine or scanner <NUM> to recommend the most appropriate fit and type of orthosis (sizes and type of prefabricated splint/brace/cast) for the patient and their particular injury. The cast/splint/brace sizes were given to the algorithm based on the product catalog, expert knowledge, and machine learning of the most appropriate fit and related information for the patient. A neural network classification described herein can predict the output size based on calibrated 2D images with high accuracy.

A preferred process flow includes: <NUM>. providing splint/brace/cast sizes to the 3D machine <NUM> based on product catalog, inventory, expert knowledge, or prior data (AI - machine learning); <NUM>. 2D images are preferably acquired by the patient or point of the service provider, and are sent to the processing unit or central server <NUM> with an application or online web app. , (alternatively, compatible image data in the form of digital radiograph, advanced medical imaging [i.e. CT/MRI], 3D scan, and related information can be input); <NUM>. the algorithm or human measures required distances (based on product catalog, expert knowledge, machine learning database); <NUM>. measured distances are provided to the algorithm and compared/integrated into a virtual model or required sizing input; <NUM>. the algorithm of the processing unit or central server <NUM> recommends the most proper size for the type of orthosis; <NUM>. the most proper size is selected at the distribution point to give, courier, or deliver to the patient or the point of the service location for application to the patient; <NUM>. custom tracking and feedback is provided throughout the process to the prescriber, patient, and distributor; <NUM>. Inventory optimization may be included for proper routing of the device; <NUM>. remote evaluation of re-uploaded images with the orthosis may be checked for appropriate fit based on the above process, fit, deviation from virtual model or via manual evaluation (orthotist/provider checks fit) by providing 2D or 3D scanned images to the processing unit or central server <NUM>.

The preferred embodiments of the present invention utilizes 3D to 2D projection techniques to generate orthoses, such as braces, splints, neck collars, boots, knee immobilizers, and related orthoses. In order to predict and generate the 3D digital file of individual 2D images, the following flow of steps are preferably followed: <NUM>. The trained machine <NUM> picks the most fitted limb by classification and follows a regression to make a decision between proper choices with the smallest error (this can be performed by iterative loops to find the matched keypoints with solving a minimization problem); <NUM>. the preferred algorithm of the processing unit or central server <NUM> calculates minimization error in two loops of scaling factor and database limbs models, such as hands; <NUM>. the algorithm picks the best choices of 3D files in the database and by a decision-making algorithm matches the most proper one between recommended scaling factor and model from the database; <NUM>. project the database model in three planes of (X, Y), (X, Z), and ( Y, Z); <NUM>. use the founded scaling factor to scale the database model into three planes; and <NUM>. reconstruct the 3D model by three planes which are a scaled model of the model at step <NUM>.

The preferred process is also augmented by using scanning, such as with the 3D machine or scanner <NUM>, or 2D image acquisition, such as with a smartphone, tablet or other image capture device. The 2D images may be calibrated by utilizing a reference mark or object positioned near the patient's body part and collecting a series of images of the body part and the reference mark or object to facilitate sizing of the implant by the processing unit or central server <NUM>. The reference mark may be comprised of a scale, ruler, mark on the patient's body part having a predetermined size and shape, a coin having a predetermined size or shape, such as a quarter, multiple reference marks or objects, an immobilizing or reference device that is attached or secured to the patient's body part or other marks, objects or devices that may be positioned on, adjacent or in proximity to the patient's body part that facilitate scaling of the 3D model created by the processing unit or central server <NUM> and construction of the 3D orthoses based on the images and data acquired by the preferred system. The patient may take multiple images of their forearm that requires a brace, splint, cast or implant with reference marks, a reference object or a brace having a predetermined size and shape attached, adjacent to or in proximity to the forearm that the processing unit or central server <NUM> utilizes to size the 3D model and related 3D orthoses that is created from the acquired data. The processing unit or central server <NUM> is preferably able to size and "fit" the 3D orthosis based on the images collected with the reference marking or object therein. As a non-limiting example, the processing unit or central server <NUM> may utilize this preferred method with a patient taking a picture of their hand with a quarter on or adjacent to the hand and the algorithm of the processing unit or central server <NUM> is able to calibrate a size and shape for a wrist brace required for the patient's wrist, such as a stock "XL wrist brace," "M wrist brace," "S wrist brace" or other sized or shaped wrist brace that is in inventory at the care provider.

