Patent Description:
Multiple intra-ocular lens (IOL) calculation formulas are currently available. For example, the SRK I formula <NUM> may be considered a first-generation formula. More recent third-generation formulas such as the SRK/T formula add theoretical portions to the formula in order to improve its accuracy, which increases the complexity of the formula when compared to the SRK I formula. Additional IOL calculation formulas include the Hoffer Q, Holladay I, Haigis, and SRK/T formulas. The Koch adjustment may also be used to adjust any of these formulas. Although the existing formulas give similar results over a range of input parameters, they also diverge significantly at specified ranges of input parameters.

Individual formulas have been demonstrated to work best with certain input parameters. The input parameters may include ocular measurement parameters such as axial length, corneal power, a white-to-white distance, gender or sex, anterior chamber depth, pre-operative refraction, and/or lens thickness. In an aspect, at least two ocular measurement parameters may be used. In an aspect, the at least two parameters may include axial length and corneal power. For example, a particular formula may work better with "shorter" eyes and another particular formula may work better in "longer" eyes. Further, "adjustments" to these formulas may be used to obtain better results. An "adjustment" may include any additional factor applied to an IOL calculation formula.

The current state of the art includes selecting one formula to determine the lens power and possibly comparing the results to those obtained using another formula. A limited number of ophthalmologists understand the data and literature that support using one formula over another. While the use of a particular formula may be debatable, there are certain scenarios (e.g. a specific measured axial length or corneal power) in which one formula is generally accepted as better than others.

<CIT> discloses an ophthalmic method for determining relationships for calculating intraocular lens (IOL) power correction values. The method involves obtaining estimates of the postoperative optical power of a plurality of eyes undergoing IOL implant surgery. Measurements of the postoperative optical power and of one or more characteristics (e.g., axial length) of the eyes can also be obtained. The eyes can be separated into groups based upon their axial lengths. For each of the groups, a mathematical relationship can be determined for calculating IOL power correction values based on the measured characteristics. The mathematical relationship can reduce prediction error for the respective eyes in each group when applied to the corresponding estimates of the postoperative optical power.

Aspects of the present disclosure may include systems, apparatuses, and methods for intraocular lens selection using a three dimensional super surface. The super surface may represent portions of a plurality of lens selection formulas based on a range of measurements most suitable to each individual intraocular lens selection formula.

The disclosure provides a novel intraocular lens calculation formula based on a combination of existing intraocular lens (IOL) calculation formulas using graphical analysis to determine the range of input parameters, such as portions thereof over which existing IOL calculation formulas are most accurate. These individual formulas have never been presented or thought of as <NUM> dimensional objects that may be combined and overlapped according to a set of determined "criteria". The intraocular lens calculation formula will graphically "pick and choose" the parts of each formula that have either been proven or are believed to be the most accurate and combine this into one overall formula. This combined or "super" formula may be adjusted and/or optimized further (e.g., iteratively) as other formulas are proposed or additional data becomes available.

<FIG> show Ladas-Siddiqui graphs, which highlight areas of clinical agreement and disparity between formulas at specified ranges of corneal power and axial length. Graph <NUM> (<FIG>) demonstrates that all formulas differ from at least one of the other four by greater than <NUM> diopter over the entire range of input parameters. As seen in graph <NUM> (<FIG>), when the tolerance for divergence between formulas is increased to <NUM> diopter in predicted IOL power (a clinically undesirable level), there are areas of correspondence between all formulas tested. The areas of correspondence increase further when tolerance is raised to <NUM> diopters as shown in graph <NUM> (<FIG>). Thus, resolving these areas of discrepancy is of high clinical relevance and requires not only access to each formula but also a detailed knowledge of their particular strengths and weaknesses. Depicting comparisons between IOL formulas in this manner shows the specific range of input parameters over which the formulas diverge, enabling more precise understanding of the differences between formulas.

<FIG> graphically illustrate a combined super formula based on the strengths and weaknesses of each IOL formula. The super formula combines the ideal, most accurate output portions of each IOL formula into a combined super formula. <FIG> illustrate the graphical results of this super formula, showing a step-wise development and evolution of a singular multi-faceted surface including the most accurate portions of multiple individual formulas based on specified ranges of input variables. <FIG> is a graph showing regions where different IOL selection formulas are determined to be optimal. <FIG> is a graph showing interconnection among the regions for the different IOL selection formulas. <FIG> shows a surface for a combined super formula. In an aspect, although a surface is illustrated to represent the output of formulas using two ocular measurements, an IOL selection formula or system may use more than two ocular measurements. An IOL selection system or formula having multiple inputs may be conceived of as a multi-dimensional hyper-surface.

