Patent Description:
Cataract surgery involves removing the natural lens of an eye and, in most cases, replacing the natural lens with an artificial intraocular lens (IOL). To achieve an optimal post-operative visual outcome, a good pre-operative surgical plan is crucial. Some of the important pre-operative planning decisions are the selection of an appropriate IOL type and power to achieve a desired manifest refraction in spherical equivalent (MRSE) after IOL implantation.

Typically, the measurements used in the IOL prediction formulas are one-dimensional measurements taken on the optical axis using an optical and/or ultrasound biometer. These traditional measurement practices lead to inaccuracy during the selection of an IOL type and power that results in a suboptimal vision outcome for the patient.

Document <CIT>, which is considered to represent the closest prior art, discloses a method to determine an estimated MRSE after the implant of an IOL, but it is silent about the estimation whether the post-operative eye will be in an emmetropia zone or not.

Therefore, there is a need in the art for techniques for better selecting an intraocular lens for implantation that leads to optimized vision outcomes for patients.

For a more complete understanding of the present technology, its features, and its advantages, reference is made to the following description, taken in conjunction with the accompanying drawings.

In the figures, elements having the same designations have the same or similar functions.

This description and the accompanying drawings that illustrate inventive aspects, embodiments, implementations, or modules should not be taken as limiting-the claims define the protected invention.

In this description, specific details are set forth describing some embodiments consistent with the present disclosure. Numerous specific details are set forth in order to provide a thorough understanding of the embodiments. It will be apparent, however, to one skilled in the art that some embodiments may be practiced without some or all of these specific details. The specific embodiments disclosed herein are meant to be illustrative but not limiting. One skilled in the art may realize other elements that, although not specifically described here, are within the scope of this disclosure. In addition, to avoid unnecessary repetition, one or more features shown and described in association with one embodiment may be incorporated into other embodiments unless specifically described otherwise or if the one or more features would make an embodiment non-functional.

The technology described below involves systems and methods to better estimate post implantation vision outcomes for a new patient by estimating a post-operative MRSE for the patient. The systems and methods use multiple prediction models to estimate the post-operative MRSE for several IOLs and IOL powers to aid a surgeon and patient in the selection of an IOL most likely to provide a desired post-operative vision outcome. More specifically, the systems and methods use one or more models to determine whether a candidate IOL is likely to lead to a post-operative vision outcome within the emmetropia zone (i.e., a post-operative vision outcome where the patient does not need to supplement the IOL with an additional corrective lens, such as glasses) or outside the emmetropia zone and then using separate prediction models to estimate the post-operative MRSE for an IOL predicted to lead to a vision outcome in the emmetropia zone and an IOL predicted to lead to a vision outcome outside the emmetropia zone.

<FIG> illustrates a system <NUM> for a system for IOL selection according to some embodiments. System <NUM> includes an IOL selection platform <NUM> coupled with one or more diagnostic training data sources <NUM> via a network <NUM>. In some examples, network <NUM> may include one or more switching devices, routers, local area networks (e.g., an Ethernet), wide area networks (e.g., the Internet), and/or the like. Each of the diagnostic training data sources <NUM> may be a database, a data repository, and/or the like made available by an, ophthalmic surgery practice, an eye clinic, a medical university, an electronic medical records (EMR) repository, and/or the like. Each of the diagnostic training data sources <NUM> may provide IOL selection platform <NUM> with training data in the form of one or more of multi-dimensional images and/or measurements of patients' pre- and post-operative eyes, surgical planning data, surgical console parameter logs, surgical complication logs, patient medical history, patient demographic data, information on an implanted IOL, and/or the like. IOL selection platform <NUM> may store the training data in one or more databases <NUM> which may be configured to anonymize, encrypt, and/or otherwise safeguard the training data.

IOL selection platform <NUM> includes a prediction engine <NUM> which may (as explained in greater detail below) process the received training data, extract measurements of an eye, perform raw data analysis on the training data, train machine learning algorithms and/or models to estimate a post-operative MRSE based on the pre-operative measurements, and iteratively refine the machine learning to optimize the various models used to predict the post-operative MRSE to improve their use with future patients to improve their post-operative vision outcomes (e.g., better optical properties of the eye with the implanted IOL). In some examples, prediction engine <NUM> may use one or more models (e.g., one or more a neural networks) that are trained based on pre-operative measurements and corresponding post-operative outcomes obtained from the one or more diagnostic training data sources <NUM>.