Referring to <FIG>, <FIG> and <FIG>, the preferred embodiments of the present invention utilizes 2D to 3D projection techniques to generate 3D printed or constructed orthoses, such as braces, splints, neck collars, boots, knee immobilizers, and related orthoses with thermoplastics and molding techniques.

In a preferred process or method, 2D images <NUM> are processed through or received by the central server <NUM> (See <FIG>). As described above, particularly with respect to <FIG> and <FIG>, 2D images are processed in an object outline calibrate and print <NUM> step to generate the 3D model of the limb or any impacted body part with Machine learning and Image processing techniques to estimate parameters for the 3D model and construct the final limb 3D splint or cast <NUM>, based on information from the object database <NUM>. The object outline calibrate and print <NUM> step may utilize object databases <NUM> to develop the 3D model. Through an edge detection algorithm, the outline of the 2D images of the limb or other body part are extracted. The outline is printed on a sheet to define a preliminary splint or cast for further cuts and usages.

An inventory match <NUM> following the print includes the outline of the 2D Image and also the generated 3D model of the limb. The inventory for the inventory match <NUM> may include a plurality of standard or relatively frequently used 3D base models (for example fifty (<NUM>) sizes). The algorithm decision making based on the previous 2D/3D projection and parameter estimation selects one of the standard or relatively frequently used 3D base models or sizes, which has the minimum error compared to the captured 2D Images, based on the described decision making. The selected standard or relatively frequently used 3D base model is picked for further orthoses processing <NUM>. Orthoses processing <NUM> is varied based on the material that will be utilized for the 3D model. As a non-limiting example, a selected standard or relatively frequently used 3D base model constructed of a thermoplastic material may be cut, machined, subtracted or otherwise manipulated by other manufacturing techniques from the printed sheet outline and molded onto the generated 3D model, which is printed previously or taken out of the inventory with the known size. The 3D model may contain a heating filament to simplify the molding process or the process may be otherwise automated. The inventory match <NUM> may include the process of the central server <NUM> selecting one of a variety of differently sized 3D base models from an inventory of 3D base models and subsequently printing additional material onto the 3D base model or removing material from the 3D base model to develop the final product or final 3D cast, splint or brace <NUM>, which is preferably produced as the 3D final product <NUM>.

As part of the orthoses processing <NUM> step, a strengthening rib or reinforcement portion along an axis to limit or prevent predetermined movements of the limb or other body part to promote healing. The orthoses processing <NUM> step may also include manipulating the material of the 3D base model to define a flex area in the 3D base model to facilitate flexing of the limb or other body part, which may also promote healing of the body part. The orthoses processing <NUM> step may further include opening or expanding venting holes in the 3D base model at predetermined areas to provide for visual inspection of the patient's limb, skin or for other clinical purposes to that a medical professional may visually inspect healing, apply medication, gain access for bone stimulation or other therapies. The orthoses processing <NUM> step may also include adding additional material to the 3D base model to reduce the size of the venting holes to protect the patient's skin, generally stiffen the 3D base model or otherwise manipulate the properties of the final 3D case, splint or brace <NUM> based on the patient's specific injury and requirements.

Referring to <FIG>, <FIG>, <FIG> and <FIG>, a second preferred 3D scanner or machine, generally designated <NUM>, and the described actuation mechanism that may be utilized with the scanner <NUM>, is an exemplary description and not meant to be limiting. The second preferred 3D scanner or machine <NUM> may include nearly any actuation mechanism with a different type of control mechanism, processing units, motors, mechanical elements and other features that facilitate performance of the preferred functions, operation in the normal operating conditions of the second preferred scanner <NUM> and functioning within the preferred size and shape of the scanner <NUM>. The interface between components of the second preferred scanner <NUM> that communicate with each other to support operation of the scanner <NUM> can be in a cable, wireless, Bluetooth, or any similar technologies. In addition, the second preferred 3D scanner system <NUM> operates based on the methods, processes and with the features of the first preferred 3D scanner system <NUM>, such as by incorporating and utilizing the first preferred cameras <NUM>, <NUM>, <NUM>, <NUM> and lasers <NUM>, <NUM>, <NUM>, the operation and method described with <FIG> and <FIG>, the operation and methods described with <FIG>, the methods and optimization described with <FIG>, the methods, capture techniques and machine learning described with <FIG> and any of the above features and methods of the first preferred embodiment that may be utilized with the scanner system <NUM> of the second preferred embodiment, as would be apparent to one having ordinary skill in the art based on a review of the present disclosure.