Another technique for determining an intraocular lens involves use of machine learning. For example, a neural network may be trained using the results of previous operations. The neural network may then be provided with the pre-operative measurements of a patient. The neural network will attempt to classify the patient's pre-operative measurements against the training set to determine the lens power. This approach may be limited based on the training data, and some sets of pre-operative measurements may be considered out-of-bounds and the neural network may be unable to produce a result. Moreover, the neural network may potentially produce anomalous results due to random correlations in the training data. Further, changes to the training data may require retraining the neural network.

This disclosure describes methods, apparatuses, and systems for combining and using any number of multiple formulas into a single formula using the ideal parts of each constituent formula based, for example, on theoretical and/or empirical information. Further, this disclosure describes a technique for improving a formula based approach using a combination of a formula and machine learning. For example, in an aspect, a neural network may be trained to determine an estimated error that may result from using a calculated intraocular lens power. The estimated error may be used to adjust a lens selection parameter such as target refraction or A-constant, and the formula may be used to recalculate the intraocular lens power using the adjusted lens selection parameter.

Aspects of the present disclosure may be implemented using hardware, software, or a combination thereof and may be implemented in one or more computer systems or other processing systems. In an aspect of the present disclosure, features are directed toward one or more computer systems capable of carrying out the functionality described herein. An example of such a computer system <NUM> is shown in <FIG>.

Computer system <NUM> includes one or more processors, such as processor <NUM>. The processor <NUM> is connected to a communication infrastructure <NUM> (e.g., a communications bus, cross-over bar, or network). Various software aspects are described in terms of this example computer system. After reading this description, it will become apparent to a person skilled in the relevant art(s) how to implement aspects of the disclosure using other computer systems and/or architectures.

Computer system <NUM> can include a display interface <NUM> that forwards graphics, text, and other data from the communication infrastructure <NUM> (or from a graphics processing unit (GPU) <NUM>) for display on a display unit <NUM>. For example, the display interface <NUM> may forward a graphical rendering of a super surface from the processor <NUM> to the display unit <NUM>. Computer system <NUM> also includes a main memory <NUM>, preferably random access memory (RAM), and may also include a secondary memory <NUM>. The secondary memory <NUM> may include, for example, a hard disk drive <NUM> and/or a removable storage drive <NUM>, representing a floppy disk drive, a magnetic tape drive, an optical disk drive, a universal serial bus (USB) flash drive, etc. The removable storage drive <NUM> reads from and/or writes to a removable storage unit <NUM> in a well-known manner. Removable storage unit <NUM> represents a floppy disk, magnetic tape, optical disk, USB flash drive, etc., which is read by and written to removable storage drive <NUM>. As will be appreciated, the removable storage unit <NUM> includes a computer usable storage medium having stored therein computer software and/or data.

Alternative aspects of the present disclosure may include secondary memory <NUM> and may include other similar devices for allowing computer programs or other instructions to be loaded into computer system <NUM>. Such devices may include, for example, a removable storage unit <NUM> and an interface <NUM>. Examples of such may include a program cartridge and cartridge interface (such as that found in video game devices), a removable memory chip (such as an erasable programmable read only memory (EPROM), or programmable read only memory (PROM)) and associated socket, and other removable storage units <NUM> and interfaces <NUM>, which allow software and data to be transferred from the removable storage unit <NUM> to computer system <NUM>.

Computer system <NUM> may also include a communications interface <NUM>. Communications interface <NUM> allows software and data to be transferred between computer system <NUM> and external devices. Examples of communications interface <NUM> may include a modem, a network interface (such as an Ethernet card), a communications port, a Personal Computer Memory Card International Association (PCMCIA) slot and card, etc. Software and data transferred via communications interface <NUM> are in the form of signals <NUM>, which may be electronic, electromagnetic, optical or other signals capable of being received by communications interface <NUM>. These signals <NUM> are provided to communications interface <NUM> via a communications path (e.g., channel) <NUM>. This path <NUM> carries signals <NUM> and may be implemented using wire or cable, fiber optics, a telephone line, a cellular link, a radio frequency (RF) link and/or other communications channels. In this document, the terms "computer program medium" and "computer usable medium" are used to refer generally to media such as a removable storage drive <NUM>, a hard disk installed in hard disk drive <NUM>, and signals <NUM>. These computer program products provide software to the computer system <NUM>. Aspects of the present disclosure are directed to such computer program products.