IOL selection platform <NUM> is further coupled, via network <NUM>, to one or more devices of an ophthalmic practice <NUM>. The one or more devices include a diagnostic device <NUM>. Diagnostic device <NUM> is used to obtain one or more multi-dimensional images and/or other measurements of an eye of a patient <NUM>. Diagnostic device <NUM> may be any of a number of devices for obtaining multi-dimensional images and/or measurements of ophthalmic anatomy such as an optical coherence tomography (OCT) device, a rotating camera (e.g., a Scheimpflug camera), a magnetic resonance imaging (MRI) device, a keratometer, an ophthalmometer, an optical biometer, and/or the like.

The ophthalmic practice <NUM> may also include one or more computing devices <NUM> for obtaining, from the diagnostic device <NUM>, the multi-dimensional images and/or measurements of patient <NUM> and sending them to IOL selection platform <NUM>. The one or more computing devices <NUM> may be one or more of a stand-alone computer, a tablet and/or other smart device, a surgical console, a computing device integrated into the diagnostic device <NUM>, and/or the like.

IOL selection platform <NUM> may receive measurements of patient <NUM>, and/or compute values from the measurements, and generate an estimate of the post-operative MRSE for various IOLs and IOL powers using prediction engine <NUM>. Prediction engine may then be used to help select an IOL and IOL power for patient <NUM> by providing ophthalmic practice <NUM> and/or a surgeon or other user with the estimated post-operative MRSE for the various IOLs and IOL powers.

Diagnostic device <NUM> may further be used to obtain post-operative measurements of patient <NUM> after the patient undergoes cataract removal and IOL implantation using the selected IOL. The one or more computing devices <NUM> may then send the post-operative multi-dimensional images and/or measurements of patient <NUM> and the selected IOL to IOL selection platform <NUM> for use in iteratively training and/or updating the models used by prediction engine <NUM> so as to incorporate information from patient <NUM> for use with future patients.

The estimated post-operative MRSE, selected IOL, and/or selected IOL power may be displayed on computing device <NUM> and/or another computing device, display, surgical console, and/or the like. Additionally, IOL selection platform <NUM> and/or the one or more computing devices <NUM> may identify in the measurements various characteristics of the anatomy of patient <NUM>, as explained below in more detail. Further, IOL selection platform <NUM> and/or the one or more computing devices <NUM> may create graphical elements that identify, highlight, and/or otherwise depict the patient anatomy and/or the measured characteristics. IOL selection platform <NUM> and/or the one or more computing devices <NUM> may supplement the measurements with the graphical elements.

In some embodiments, IOL selection platform <NUM> may further include a surgical planner <NUM> that may be used to provide one or more surgical plans to ophthalmic practice <NUM> that uses the estimated post-operative MRSE, the selected IOL, and/or the selected IOL power.

In some embodiments, system <NUM> may further include a stand-alone surgical planner <NUM> and/or ophthalmic practice <NUM> may further include a surgical planner module <NUM> on the one or more computing device <NUM>.

As discussed above and further emphasized here, <FIG> is merely an example which should not unduly limit the scope of the claims. One of ordinary skill in the art would recognize many variations, alternatives, and modifications. According to some embodiments, IOL selection platform <NUM> and/or one or more components of IOL selection platform <NUM>, such as databases <NUM>, prediction engine <NUM>, and/or surgical planner <NUM>, may be integrated into the one or more devices of ophthalmic practice <NUM>. In some examples, computing device <NUM> may host IOL selection platform <NUM>, databases <NUM>, prediction engine <NUM>, and/or surgical planner <NUM>. In some examples, surgical planner <NUM> may be combined with surgical planner <NUM>.

<FIG> is a diagram of a method <NUM> of implanting an IOL according to some embodiments. One or more of the processes <NUM>-<NUM> of method <NUM> may be implemented, at least in part, in the form of executable code stored on non-transitory, tangible, machine-readable media that when run by one or more processors (e.g., the processors of prediction engine <NUM>, IOL prediction platform, diagnostic device <NUM>, the one or more computing devices <NUM>, and/or one or more of the surgical planners <NUM>, <NUM>, and/or <NUM>) may cause the one or more processors to perform one or more of the processes <NUM>-<NUM>. According to some embodiments, processes <NUM> and/or <NUM> are optional and may be omitted.