Referring to <FIG> and <FIG>, a second preferred embodiment of a 3D scanner or machine <NUM> is preferably used for capturing a 3D model of an object, such as a patient's limb. The second preferred 3D scanner <NUM> operates such that the scanning process doesn't require a dark environment and can be used in both light and dark environments. The 3D scanner <NUM> of the second preferred embodiment transmits and processes the data from cameras <NUM> and lasers <NUM> similar to or substantially the same as the first preferred scanner system <NUM> and the creation of 3D model and the 3D printing of the cast or splint may be utilized with any of the techniques described in the first preferred embodiment in the second preferred 3D scanner system <NUM>.

In the second preferred 3D scanner <NUM>, a tube <NUM> is mounted to and moves on a rail <NUM> in either left-to-right or front-to-rear direction along a travel axis 1202a of the rail <NUM>. In both directions, the second preferred 3D scanner <NUM> can capture video and/or still/2D images and convert the video, scan and/or still/2D images into a 3D digital file, similar to the techniques described above with respect to the first preferred 3D scanner <NUM>. The 3D scanner <NUM> includes microswitches <NUM>, <NUM> mounted on or near endplates <NUM>, <NUM> that are connected to the rail <NUM>. The microswitches <NUM>, <NUM> include a first microswitch <NUM> mounted to a first endplate <NUM> and a second microswitch <NUM> mounted to a second endplate <NUM>. The first endplate <NUM> is preferably connected to a first rail end 1202d of the rail <NUM> and the second endplate <NUM> is preferably connected to a second rail end 1202e of the rail <NUM>. The first and second microswitches <NUM>, <NUM> are preferably mounted under the first and second endplates <NUM>, <NUM> and detect proximity of the tube <NUM> during use. The preferred tube <NUM> includes end stops that interact with the microswitches <NUM>, <NUM> to control the movement of the tube <NUM> at the ends of the rail <NUM> proximate the first and second endplates <NUM>, <NUM>. The tube <NUM> is preferably driven in its movement by an actuation mechanism that includes a motor <NUM>. In the second preferred embodiment, the motor <NUM> is mounted to the first endplate <NUM>, although such mounting is not so limited and the motor <NUM> may be otherwise mounted, such as to the second endplate <NUM> or to the rail <NUM>. The actuation mechanism also preferably includes encoders configured to move the tube <NUM> along the rail <NUM>, mechanical elements including a belt, ball bearings, a roller bearing, position sensors and coupling components or elements to attach the belt to the tube <NUM>.

The computer <NUM>, the processor <NUM> or the user can control the position of tube <NUM> on the rail <NUM>, as well as the movement of the tube <NUM> along the rail <NUM> for capturing the video and images of the object during operation. Scanner holders or legs <NUM>, <NUM> are used to stabilize the 3D scanner <NUM> and may be adjusted in height with a screw or a hydraulic mechanical system to raise or lower the tube <NUM> to adapt to the positioning or comfort of the patient, the object or the body part being scanned.

The laser <NUM> of the second preferred embodiment provides a stripe of light to illuminate the object that is positioned in the tube <NUM> for scanning. The laser <NUM> is preferably comprised of five (<NUM>) lasers <NUM> that are mounted inside the tube <NUM>. The lasers <NUM> are mounted inside the tube <NUM> on a laser holder <NUM> that is comprised of a shelf or rib that extends generally around the inside of the tube <NUM> in a frusta-circular configuration. The laser holder <NUM> is configured to provide strength and stiffness to the tube <NUM> and to facilitate mounting of the lasers <NUM> to the tube <NUM>. The laser <NUM> is not limited to being comprised of five (<NUM>) lasers <NUM> mounted to the inside of the tube <NUM> and may be comprised of nearly any number of lasers <NUM> that are able to perform the preferred functions and withstand the normal operating conditions of the preferred laser <NUM> of the second preferred embodiment.