In an aspect, the computer system <NUM> may include an ocular measurement device <NUM>. The ocular measurement device <NUM> may determine one or more ocular measurement parameters. An ocular measurement device may include any device for measuring an eye. For example, the ocular measurement device <NUM> may measure an axial length and a corneal power of an eye. In an aspect, the ocular measurement device <NUM> may further measure a white-to-white distance, anterior chamber depth, pre-operative refraction, and/or lens thickness. The ocular measurement device <NUM> may further receive input of ocular measurement parameters (e.g., gender or sex). The axial length may be a distance from the surface of the cornea to the retina. The corneal power may be a dioptric power of the cornea. As another example, the ocular measurement device <NUM> may measure an anterior chamber depth of an eye. In an aspect, the ocular measurement device <NUM> may be an ultrasound device. In another aspect, the ocular measurement device <NUM> may be an optical biometer. Various optical biometers are available under the names LENSTAR® and IOL MASTER. In another aspect, the ocular measurement device <NUM> may include an intraoperative abberrometry device. The intraoperative abberrometry device may take measurements of refractive properties of the eye during surgery. For example, an intraoperative abberrometry device may provide information on sphere, cylinder, and axis of the eye. Additionally, an ocular measurement device may include a post-operative measurement device such as a wavefront analyzer. The ocular measurement device <NUM> may be communicatively coupled to the processor <NUM> via the communication infrastructure <NUM>, the communications interface <NUM>, and/or the communications path <NUM>.

Computer programs (also referred to as computer control logic) are stored in main memory <NUM> and/or secondary memory <NUM>. Computer programs may also be received via communications interface <NUM>. Such computer programs, when executed, enable the computer system <NUM> to perform the features in accordance with aspects of the present disclosure, as discussed herein. In particular, the computer programs, when executed, enable the processor <NUM> to perform the features in accordance with aspects of the present disclosure. Accordingly, such computer programs represent controllers of the computer system <NUM>.

In an aspect of the present disclosure where the disclosure is implemented using software, the software may be stored in a computer program product and loaded into computer system <NUM> using removable storage drive <NUM>, hard drive <NUM>, or communications interface <NUM>. The control logic (software), when executed by the processor <NUM>, causes the processor <NUM> to perform the functions described herein. In another aspect of the present disclosure, the system is implemented primarily in hardware using, for example, hardware components, such as application specific integrated circuits (ASICs). Implementation of the hardware state machine so as to perform the functions described herein will be apparent to persons skilled in the relevant art(s).

In yet another aspect of the present disclosure, the disclosure may be implemented using a combination of both hardware and software.

<FIG> shows a communication system <NUM> usable in accordance with aspects of the present disclosure. The communication system <NUM> includes one or more accessors <NUM> (also referred to interchangeably herein as one or more "users") and one or more terminals <NUM> and/or other input device or devices (e.g., an ocular measurement device <NUM>). In an aspect, the ocular measurement device <NUM> may be similar to the ocular measurement device <NUM> (<FIG>). The ocular measurement device <NUM> may further be configured to communicate with the network <NUM>. In one aspect of the present disclosure, data for use is, for example, input and/or accessed after being received from an input device by accessors <NUM> via terminals <NUM>, such as personal computers (PCs), minicomputers, mainframe computers, microcomputers, telephonic devices, or wireless devices, personal digital assistants ("PDAs") or a hand-held wireless devices (e.g., wireless telephones) coupled to a server <NUM>, such as a PC, minicomputer, mainframe computer, microcomputer, or other device having a processor and a repository for data and/or connection to a repository for data, via, for example, a network <NUM>, such as the Internet or an intranet, and/or a wireless network, and couplings <NUM>, <NUM>, <NUM>. The couplings <NUM>, <NUM>, <NUM> include, for example, wired, wireless, or fiberoptic links. In another aspect of the present disclosure, the method and system of the present disclosure may include one or more features that operate in a stand-alone environment, such as on a single terminal.

In an aspect, the server <NUM> may be an example of the computer system <NUM> (<FIG>). In an aspect, for example, the server <NUM> may be configured to perform the methods described herein. For example, the server <NUM> may obtain measurements such as an axial length and corneal power measurements from a terminal <NUM> and/or other input device. The measurements may be entered by an accessor <NUM>, or provided by an ocular measurement device <NUM> (<FIG>). The server <NUM> may determine a relevant portion of a super surface including ideal portions of a plurality of intraocular lens selection formulas based on a range of the axial length and corneal power measurements most suitable to each individual intraocular lens selection formula. Further, the server <NUM> may determine an intraocular lens power based on a formula for the relevant portion of the super surface.

<FIG> illustrates an example input user interface (UI) <NUM> for use in accordance with aspects of the present invention. The user interface <NUM> may be implemented by the server <NUM> and displayed on the terminal <NUM>, for example. The user interface <NUM> may allow a user to enter pre-operative measurements of one or more eyes as well as target parameters. The user interface <NUM> allows simultaneous calculation and plotting of both eyes. Input-data may include the keratometry (K1, K2, and K Index), axial length, corneal index and anterior chamber depth (ACD). The K1 and K2 values may be averages of refractive power of the front surface of the eye including different angles. The K-index may be a standard refractive index for the cornea. Slight variations of the K-index may be used by different surgeons or in different countries. The anterior chamber depth is a measurement of the position of the original lens prior to removal. The lens A-constant and desired postop refraction are then selected and the optimized calculations are then performed. The lens A-constant is a property of a lens that may be specified by a manufacturer and may contribute to total refraction. The target refraction may be a goal specified by the surgeon. For example, a target refraction of <NUM> may be used.