At a process <NUM>, one or more pre-operative measurements of an eye are determined. In some examples, one or more of the pre-operative measurements may be extracted from one or more pre-operative images of the eye obtained using a diagnostic device, such as diagnostic device <NUM>, an OCT device, a rotating (e.g., Scheimpflug) camera, an MRI device, and/or the like. In some examples, one or more of the pre-operative measurements may be determined using one or more measuring devices, such as diagnostic device <NUM>, a keratometer, an ophthalmometer, an optical biometer, and/or the like. Process <NUM> is described in the context of <FIG>, which is a diagram of an eye <NUM> and characteristics of the eye according to some embodiments. As shown in <FIG>, eye <NUM> includes a cornea <NUM>, an anterior chamber <NUM>, and a lens <NUM>.

In some embodiments, one measurement of interest for eye <NUM> is the white-to-white diameter of cornea <NUM>. In some examples, the white-to-white diameter of cornea <NUM> may be measured using an optical biometer. In some examples, the white-to-white diameter of cornea <NUM> may be determined by analyzing one or more images of eye <NUM>. In some examples, the one or more images may be analyzed to identify nasal and temporal angles <NUM> and <NUM>, respectively, of anterior chamber <NUM>. In some examples, nasal and temporal angles <NUM> and <NUM> of anterior chamber <NUM> may be identified from the one or more images by identifying the structures identifying anterior chamber <NUM> (e.g., using one or more edge detection and/or region detection algorithms) and noting the acute angles at the edges of anterior chamber <NUM> located toward the temporal and nasal extents of anterior chamber <NUM>. Once identified, a distance between the nasal and temporal angles <NUM> and <NUM> may be measured to determine the white-to-white diameter of cornea <NUM>, which corresponds to a length of line <NUM> between nasal and temporal angles <NUM> and <NUM>.

In some embodiments, one measurement of interest for eye <NUM> is the average keratometry or roundness of the anterior surface of cornea <NUM>. In some examples, the average keratometry of cornea <NUM> may be measured using the one or more images of eye <NUM>, a keratometer, and/or the like. In some examples, the average keratometry of cornea <NUM> may be base based on an average of the steep keratometry and the shallow keratometry measurements of cornea <NUM>. In some examples, the average keratometry of cornea <NUM> may be expressed as a radius of curvature (rc) of cornea <NUM>, which is <NUM> divided by the average keratometry.

In some embodiments, one measurement of interest from eye <NUM> is the axial length <NUM> of eye <NUM> as measured from the anterior surface of cornea <NUM> to the retina along central axis <NUM> of eye <NUM>. In some examples, axial length <NUM> may be determined using the one or more images of eye <NUM>, biometry of the eye, and/or the like.

Referring back to <FIG>, at a process <NUM>, a pre-operative anterior chamber depth (ACD) of the eye is estimated. In the examples, of <FIG>, the pre-operative ACD <NUM> corresponds to the distance between the posterior surface of cornea <NUM> and the anterior surface of the pre-operative lens <NUM>. In some examples, the pre-operative ACD may be estimated using a combination of one or more geometric models of the eye and a first correction model. In some examples, each of the one or more geometric models provide an initial estimate of the pre-operative ACD based on the radius of curvature (rc) of the cornea, the axial length of the eye, and the white-to-white diameter of the cornea as measured during process <NUM>. In some examples, each of the one or more geometric models of the eye may be determined by fitting each of the one or more geometric models using, for example, a least squares approach to data from the eyes of previous patients where the radius of curvature, axial length, white-to-white diameter, and anterior chamber depth are known. In some examples, the data may be stored in a data source, such as data source <NUM>. Each of the initial estimates of the pre-operative ACD, the radius of curvature, axial length, and white-to-white diameter are then passed to the first correction model that refines the one or more initial estimates of the pre-operative ACD to provide a more accurate estimate of the pre-operative ACD. In some examples, the first correction model may include a neural network trained using the data from the eyes of previous patients.

At a process <NUM>, a post-operative anterior chamber depth (ACD) of the eye is estimated. In the examples, of <FIG>, the post-operative ACD <NUM> corresponds to the distance between the posterior surface of cornea <NUM> and the anterior surface of an IOL to be implanted into eye <NUM>. In some examples, the post-operative ACD may be estimated using a combination of the one or more geometric models of the eye used during process <NUM> and a first prediction model. Each of the initial estimates of the pre-operative ACD from process <NUM>, the radius of curvature, axial length, and white-to-white diameter are then passed to the first prediction model to generate the estimate of the post-operative ACD. In some examples, the first prediction model may include a neural network trained using the data from the eyes of previous patients including information about the post-operative ACD for each implanted IOL.