The camera <NUM> is configured to capture 2D images of the object that is positioned in the tube <NUM> during operation. The camera <NUM> is preferably comprised of five (<NUM>) cameras <NUM> mounted inside the tube <NUM>. The cameras <NUM> are preferably mounted to the tube <NUM> on a camera mount <NUM> that is comprised of a frusta-circular structural element that provides strength and stiffness to the tube <NUM> and stable mounting locations for the cameras <NUM>. The camera <NUM> is not limited to being comprised of five (<NUM>) cameras <NUM> mounted to the inside of the tube <NUM> and may be comprised of nearly any number of cameras <NUM> that are able to perform the preferred functions and withstand the normal operating conditions of the preferred camera <NUM>. The preferred five (<NUM>) cameras <NUM> are generally evenly spaced from each other inside the tube <NUM> and mounted on the camera mount <NUM>. The preferred five (<NUM>) cameras <NUM> include a first camera 1301a, a second camera 1301b, a third camera 1301c, a fourth camera 1301d and a fifth camera 1301e. The first and second cameras 1301a, 1301b preferably define a camera spacing angle Δ measured relative to a tube central axis 1205c. The camera spacing angle Δ is approximately seventy-two degrees (<NUM>°) and each of the adjacent cameras <NUM> are also spaced from each other at the spacing angle Δ but are not so limited. The cameras 1301a, 1301b, 1301c, 1301d, 1301e may be spaced and arranged in nearly any manner that facilitates collecting the images of the object inside the tube <NUM> during operation.

The tube <NUM> is configured to move generally along the travel axis 1202a of the rail <NUM> from left-to-right and/or front-to-rear between the ends of the rail <NUM>. The tube <NUM> includes a first tube end 1205a and a second tube end 1205b that are open such that the object may be positioned in the tube <NUM> during the scanning process. The camera <NUM> and the laser <NUM> are mounted inside the tube <NUM> between the first tube end 1205a and the second tube end 1205b. The rail <NUM> of the second preferred embodiment includes a first track 1202b and a second track 1202c that are substantially grooves in the rail <NUM> that extend along the length of the rail <NUM> substantially parallel to the travel axis 1202a, although are not so limited and may extend along only portions of the rail <NUM> or the rail <NUM> may be otherwise designed and configured to facilitate movement of the tube <NUM> along the rail <NUM>. A first wheel 1100a is preferably mounted to a first longitudinal stiffening rib 1260a and is positioned in the first track 1202b and a second wheel 1100b is preferably mounted to a second longitudinal stiffening rib 1260b and is positioned in the second track 1202c in the assembled configuration. The first and second tracks 1202b, 1202c guide the movement of the tube <NUM> as the wheels 1100a, 1100b roll along the tracks 1202b, 1202c and movement of the tube <NUM> along the rail <NUM> along the travel axis 1202a. The second preferred 3D scanner is not limited to including the first and second wheels 1100a, 1100b or the first and second tracks 1202b, 1202c and may be otherwise designed and configured to facilitate movement of the tube <NUM> along the rail <NUM>, such as a pin and track, opposing sliding surfaces or other arrangements that direct and guide the tube <NUM> along the rail <NUM>.

The second preferred tube <NUM> includes a first continuous ring 1205d at the first tube end 1205a and a second continuous ring 1205e at the second tube end 1205b. The first and second continuous rings 1205d, 1205e provide structural support for the tube <NUM> and are preferably constructed of a relatively stiff, structural material. The tube <NUM> also includes a channel <NUM> extending through the tube <NUM> between the first ring 1205d and the second ring 1205e positioned adjacent the rail <NUM> in the assembled configuration. The channel <NUM> accommodates the rail <NUM> and connection of the first and second longitudinal stiffening ribs 1260a, 1260b to the wheels 1100a, 1100b and a belt that drives the tube <NUM>, as is described in greater detail below. The first stiffening rib 1260a extends along a first side of the channel <NUM> between the first tube end 1205a and the second tube end 1205b and the second longitudinal stiffening rib 1260b extends along a second side of the channel <NUM> between the first and second tube ends 1205a, 1205b.