The user interface <NUM> may include an input field <NUM> for a right eye and an input field <NUM> for a left eye. Each input field <NUM>,<NUM> may include input fields for specific measurements or parameters. For example, the input field <NUM> may include an axial length field <NUM>, a K1 field <NUM>, a K2 field <NUM>, a K Index field <NUM>, and an Optical ACD field <NUM>. The input field <NUM> may also include lens selection parameters including an A-constant field <NUM> and a target refraction <NUM>. The input fields <NUM>, <NUM> may also include a help icon (e.g., "?") that provides a description of the measurement or parameter including allowed ranges. Some fields such as the K Index field <NUM> may use a drop-down menu to select a value. Additional fields that may be included in the user interface <NUM> include intraoperative aberrometry measurements such as sphere, cylinder, and axis of the eye.

Additionally, the user interface <NUM> may include a surgeon field <NUM>, a patient field <NUM>, and a patient ID field <NUM>. The server <NUM> may generate records for the surgeon and patent based on the fields <NUM>, <NUM>, <NUM>. The user interface <NUM> may also include an import option <NUM> that may allow a user to upload a file (e.g., a spreadsheet) including measurements and parameters for one or more patients. A dedicated toric calculator and a post-LASIK calculator may also be included.

<FIG> illustrates an example output user interface <NUM> for use in accordance with aspects of the present invention. The user interface <NUM> may display a recommended intraocular lens power (IOL) <NUM>. The user interface <NUM> may display multiple IOL power values corresponding to available lenses. The user interface <NUM> may also display an estimated final refraction (REF) <NUM> associated with each IOL power value. Additionally, the user interface <NUM> may include a graphical representation <NUM> of a supersurface used to select the intraocular lens power. The relevant point on the supersurface based on the measurements may be indicated. The inclusion of the three-dimensional graph is useful for a surgeon to know instantly if his patient has typical or unusual eyes. The user interface <NUM> may include output fields for each eye.

<FIG> illustrates a graph <NUM> representing a data set of example eyes. The data set may be collected from one or more surgeons. As illustrated, while most eyes have an average keratometry and axial length, there are also outliers.

<FIG> illustrates a three-dimensional surface representative of a learned variation of a formula. The original Ladas super surface has evolved to enhance accuracy for eyes of all dimensions including types of eyes previously though to present difficulties such as small eyes with short axial lengths. The circles represent data points of actual postop results that allow creation of an optimized Super Surface <NUM> compared with the original super surface <NUM>, which is mostly overlapped by the new surface).

In an aspect, the super surface or another formula may be customized to one or more surgeons. For example, verified results from a surgeon may be analyzed against the super surface or the formula to determine whether any patterns in the surgeon's operations can be detected.

Turning now to <FIG>, an example system <NUM> may recommend an intraocular lens power based on a formula and a trained deep learning machine <NUM>. The system <NUM> may be implemented on a server <NUM>, for example. The system <NUM> may communicate with one or more terminals <NUM> via the user interfaces <NUM>, <NUM> discussed above. The system <NUM> may include a formula component <NUM> for determining an intraocular lens power based on a formula using at least two ocular measurement parameters, a deep learning machine <NUM> such as neural network <NUM> for determining an estimated error of the formula, and the user interface <NUM> for obtaining at least two ocular measurement parameters and a target refraction or A-constant for an eye. The formula component <NUM> may further adjust the target refraction of A-constant based on the estimated error and redetermine the intraocular lens power based on the formula and the adjusted target refraction or A-constant. The system <NUM> may further include one or more training sets <NUM>. The training sets <NUM> may include sets of post-operative data including two or more of the pre-operative measurements and parameters (e.g., axial length field <NUM>, a K1 field <NUM>, a K2 field <NUM>, a K Index field <NUM>, an Optical ACD field <NUM> and intraoperative aberrometry measurements such as sphere, cylinder, and axis of the eye), a selected intraocular lens power, and one or more lens selection parameters (e.g., post-operative refraction or A-constant). The training sets <NUM> may be used to train one or more of the deep learning machine <NUM> for estimating an error of an intraocular lens power determined by the formula <NUM>, as explained in further detail below.