At a process <NUM>, one or more candidate intraocular lenses (IOLs) are identified. In some examples, the one or more candidate IOLs may be selected by the surgeon or other user based on one or more of the IOLs that are available, past experience, preference, current vision issues of the patient, anticipated vision outcomes for the patient, and/or the like. Each of the one or more candidate IOLs has a corresponding type and IOL power.

At a process <NUM>, a post-operative manifest refraction in spherical equivalent (MRSE) is estimated for each of the candidate IOLs identified during process <NUM>. The MRSE is indicated in diopters (D). According to some embodiments, the MRSE for each of the candidate IOLs may be determined using a method of evaluating an IOL and IOL power, such as a method <NUM> as shown in <FIG>. One or more of the processes <NUM>-<NUM> of method <NUM> may be implemented, at least in part, in the form of executable code stored on non-transitory, tangible, machine-readable media that when run by one or more processors (e.g., the processors of prediction engine <NUM>, IOL prediction platform, and/or the like) may cause the one or more processors to perform one or more of the processes <NUM>-<NUM>.

At a process <NUM>, the post-operative MRSE is estimated. In some examples, the post-operative MRSE may be estimated based on one or more of the white-to-white diameter determined during process <NUM>, the average keratometry of the eye determined during process <NUM>, the axial length determined during process <NUM>, the pre-operative ACD estimated during process <NUM>, the post-operative ACD estimated during process <NUM>, the IOL type of the IOL being evaluated, the IOL power of the IOL being evaluated, and/or the like. In some examples, process <NUM> may use a ray tracing approach that uses a paraxial model eye constructed to represent a pseudophakic eye implanted with the IOL being evaluated. In the ray tracing model, a ray entering the eye sequentially passes through the anterior surface of the cornea, the posterior surface of the cornea, the anterior surface of the IOL, and the posterior surface of the IOL, before finally reaching the surface of the retina. The shape of each of these surfaces is defined as a biconic shape using parameters from measurements of the eye determined during process <NUM>, the design profile of the IOL, the estimated post-operative ACD, and known models of the eye. The refractive indices of the ocular material or medium in the cornea, the anterior chamber, the other parts of the eye are known from models of the eye and/or models of the IOL.

In some embodiments, the ray tracing is used to "measure" a whole-eye wave front from the paraxial model eye. Rays are traced from the center of the retina (e.g., the fovea), radiating towards the anterior surface of the cornea in all directions. A planar surface is placed at the apex of the anterior surface of the cornea. The loci of intersection of the ray with the planar surface upon exiting the eye and the corresponding optical path length are recorded. In some examples, ray data within a circular aperture (e.g., corresponding to an entrance pupil) of <NUM> in diameter is used to calculate the whole-eye wave front.

In some examples, the estimated post-operative MRSE may be calculated using Zernike polynomials from the representation of the whole-eye wave front. In some examples, the estimated post-operative MRSE includes a combination of both spherical and cylindrical refractive power of the eye with an implanted IOL corresponding to the IOL being evaluated. The ray tracing approach is described in more detail in <NPL>.

At a process <NUM>, the estimated post-operative MRSE from process <NUM> is corrected. In some examples, the estimated post-operative MRSE may be corrected using a second correction model. The second correction model may use one or more of the measurements of the eye determined during process <NUM> (e.g., the white-to-white diameter, the average keratometry, and/or the axial length), the post-operative ACD estimated during process <NUM>, and/or the post-operative MRSE estimated during process <NUM> to determine a correction to the post-operative MRSE estimated during process <NUM>. In some examples, the second correction model may include a neural network trained using data from the eyes of previous patients including both estimated and actual post-operative MRSEs. In some examples, the second correction model may determine a correction value that is added to the post-operative MRSE estimated during process <NUM> to obtain a corrected estimate of the post-operative MRSE.