In the second preferred embodiment, the rail <NUM> and the tube <NUM> are supported off of a floor surface by a first leg <NUM> and a second leg <NUM> that are connected to the rail <NUM>. The first and second legs <NUM>, <NUM> are preferably constructed of a relatively stiff, structural material that is able to take on the general size and shape of the first and second legs <NUM>, <NUM>, withstand the normal operating conditions of the first and second legs <NUM>, <NUM> and perform the preferred functions of the first and second legs <NUM>, <NUM>, as are described herein. The first and second legs <NUM>, <NUM> may be constructed of a polymeric or metallic material, such as polyvinyl chloride, aluminum or steel. The first and second legs <NUM>, <NUM> may also be configured to raise and lower the rail <NUM> and tube <NUM> relative to the support surface or floor, manually or automatically, to arrange the tube <NUM> for easy insertion of the object for scanning.

The 3D scanner <NUM> is utilized with dual scanning such that the lasers <NUM> and/or cameras <NUM> in the tube <NUM> operate in a double or two scan process, including left-to-right and front-to-rear scanning as the tube <NUM> travels along the rail <NUM>. This process is used to scan the object, then used to scan the texture and placed features or landmarks on the object with the installed cameras (monochrome, DSLR, Infrared ,and etc.) <NUM> and/or lasers <NUM> during the second phase of the scanning process. In the preferred embodiment, the lasers <NUM> are utilized to scan the object to create the 3D model of the object in a first pass along the rail <NUM> and the cameras <NUM> are subsequently utilized to identify or scan the texture and placed features or landmarks on the object during a second pass along the rail <NUM>. The preferred lasers <NUM> may be comprised of infrared, near-infrared, red, green, and other specific wavelengths/bandwidth types of lasers. The preferred lasers <NUM> with equipped specific lens are mounted on the laser holders <NUM> that secure the lasers <NUM> to the tube <NUM> and result in the lasers <NUM> moving with tube <NUM> during operation.

The preferred 3D scanner <NUM> also includes the cameras <NUM> positioned on a camera mount <NUM> inside the tube <NUM>. The cameras <NUM> are preferably fixed to tube <NUM> and move with the tube <NUM> and lasers <NUM> on the rail <NUM> during operation. In the preferred second embodiment, a securing block <NUM> is secured or connected to the tube <NUM> proximate the rail <NUM>, preferably below the rail <NUM>, to secure the tube <NUM> to the rail <NUM> on a transport mechanism 1100a, 1100b to movably connect the tube <NUM> to the rail <NUM>. In the second preferred embodiment, the transport mechanism is comprised of wheels 1100a, 1100b that facilitate movement of the tube <NUM> along the rail <NUM> during operation. The wheels 1100a, 1100b may be directly driven to move the tube <NUM> or may be passive and facilitate the translation movement of the tube <NUM> that is pulled along the travel axis 1202a by a belt or a chain (not shown) connected to the securing block <NUM> or the tube <NUM>. The movement of the tube <NUM> is preferably controlled and driven by the computer <NUM> and/or the central processor <NUM>. In order to fix or secure the object, preferably the patient's limb, relative to the tube <NUM> and rail <NUM>, a holder(s) (not shown) can be installed on the rail <NUM>, the endplates <NUM>, <NUM>, the scanner holders or legs <NUM>, <NUM> or to an external support adjacent to the 3D scanner <NUM>. The holder preferably secures or fixes the body limb or scanning object in the scanning area above the rail <NUM> for image capture and creation of the 3D model of the object, preferably the limb. The size of the scanning area can be modified by changing the size of the tube <NUM>, the length of the rail <NUM> or making other adjustments to the 3D scanner <NUM>.