The system <NUM> may include an administrative portal <NUM> for controlling access to the system <NUM>. For example, the administrative portal <NUM> may permit an administrative user to generate training sets <NUM> from verified results <NUM>. The verified results <NUM> may be uploaded in the form of a database or spreadsheet. The administrative user may select combinations of measurements and parameters to use for the training sets <NUM>. The administrative user may combine the uploaded verified results with any existing training sets <NUM>. The system <NUM> may generate a new neural network <NUM> based on a new or updated training set <NUM>. The system <NUM> may provide the administrative user with statistics regarding the neural network. For example, a neural network may be associated with input boundaries and correlation values. The administrative user may also configure access controls <NUM> to manage user accounts for different end users. The user accounts may be associated with saved patient data <NUM>. Additionally, the user accounts may be associated with a customized neural network <NUM>. For example, a customized neural network <NUM> may trained with verified results <NUM> exclusively from a particular surgeon, practice group, or lens manufacturer. A customized neural network <NUM> may help control for unknown or immeasurable factors affecting the particular surgeon, practice group, or lens manufacturer.

The formula component <NUM> may implement an intraocular lens power determination formula. An intraocular lens power determination formula may include any deterministic technique for generating an intraocular lens power based on two or more ocular measurements. For example, the formula component <NUM> may include software executed by a processor to determine an intraocular lens power according to a formula using input values from the user interface <NUM>. For example, the formula component <NUM> may implement one or more of: a Hoffer Q formula, a Holladay I formula, a Haigis formula, a SRK/T formula, a Barrett Universal II or adjustments to any of these formulas. In an aspect, the formula component <NUM> may implement the Ladas Super Formula to select a calculation from one or more of the above formulas. For example, the formula component <NUM> may determine a relevant portion of a super surface including ideal or near ideal portions of a plurality of intraocular lens selection formulas based on a range of the axial length and corneal power measurements most suitable to each individual intraocular lens selection formula. The formula component <NUM> may provide the determined intraocular lens power value to the neural network <NUM> along with all of the input measurements and parameters. In an aspect, the formula component <NUM> may be implemented as a machine learned formula such as the Hill-RBF.

The learning machine <NUM> may use deep learning techniques to predict error of the formula component <NUM> based on the training set <NUM> including post-operative results. The learning machine <NUM> may be implemented by, for example, a neural network <NUM>, which may utilize a Python based tensor flow. In an aspect, the learning machine <NUM> may include a computer processor (e.g., processor <NUM> that is programmed to execute instructions for developing the neural network <NUM> based on a network structure (e.g., number and type of layers). Once the learning machine <NUM> has trained the neural network <NUM> (or other learning machine), the processor configured with the trained learning machine <NUM> may determine the predicted error of the formula component <NUM> based the ocular measurement parameters. The neural network <NUM> may receive multiple numeric inputs to predict a single numeric output. In an implementation, the neural network may receive three numeric inputs (axial length, K, and ACD) and output an error value. The neural network <NUM> may be trained by one or more of the training set <NUM>. The training sets <NUM> may be considered labelled data because the training sets <NUM> may include the post-operative refraction, which may be used to determine the accuracy or error of the formula. Accordingly, when the neural network <NUM> receives the set of numeric inputs, the neural network <NUM> may predict an estimated error of the formula component <NUM>. The learning machine <NUM> may be implemented using different types of learning machines. For example, the learning machine <NUM> may use any combination of supervised and unsupervised learning techniques. The learning machine <NUM> may be structured as, for example, an artificial neural network, convolutional neural network, Bayesian network, or other deep learning model.

The formula component <NUM> may then adjust the formula inputs according to the predicted error. In particular, the formula component <NUM> may adjust a target refraction or an A-constant based on the predicted error. For example, the new target refraction may be set to the difference between the user input target refraction and the neural net predicted error. As a numeric example, for an eye with AL of <NUM>, K of <NUM>, and ACD of <NUM>, the neural network <NUM> may predict an error of <NUM>. That is, when the formula component <NUM> calculates an eye with AL of <NUM>, K of <NUM>, ACD of <NUM>, and A constant of <NUM> (doesn't matter what user chooses for this) and target refraction of -<NUM>, then the eye will get re-calculated using the current formula component <NUM> but using the new target refraction value of -<NUM> - <NUM> = -<NUM> instead of the user input target refraction. In other words, the formula component <NUM> may adjust one component of the formula's input (e.g., target refraction) by subtracting the neural network predicted error from the input value.

In an aspect, the formula component <NUM> may limit the adjustment to the formula by the neural network predicted error. For example, the neural network <NUM> may produce an extreme value in the case of an out-of-bounds case where the neural network <NUM> does not have good training data. The formula component <NUM> may limit the value of the predicted error. For example, the formula component <NUM> may limit the neural network predicted error never to exceed +/- <NUM>.

In an aspect, the neural network <NUM> may be adjusted based on a new ocular measurement. For example, in an implementation, the neural network <NUM> was provided with both pre-operative and post-refractive measurements of patients who had previously had laser-assisted in-situ keratomileusis (LASIK). The post-refractive measurements can be viewed as an error in the formula due to the previous refractive surgery. Being trained based on the difference for the pre-operative measurements and post-refractive measurements, the neural network <NUM> may provide a correction to the result provided by the IOL formula component <NUM>.