At a process <NUM>, it is determined whether the IOL being evaluated places the post-operative eye in the emmetropia zone. In some examples, the post-operative eye is considered to be in the emmetropia zone when the post-operative MRSE is within half a diopter of a desired post-operative MRSE for the eye so that the post-operative eye is unlikely to need an additional corrective lens. , when an absolute difference between the desired and actual post-operative MRSEs are within a half diopter of each other. ) In some examples, whether the IOL being evaluated places the post-operative eye in the emmetropia zone may be determined by taking the absolute difference between the corrected estimate of the post-operative MRSE determined during process <NUM> and the desired post-operative MRSE for the eye so see whether it is less than half a diopter.

In some examples, a more robust test of whether the IOL being evaluated places the post-operative eye in the emmetropia zone may be preferred because the corrected estimate of the post-operative ACD is merely an estimate. In some examples, a more robust classifier for determining whether the IOL being evaluated places the post-operative eye in the emmetropia zone may be developed using statistical techniques, such as a logistic regression based on bivariate density quantiles and a receiver operating characteristic (ROC) curve. In some examples, this approach may fit a regression curve, such as a logistic regression curve, between pairs of data that include previous corrected estimates of the post-operative MRSE and corresponding actual post-operative MRSE values from implanted IOLs. Bivariate quantiles may then be used with the regression curve to provide a classifier that predicts the likelihood that the actual post-operative MRSE will be within the emmetropia zone (e.g., within a half diopter of the desired post-operative MRSE). A ROC curve may then be used to determine a threshold of likelihood that should be exceeded to reliably conclude that the implanted IOL will place the post-operative eye will be in the emmetropia zone. Use of the ROC curve accounts for predictions that result in false positive and false negative determinations as to whether the post-operative eye will be in the emmetropia zone, thus, providing a classification approach that better maximizes both sensitivity and specificity of the regression-based classifier. Thus, use of this approach provides an estimated likelihood of whether the IOL being evaluated will result in the post-operative eye being in the emmetropia zone and a threshold likelihood that should be exceeded in order to conclude that the post-operative eye will be in the emmetropia zone using the IOL being evaluated. ROC curves are described in more detail in <NPL> and <NPL>.

When it is determined that the IOL being evaluated is likely to place the post-operative eye in the emmetropia zone (e.g., the likelihood predicted by the classifier is greater than or equal to the threshold from the ROC curve), the post-operative MRSE is re-estimated using an emmetropia zone prediction model using a process <NUM>. When it is determined that the IOL being evaluated is not likely to place the post-operative eye in the emmetropia zone, the post-operative MRSE is re-estimated using a non-emmetropia zone prediction model using a process <NUM>.

At the process, <NUM> an emmetropia zone prediction model is used to re-estimate the post-operative MRSE for the IOL being evaluated. In some examples, the emmetropia zone prediction model may use one or more of the measurements of the eye determined during process <NUM> (e.g., the white-to-white diameter, the average keratometry, and/or the axial length), and/or the post-operative ACD estimated during process <NUM> to re-estimate the post-operative MRSE. In some examples, the emmetropia zone prediction model may include a neural network trained using data from the eyes of previous patients including both estimated and actual post-operative MRSEs where the post-operative eye was placed in the emmetropia zone. Once the post-operative MRSE is re-estimated using the emmetropia zone prediction model, method <NUM> concludes.

At the process, <NUM> a non-emmetropia zone prediction model is used to re-estimate the post-operative MRSE for the IOL being evaluated. In some examples, the non-emmetropia zone prediction model may use one or more of the measurements of the eye determined during process <NUM> (e.g., the white-to-white diameter, the average keratometry, and/or the axial length), and/or the post-operative ACD estimated during process <NUM> to re-estimate the post-operative MRSE. In some examples, the non-emmetropia zone prediction model may include a neural network trained using data from the eyes of previous patients including both estimated and actual post-operative MRSEs where the post-operative eye was placed in the non-emmetropia zone. Once the post-operative MRSE is re-estimated using the non-emmetropia zone prediction model, method <NUM> concludes.