The angle and number of the cameras <NUM> can be changed to a higher or lower number to maintain a scanning area without any blind spots based on the object's complexity and shape. The process to develop and construct a 3D digital file of the scanned object is preferably the same as the description above with respect to the first and second preferred embodiment although not limiting and various processes may be utilized to develop the 3D model of the scanned object utilizing the 3D scanner <NUM> of the second preferred embodiment. The data collected from the scanning process is preferably transferred with a universal to serial bus ("USB") to the computer <NUM> and/or central processor <NUM>. The transfer can be done with wireless protocols or over a local area network ("LAN") connection. The user can calibrate the preferred 3D scanner <NUM> with the same process and a checkboard that provides calibration for the lasers <NUM>, the cameras <NUM> and processing capabilities of the 3D scanner <NUM>. The user can also check the lasers <NUM>, the motor <NUM> that is preferably installed to or under the first endplate <NUM>, and the cameras <NUM> part by part to diagnose the system of the 3D scanner <NUM>. Different motors or driving mechanisms can be used based on the precision and required speed to drive the movement of the tube <NUM> along the rail <NUM>.

Referring to <FIG>, <FIG>, <FIG> and <FIG>, in the software of the second preferred 3D scanner <NUM>, the raw data of the cameras <NUM>, which preferably includes videos and/or pictures, is preferably processed in an image processing, or raw data step <NUM>. The 2D images from the cameras <NUM> are converted to 3D coordinates and the point cloud of the object. Following the initial scan, the central server or processor <NUM> and/or computer <NUM> analyzes the collected data to determine potential missing or underdeveloped areas of the object. The constructed file is preferably converted to a 3D mesh from point cloud in the point cloud step <NUM>, the mesh is further processed in a mesh post-processing step <NUM>-<NUM> and the 3D mesh is finalized in a 3D digital file output step <NUM> so that the mesh can be exported in the desired format to the user. This 3D object file is used to input the mass customization software algorithms to design the fully automated or semi-automated brace, cast, and/or splint with the user specific parameters.

In the second preferred embodiment, the tube <NUM> is preferably constructed of a lightweight structural material that is able to take on the size and shape of the tube <NUM>, withstand the normal operating conditions of the tube <NUM> and perform the preferred functions of the tube <NUM>, as is described herein, such as a polymeric, metallic or other related material. The laser holders <NUM> and camera mount <NUM> are preferably constructed of a similar material to the tube <NUM> and are mounted inside the tube <NUM> but may alternatively be integrally formed or molded with the tube <NUM>. The laser holders <NUM> and camera mount <NUM> are preferably constructed of frusta-circular structural elements that mount to the inside of the tube <NUM> and support the cameras <NUM> and lasers <NUM> within the tube <NUM>, respectively. The laser holders <NUM> and camera mount <NUM> include ends adjacent to the rail <NUM> in the mounted configuration to facilitate connection to the rail <NUM> and movement of the tube <NUM> relative to the rail <NUM>.

Claim 1:
A scanner system (<NUM>) for capturing a 3D model of an object, the scanner system (<NUM>) comprising:
a laser (<NUM>) providing a stripe of light to illuminate the object;
a camera (<NUM>) to capture two-dimensional images of the object; characterised by
a tube (<NUM>) mounted to a rail (<NUM>), the tube (<NUM>) configured to move generally along a travel axis (1202a) of the rail (<NUM>), the tube (<NUM>) including a first tube end (1205a) and a second tube end (1205b), the first and second tube ends (1205a, 1205b) being open, the laser (<NUM>) and the camera (<NUM>) mounted inside the tube (<NUM>) between the first tube end (1205a) and the second tube end (1205b), the first tube end (1205a) including a first continuous ring (1205d) and the second tube end (1205b) including a second continuous ring (1205e), a channel (<NUM>) extending through the tube (<NUM>) between the first ring (1205d) and the second ring (1205e) positioned adjacent the rail (<NUM>) in an assembled configuration; and
a central processor (<NUM>) configured to receive data collected from the laser (<NUM>) and the camera (<NUM>); and
an actuation mechanism configured to move the tube (<NUM>) along the rail (<NUM>) generally along the travel axis (1202a) .