<FIG> is a flowchart illustrating an example method <NUM> of providing a recommended intraocular lens power. The method <NUM> may be performed by the system <NUM>.

In block <NUM> the method <NUM> includes obtaining at least two ocular measurement parameters and a lens selection parameter for an eye. In an aspect, for example, the UI <NUM> may obtain the at least two ocular measurement parameters and a lens selection parameter for an eye. In an implementation, the UI <NUM> may obtain the parameters for both eyes of a patient. In another implementation, the measurement parameters may be obtained from an ocular measurement device <NUM>. The ocular measurement parameters may include, for example, axial length, corneal power, corneal power index, and anterior chamber depth. In an aspect, the ocular measurement parameters may include intraoperative aberrometry measurements such as sphere, cylinder, and axis of the eye.

In block <NUM>, the method <NUM> includes determining an intraocular lens power based on a formula using the at least two ocular measurement parameters. For example, the formula component <NUM> may determine the intraocular lens power based on the formula using the at least two ocular measurement parameters. For instance, in block <NUM>, determining the intraocular lens power based on the formula may optionally include determining a relevant portion of a super surface including ideal or near ideal portions of a plurality of intraocular lens selection formulas based on a range of the at least two ocular measurement parameters most suitable to each individual intraocular lens selection formula.

In block <NUM>, the method <NUM> may include determining an estimated error of the formula using a deep learning machine trained on verified post-operative results including post-operative refractions corresponding to intraocular lens powers. In an aspect, for example, the neural network <NUM> may determine the estimated error of the formula. The neural network <NUM> may have been trained on training sets <NUM> including verified post-operative results including post-operative refractions corresponding to intraocular lens powers. The verified post-operative results may be obtained from a measurement device such as an autorefractor or a wavefront analyzer.

In block <NUM>, the method <NUM> includes adjusting the lens selection parameter based on the estimated error. In an aspect, for example, the formula component <NUM> may adjust the lens selection parameter based on the estimated error. For instance, the formula component <NUM> may subtract the estimated error from a user input lens selection parameter.

In block <NUM>, the method <NUM> includes redetermining the intraocular lens power based on the formula and the adjusted lens selection parameter. In an aspect, for example, the formula component <NUM> may redetermine the intraocular lens power based on the formula and the adjusted lens selection parameter. For instance, the block <NUM> may also include the optional block <NUM>.

In block <NUM>, the method <NUM> may optionally include rendering the super surface including the relevant portion on a display device. For example, the Ul <NUM> may render the super surface including the relevant portion including the at least two measurement parameters and the intraocular lens power.

Turning now to <FIG>, an example apparatus <NUM> may combine various portions of the system <NUM> with medical diagnostic equipment to provide a single apparatus that implements all or part of the system <NUM>. For example, the apparatus <NUM> may recommend an intraocular lens power based on a formula and a trained deep learning machine <NUM>. It should be appreciated that while in an example implementation the apparatus <NUM> may physically include multiple components within a single case <NUM>, the apparatus <NUM> may also be implemented as interconnected components, which may or may not be physically co-located. For example, in an aspect, verified results from multiple apparatuses <NUM> may provide post-operative measurements to a network located database in addition to or instead of using the post-operative measurements locally. Some components of the apparatus <NUM> may be implemented as computer-executable instructions stored on a computer-readable medium such as memory <NUM>. The instructions may be executed by a processor <NUM>. In an aspect, the processor <NUM> and the memory <NUM> may reside within the case <NUM>. Additionally, the apparatus <NUM> may include a display <NUM>, which may display a user interface <NUM>. The display <NUM> may be a touch sensitive screen that receives input from a user. Alternative or additional input/output devices may be included.

The example apparatus <NUM> includes a biometer <NUM> and an autorefractor <NUM> for obtaining measurements of an eye. The biometer <NUM> may obtain physical characteristics of the eye such as, but not limited to, corneal power, axial length, anterior chamber depth, corneal power index, a white-to-white distance, and/or lens thickness. The autorefractor <NUM> may obtain optical measurements of the eye such as, but not limited to, the refraction of the eye, sphere, cylinder, and axis. In an aspect, the autorefractor <NUM> may perform other vision assessment functions including aberrometry, topography, keratometry, and puplillometry. For example, the autorefractor <NUM> may include a wavefront analyzer or be referred to as a wavefront analyzer. Conventionally, biometers and autorefractors are separate devices that are used for different purposes. For example, a biometer may be used to obtain measurements for selecting an intraocular lens, whereas an autorefractor may be used to estimate a patient's prescription for eyeglasses or contact lenses. In an aspect, the biometer <NUM> and the autorefractor <NUM> may be located in separate sensor heads of the apparatus <NUM>. The apparatus <NUM> may include a single chinrest for positioning the patient with respect to one of the sensor heads. Each sensor head may be moved with respect to the chinrest to position the head for obtaining the respective measurements. It should be appreciated that various alternative physical arrangements of the biometer <NUM> and autorefractor <NUM> may be constructed.