Referring back to <FIG>, at a process <NUM>, an IOL is selected from among the candidate IOLs. In some examples, process <NUM> may include providing information via a user interface to the surgeon, another user, and/or the patient so that an appropriate IOL may be selected for implantation. In some examples, the information may include, for each of the candidate IOLs identified during process <NUM>, the IOL type, the IOL power, the estimated post-operative ACD from process <NUM>, the emmetropia zone determination from process <NUM>, the corrected post-operative estimate of MRSE from process <NUM>, the re-estimated post-operative MRSE from process <NUM> or <NUM> (as applicable), the difference between the re-estimated post-operative MRSE and a desired post-operative MRSE, and/or the like. In some examples, the candidate IOLs may be separated into groups based on the emmetropia zone determination from process <NUM> and/or sorted based on the actual difference or absolute difference between the re-estimated post-operative MRSE and a desired post-operative MRSE. In some examples, the surgeon or other user may select the IOL by selecting from the list of results, clicking a link, pressing a button, and/or the like.

At a process <NUM>, the IOL is implanted. In some examples, the IOL selected during process <NUM> is implanted in the eye by the surgeon.

At a process <NUM>, one or more post-operative measurements of the eye are obtained. In some examples, the one or more post-operative measurements may include an actual post-operative ACD of the IOL after implantation of the IOL, an actual post-operative MRSE after implantation of the IOL, an actual post-operative emmetropia zone determination, and/or the like. In some examples, the actual post-operative ACD and/or the actual post-operative MRSE may be determined based on one or more images of the post-operative eye, one or more physiological and/or optical measurements of the post-operative eye, and/or the like.

At a process <NUM>, the one or more models used by methods <NUM> and/or <NUM> are updated. In some examples, the one or more pre-operative measurements determined during process <NUM>, the actual post-operative ACD determined during process <NUM>, the actual post-operative MRSE, the actual post-operative emmetropia zone determination, and/or the like may be used as additional training data for any of the one or more geometric models of process <NUM>, the first correction model of process <NUM>, the first prediction model of process <NUM>, the second correction model of process <NUM>, the emmetropia zone prediction model of process <NUM>, or the non-emmetropia zone prediction model of process <NUM>. In some examples, the additional training data may be added to a data source, such as data source <NUM>. In some examples, the updating may include one or more of updating least-squares fits, feedback to neural networks (e.g., using back propagation), and/or the like. In some examples, one or more of the models may be trained using one or more loss functions based on their ability to correctly predict the post-operative MRSE for the various candidate IOLs. In some examples, the one or more loss functions may include a mean absolute error loss function (MAPE) determined according to Equation <NUM> and/or a probability of half diopter prediction success loss function (MAE) determined according to Equation <NUM>, where MRSEActi and MRSEEsti are the actual and estimated post-operative MRSE for ith training example and N is the number of training samples. <MAT><MAT>.

<FIG> are diagrams of processing systems according to some embodiments. Although two embodiments are shown in <FIG>, persons of ordinary skill in the art will also readily appreciate that other system embodiments are possible. According to some embodiments, the processing systems of <FIG> and/or 5B are representative of computing systems that may be included in one or more of IOL selection platform <NUM>, ophthalmic practice <NUM>, prediction engine <NUM>, diagnostic device <NUM>, the one or more computing devices <NUM>, any of surgical planner <NUM>, <NUM>, and/or <NUM>, and/or the like.

<FIG> illustrates a computing system <NUM> where the components of system <NUM> are in electrical communication with each other using a bus <NUM>. System <NUM> includes a processor <NUM> and a system bus <NUM> that couples various system components including memory in the form of a read only memory (ROM) <NUM>, a random access memory (RAM) <NUM>, and/or the like (e.g., PROM, EPROM, FLASH-EPROM, and/or any other memory chip or cartridge) to processor <NUM>. System <NUM> may further include a cache <NUM> of high-speed memory connected directly with, in close proximity to, or integrated as part of processor <NUM>. System <NUM> may access data stored in ROM <NUM>, RAM <NUM>, and/or one or more storage devices <NUM> through cache <NUM> for high-speed access by processor <NUM>. In some examples, cache <NUM> may provide a performance boost that avoids delays by processor <NUM> in accessing data from memory <NUM>, ROM <NUM>, RAM <NUM>, and/or the one or more storage devices <NUM> previously stored in cache <NUM>. In some examples, the one or more storage devices <NUM> store one or more software modules (e.g., software modules <NUM>, <NUM>, <NUM>, and/or the like). Software modules <NUM>, <NUM>, and/or <NUM> may control and/or be configured to control processor <NUM> to perform various actions, such as the processes of methods <NUM> and/or <NUM>. And although system <NUM> is shown with only one processor <NUM>, it is understood that processor <NUM> may be representative of one or more central processing units (CPUs), multi-core processors, microprocessors, microcontrollers, digital signal processors (DSPs), field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), graphics processing units (GPUs), tensor processing units (TPUs), and/or the like. In some examples, system <NUM> may be implemented as a stand-alone subsystem and/or as a board added to a computing device or as a virtual machine.

To enable user interaction with system <NUM>, system <NUM> includes one or more communication interfaces <NUM> and/or one or more input/output (I/O) devices <NUM>. In some examples, the one or more communication interfaces <NUM> may include one or more network interfaces, network interface cards, and/or the like to provide communication according to one or more network and/or communication bus standards. In some examples, the one or more communication interfaces <NUM> may include interfaces for communicating with system <NUM> via a network, such as network <NUM>. In some examples, the one or more I/O devices <NUM> may include on or more user interface devices (e.g., keyboards, pointing/selection devices (e.g., mice, touch pads, scroll wheels, track balls, touch screens, and/or the like), audio devices (e.g., microphones and/or speakers), sensors, actuators, display devices, and/or the like).

Each of the one or more storage devices <NUM> may include non-transitory and nonvolatile storage such as that provided by a hard disk, an optical medium, a solid-state drive, and/or the like. In some examples, each of the one or more storage devices <NUM> may be co-located with system <NUM> (e.g., a local storage device) and/or remote from system <NUM> (e.g., a cloud storage device).

<FIG> illustrates a computing system <NUM> based on a chipset architecture that may be used in performing any of the methods (e.g., methods <NUM> and/or <NUM>) described herein. System <NUM> may include a processor <NUM>, representative of any number of physically and/or logically distinct resources capable of executing software, firmware, and/or other computations, such as one or more CPUs, multi-core processors, microprocessors, microcontrollers, DSPs, FPGAs, ASICs, GPUs, TPUs, and/or the like. As shown, processor <NUM> is aided by one or more chipsets <NUM>, which may also include one or more CPUs, multi-core processors, microprocessors, microcontrollers, DSPs, FPGAs, ASICs, GPUs, TPUs, coprocessors, coder-decoders (CODECs), and/or the like. As shown, the one or more chipsets <NUM> interface processor <NUM> with one or more of one or more I/O devices <NUM>, one or more storage devices <NUM>, memory <NUM>, a bridge <NUM>, and/or one or more communication interfaces <NUM>. In some examples, the one or more I/O devices <NUM>, one or more storage devices <NUM>, memory, and/or one or more communication interfaces <NUM> may correspond to the similarly named counterparts in <FIG> and system <NUM>.

In some examples, bridge <NUM> may provide an additional interface for providing system <NUM> with access to one or more user interface (UI) components, such as one or more keyboards, pointing/selection devices (e.g., mice, touch pads, scroll wheels, track balls, touch screens, and/or the like), audio devices (e.g., microphones and/or speakers), display devices, and/or the like.

According to some embodiments, systems <NUM> and/or <NUM> may provide a graphical user interface (GUI) suitable for aiding a user (e.g., a surgeon and/or other medical personnel) in the performance of the processes of methods <NUM> and/or <NUM>. The GUI may include instructions regarding the next actions to be performed, diagrams of annotated and/or un-annotated anatomy, such as pre-operative and/or post-operative images of an eye (e.g., such as depicted in <FIG>), requests for input, and/or the like. In some examples, the GUI may display true-color and/or false-color images of the anatomy, and/or the like.

<FIG> is a diagram of a multi-layer neural network <NUM> according to some embodiments. In some embodiments, neural network <NUM> may be representative of a neural network used to implement each of the one or more models described with respect to processes <NUM>, <NUM>, <NUM>, <NUM>, and/or <NUM> and used by prediction engine <NUM>. Neural network <NUM> processes input data <NUM> using an input layer <NUM>. In some examples, input data <NUM> may correspond to the input data provided to the one or more models and/or the training data provided to the one or more models during the updating during process <NUM> used to train the one or more models. Input layer <NUM> includes a plurality of neurons that are used to condition input data <NUM> by scaling, range limiting, and/or the like. Each of the neurons in input layer <NUM> generates an output that is fed to the inputs of a hidden layer <NUM>. Hidden layer <NUM> includes a plurality of neurons that process the outputs from input layer <NUM>. In some examples, each of the neurons in hidden layer <NUM> generates an output that are then propagated through one or more additional hidden layers that end with hidden layer <NUM>. Hidden layer <NUM> includes a plurality of neurons that process the outputs from the previous hidden layer. The outputs of hidden layer <NUM> are fed to an output layer <NUM>. Output layer <NUM> includes one or more neurons that are used to condition the output from hidden layer <NUM> by scaling, range limiting, and/or the like. It should be understood that the architecture of neural network <NUM> is representative only and that other architectures are possible, including a neural network with only one hidden layer, a neural network without an input layer and/or output layer, a neural network with recurrent layers, and/or the like.