In an aspect, the biometer <NUM> and the autorefractor <NUM> may store measurements in a patient data storage <NUM>. The patient data storage <NUM> may be a computer memory, preferably a non-volatile computer memory such as a hard disc drive, solid state drive, EEPROM, etc. The biometer <NUM> and the autorefractor <NUM> may access a file of a patient in the patient data storage <NUM> and directly record measurements. Such automatic recording may reduce manual transcription errors. In an alternative implementation, the patient data storage <NUM> may be stored externally such as, for example, on a doctor's patient management system or a network storage system, in which case the apparatus <NUM> may electronically communicate with the external storage.

The apparatus <NUM> may include a user interface <NUM>. The user interface <NUM> may guide a user (e.g., a technician) through operating the biometer <NUM> and autorefractor <NUM> to obtain measurements from a patient. The user interface <NUM> may also include a user interfaces similar to the user interfaces <NUM> (<FIG>) and <NUM> (<FIG>). The user interface <NUM> may automatically import measurements into the input field <NUM> from the patient data storage <NUM>. The user interface <NUM> may receive input from the user for the A-constant field <NUM> and the target refraction field <NUM>. The user interface <NUM> may provide the same information as in the user interface <NUM>.

The formula component <NUM> may be similar to the formula component <NUM>. The formula component <NUM> may receive the ocular measurements directly from the biometer <NUM> or from the patient data storage <NUM>. The formula component <NUM> may implement any of the intraocular lens power determination formulas described herein or known in the art.

The formula component <NUM> may provide the determined intraocular lens power value to the neural network <NUM> along with all of the input measurements and parameters. The learning machine <NUM> may be similar to the learning machine <NUM> and include, for example, a neural network <NUM>. The learning machine <NUM> may use deep learning techniques to predict error of the formula component <NUM> based on the training set <NUM> including post-operative results. In an aspect, the training set <NUM> may include post-operative results received from the autorefractor <NUM>. In an aspect, the training set <NUM> may include post-operative results from only the autorefractor <NUM> such that the trained learning machine <NUM> is specific for the apparatus <NUM>. That is, by training the learning machine <NUM> based on input measurements and post-operative results from a single apparatus, the apparatus <NUM> may be calibrated to correct for previous errors. In another aspect, the training set <NUM> may be combined with other verified results <NUM> such as results from other apparatuses <NUM>, which may be remotely located. The administration portal <NUM> may receive and authenticate the verified results <NUM>, for example, from a trusted web service. In an aspect, the access control <NUM> may be accessed by a user via the user interface <NUM> to specify which training set <NUM> to use.

In another aspect, some functionality of apparatus <NUM> may be performed via network service <NUM>. For example, the formula component <NUM> may be periodically updated based on results of machine learning performed remotely. For example, the system <NUM> may provide the network service <NUM>. The system <NUM> may periodically generate an updated formula, e.g., based on learning machine <NUM>, and provide the updated formula to the formula component <NUM> via the network service <NUM>. In that case, the learning machine <NUM> and training set <NUM> may be remotely located (e.g., as learning machine <NUM> and training set <NUM>). The admin portal <NUM> may be used to receive the updated formula component <NUM>.

In another aspect, the apparatus <NUM> may retain a local learning machine <NUM>, which may be trained by the network service <NUM> or system <NUM>. The apparatus <NUM> may transmit correlated pre-operative and post-operative measurements to the network service <NUM> via the admin portal <NUM>. The system <NUM> may then train a learning machine <NUM> based on a training set <NUM> including the correlated pre-operative and post-operative measurements of apparatus <NUM>. The system <NUM> may then provide the trained learning machine <NUM> to the apparatus <NUM> for installation. Accordingly, the apparatus <NUM> may utilize the trained learning machine <NUM> without performing training and without accessing verified results of other apparatuses <NUM>, which may include confidential data or protected health information (PHI).

<FIG> is a flowchart illustrating an example method <NUM> of providing a recommended intraocular lens power. The method <NUM> may be performed by the apparatus <NUM>. The method <NUM> may include some similar blocks to the method <NUM>. It should be appreciated that the methods <NUM> and <NUM> may be combined. For brevity, description of some duplicate blocks is omitted. Further, as described above, the system <NUM> may perform some optional blocks of the method <NUM>.

In block <NUM>, the method <NUM> includes obtaining at least two ocular measurement parameters for an eye by a biometer. In an aspect, for example, the biometer <NUM> may obtain the at least two ocular measurement parameters and a lens selection parameter for an eye. In an implementation, the biometer <NUM> may obtain the parameters for both eyes of a patient. The ocular measurement parameters may include, for example, axial length, corneal power, corneal power index, and anterior chamber depth. In an aspect, the ocular measurement parameters may include intraoperative aberrometry measurements such as sphere, cylinder, and axis of the eye.