In some examples, each of input layer <NUM>, hidden layers <NUM>-<NUM>, and/or output layer <NUM> includes one or more neurons. In some examples, each of input layer <NUM>, hidden layers <NUM>-<NUM>, and/or output layer <NUM> may include a same number or a different number of neurons. In some examples, each of the neurons takes a combination (e.g., a weighted sum using a trainable weighting matrix W) of its inputs x, adds an optional trainable bias b, and applies an activation function f to generate an output a as shown in Equation <NUM>. In some examples, the activation function f may be a linear activation function, an activation function with upper and/or lower limits, a log-sigmoid function, a hyperbolic tangent function, a rectified linear unit function, and/or the like. In some examples, each of the neurons may have a same or a different activation function.

In some examples, neural network <NUM> may be trained using supervised learning (e.g.,, during process <NUM>) where combinations of training data that include a combination of input data and a ground truth (e.g., expected) output data. Differences between the output of neural network <NUM> as generated using the input data for input data <NUM> and comparing output data <NUM> as generated by neural network <NUM> to the ground truth output data. Differences between the generated output data <NUM> and the ground truth output data may then be fed back into neural network <NUM> to make corrections to the various trainable weights and biases. In some examples, the differences may be fed back using a back propagation technique using a stochastic gradient descent algorithm, and/or the like. In some examples, a large set of training data combinations may be presented to neural network <NUM> multiple times until an overall loss function (e.g., a mean-squared error based on the differences of each training combination) converges to an acceptable level.

Methods according to the above-described embodiments may be implemented as executable instructions that are stored on non-transitory, tangible, machine-readable media. The executable instructions, when run by one or more processors (e.g., processor <NUM> and/or process <NUM>) may cause the one or more processors to perform one or more of the processes of methods <NUM> and/or <NUM>. Some common forms of machine-readable media that may include the processes of methods <NUM> and/or <NUM> are, for example, floppy disk, flexible disk, hard disk, magnetic tape, any other magnetic medium, CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, RAM, PROM, EPROM, FLASH-EPROM, any other memory chip or cartridge, and/or any other medium from which a processor or computer is adapted to read.

Devices implementing methods according to these disclosures may comprise hardware, firmware, and/or software, and may take any of a variety of form factors. Typical examples of such form factors include laptops, smart phones, small form factor personal computers, personal digital assistants, and/or the like. Portions of the functionality described herein also may be embodied in peripherals and/or add-in cards. Such functionality may also be implemented on a circuit board among different chips or different processes executing in a single device, by way of further example.

Claim 1:
A method comprising:
determining (<NUM>), by one or more computing devices implementing a prediction engine, one or more pre-operative measurements of an eye;
estimating (<NUM>), by the prediction engine based on the one or more pre-operative measurements of the eye, a post-operative anterior chamber depth, ACD, of an intraocular lens IOL implanted in the eye;
estimating (<NUM>, <NUM>), by the prediction engine based on the one or more pre-operative measurements of the eye and the estimated post-operative ACD, a post-operative manifest refraction in spherical equivalent, MRSE, of the eye with the IOL implanted;
determining (<NUM>), by the prediction engine based on the estimated post-operative MRSE, whether the eye with the IOL implanted is in an emmetropia zone, the emmetropia zone being when the determined post-operative MRSE is within half a diopter of a target post-operative MRSE;
re-estimating, by the prediction engine based on the emmetropia zone determining, the post-operative MRSE of the eye with the IOL implanted:
using an emmetropia zone prediction model (<NUM>) comprising a neural network, if the eye with the IOL implanted is in the emmetropia zone; and
using a non-emmetropia zone prediction model (<NUM>) comprising a neural network, if the eye with the IOL implanted is not in the emmetropia zone; and
providing, by the prediction engine, the re-estimated post-operative MRSE to a user to aid in selection of an IOL for implantation in the eye.