In block <NUM>, the method <NUM> includes obtaining a lens selection parameter for the eye. In an implementation, the user interface <NUM> may obtain the lens selection parameter for the eye. For example, the lens selection parameter may be a target refraction for the eye following insertion of the intraocular lens. The lens selection parameter may be entered by a technician or an ophthalmologist.

In block <NUM>, the method <NUM> includes determining an intraocular lens power based on a formula using the at least two ocular measurement parameters. For example, the formula component <NUM> may determine the intraocular lens power based on the formula using the at least two ocular measurement parameters.

In block <NUM>, the method <NUM> includes obtaining a post-operative refraction of the eye from an autorefractor communicatively coupled with the biometer. In an aspect, for example, the autorefractor <NUM> may obtain the post-operative refraction of the eye. The autorefractor <NUM> may be coupled to the biometer <NUM> such that the same apparatus is used to obtain the at least two ocular measurement parameters and the post-operative refraction. Additionally, the measurements may be stored in a common patient data storage <NUM>.

In block <NUM>, the method <NUM> includes correlating the at least two ocular measurement parameters, the intraocular lens power, and the post-operative refraction as a training set. Since the biometer <NUM> and the autorefractor <NUM> are communicatively coupled, the training set can be correlated directly from the devices without need for human data entry, which may result in transcription errors. Further, consistency may be improved by generating multiple data sets using a known pair of biometer <NUM> and autorefractor <NUM>.

In block <NUM>, the method <NUM> optionally includes training a deep learning machine using the post-operative refraction of the eye and the intraocular lens power to determine an estimated error of the formula. In an aspect, the apparatus <NUM> may train the learning machine <NUM> using the post-operative refraction of the eye and the intraocular lens power. The learning machine <NUM> may be trained to estimate the error of the formula component <NUM> for a particular set of input parameters including the at least two ocular measurement parameters and the lens selection parameter. The learning machine <NUM> may be trained by providing the training sets labeled with the post-operative refraction as the result. It should be understood that the learning machine <NUM> may be trained on training data from previous procedures. The at least two ocular measurement parameters for a current procedure may not be included in the training data because the post-operative refraction is not available. Once the post-operative refraction becomes available, the complete training set may be used to further train or retrain the learning machine <NUM>. In an aspect, the block <NUM> may be performed by an external system such as the system <NUM>, which may communicate with the apparatus <NUM> via a network service <NUM>. The apparatus <NUM> may receive the trained learning machine <NUM> via the network service <NUM>.

In block <NUM>, the method <NUM> includes determining an estimated error of the formula using a deep learning machine trained on verified post-operative results including post-operative refractions corresponding to intraocular lens powers. In an aspect, for example, the learning machine <NUM> may determine the estimated error of the formula. As discussed above, the learning machine <NUM> may have been trained on training sets <NUM> or <NUM> including verified post-operative results including post-operative refractions corresponding to intraocular lens powers.

In block <NUM>, the method <NUM> includes redetermining the intraocular lens power based on the formula and the adjusted lens selection parameter. In an aspect, for example, the formula component <NUM> may redetermine the intraocular lens power based on the formula and the adjusted lens selection parameter.

<FIG> is a conceptual diagram illustrating an example use context for the example apparatus <NUM>. The apparatus <NUM> may be referred to as a self-calibrating biometer. The apparatus <NUM> may automate collection of objective data that can be used to calibrate the apparatus <NUM>. For example, IOL calculations of the apparatus <NUM> may be improved using deep learning to analyze post-operative results obtained via the autorefractor <NUM>. The apparatus <NUM> may continually improve as additional data is collected.

Claim 1:
An apparatus (<NUM>) for intraocular lens selection, comprising:
a biometer (<NUM>) configured to obtain at least two pre-operative ocular measurement parameters for an eye;
a user interface (<NUM>) configured to obtain a lens selection parameter for the eye;
an autorefractor (<NUM>) configured to obtain a post-operative refraction of the eye;
a memory (<NUM>); and
a processor (<NUM>) communicatively coupled to the biometer, the user interface, the autorefractor, and the memory, and configured to:
determine (<NUM>) an intraocular lens power based on a formula using the at least two pre-operative ocular measurement parameters;
determine (<NUM>) an estimated error of the formula using a deep learning machine trained on verified post-operative results including post-operative refractions corresponding to intraocular lens powers that are associated with at least two pre-operative ocular measurement parameters;
adjust (<NUM>) the lens selection parameter based on the estimated error;
redetermine (<NUM>) a final intraocular lens power based on the formula and the adjusted lens selection parameter; and
correlate (<NUM>) the at least two pre-operative ocular measurement parameters, the intraocular lens power, and the post-operative refraction as a training set.