SELECTION OF INTRAOCULAR LENS BASED ON A PREDICTED SUBJECTIVE OUTCOME SCORE

A system and method for selecting an intraocular lens, for implantation into an eye, includes a controller having a processor and a tangible, non-transitory memory on which instructions are recorded. The controller is configured to selectively execute a machine learning model trained with a training dataset. Execution of the instructions by the processor causes the controller to obtain pre-operative objective data for the patient, including one or more anatomic eye measurements. The controller is configured to obtain pre-operative questionnaire data for the patient, including at least one personality trait. The pre-operative objective data and the pre-operative questionnaire data are entered as respective inputs to the machine learning model. A predicted subjective outcome score for the patient is generated as an output of the machine learning model. The intraocular lens is selected based in part on the predicted subjective outcome score.

INTRODUCTION

The disclosure relates generally to a system and method of selecting an intraocular lens for implantation in an eye. The human lens is generally transparent such that light may travel through it with ease. However, many factors may cause areas in the lens to become cloudy and dense, and thus negatively impact vision quality. The situation may be remedied via a cataract procedure, whereby an artificial lens is selected for implantation into a patient's eye. Indeed, cataract surgery is commonly performed all around the world. With different types of intraocular lenses available today, it is not always clear what the best choice for a specific patient may be.

SUMMARY

Disclosed herein is a system and method for selecting an intraocular lens for implantation into an eye of a patient. The system includes a controller having a processor and a tangible, non-transitory memory on which instructions are recorded. The controller is configured to selectively execute at least one machine learning model (“at least one” omitted henceforth). The machine learning model is trained with a training dataset.

Execution of the instructions by the processor causes the controller to obtain pre-operative objective data for the patient, including one or more anatomic eye measurements. The controller is configured to obtain pre-operative questionnaire data for the patient, including at least one personality trait. The pre-operative objective data and the pre-operative questionnaire data are entered as respective inputs to the machine learning model. A predicted subjective outcome score for the patient is generated as an output of the machine learning model. The intraocular lens is selected based in part on the predicted subjective outcome score.

The machine learning model may include a neural network. The personality trait of the patient may be represented as at least one of a numerical scale of agreeability or as a binary result, the binary result being either predominantly agreeable or predominantly non-agreeable. The pre-operative questionnaire data may further include a lifestyle needs assessment for the patient.

An integrated diagnostic device may be configured to obtain the pre-operative objective data. The pre-operative objective data further includes refractive eye measurements and physiologic eye measurements. The training dataset includes respective historical sets composed of respective pre-operative objective data, respective pre-operative personality data, respective intra-operative data, respective post-operative objective data, and respective subjective outcome data. The system may include a data management module configured to collect the respective historical sets from a plurality of electronic medical record units and deliver the respective historical sets to the at least one machine learning module. The respective subjective outcome data in the respective historical sets may include a numerical satisfaction scale.

The controller may be configured to quantify a correlation of the respective post-operative objective data to the respective subjective outcome score in the respective historical sets and identify the respective post-operative objective data most strongly correlating with the respective subjective outcome score. The controller may be configured to identify and screen out the respective historical sets having at least one variable in the respective post-operative objective data matching with a predefined confounding parameter.

DETAILED DESCRIPTION

Referring to the drawings, wherein like reference numbers refer to like components,FIG. 1schematically illustrates a system10for selecting an intraocular lens L for implantation in a patient P. As described below, the system10leverages both objective and subjective data for optimizing the selection process. Examples of a first intraocular lens12and a second intraocular lens22are shown inFIG. 1. While the first intraocular lens12and the second intraocular lens22are multifocal lenses in the example shown, it is understood that any type of intraocular lens L available to those skilled in the art may be employed.

Referring toFIG. 1, the first intraocular lens12includes an optic zone14contiguous with one or more supporting structures16. The optic zone14may include an apodised diffractive multifocal zone18and an outer distance zone20, with the first intraocular lens12being configured to provide good vision over a broad range of distances. Referring toFIG. 1, the second intraocular lens22includes an optic zone24contiguous with one or more supporting structures26. The optic zone24may include an apodised diffractive multifocal zone28, an outer distance zone30and a center distance zone32. The second intraocular lens22may be configured to provide crisper distance vision and improved intermediate vision, compared to the first intraocular lens12.

Alternatively, the intraocular lens L may be a mono-focal lens. The intraocular lens L may be an accommodating lens with a fluid-filled internal cavity, the fluid being movable in order to vary a thickness (and power) of the intraocular lens L. It is to be understood that the intraocular lens L may take many different forms and include multiple and/or alternate components.

Referring toFIG. 1, the system10includes a controller C having at least one processor36and at least one memory38(or non-transitory, tangible computer readable storage medium) on which instructions are recorded for executing a method100for selecting an intraocular lens L for the patient P. Method100is shown in and described below with reference toFIG. 2.

Referring toFIG. 1, the controller C is specifically programmed to selectively execute one or more machine learning models40, such as first machine learning model42and second machine learning model44. The machine learning models40may be embedded in the controller C. The machine learning models40may be stored elsewhere and accessible to the controller C. The machine learning models40may be configured to find parameters, weights or a structure that minimizes a respective cost function.

Referring toFIG. 1, the machine learning models40are trained with one or more training datasets from a plurality of facilities50, such as first facility52, second facility54and third facility56, which may be clinical sites located all over the world. The controller C may be in communication with the plurality of facilities50via a first network58. The training dataset includes respective historical sets for a large number of patients. As described below, the respective historical set includes respective pre-operative objective data, respective pre-operative personality data, respective intra-operative data, respective post-operative objective data, and respective subjective outcome data for each patient. The training datasets may be stratified based on demographic data, patients with similar-sized dimensions of eyes or other health status factors. Each of the plurality of facilities50may include an integrated diagnostic device60configured to obtain the pre-operative objective data.

Referring toFIG. 1, the system10may include a data management module62having a computerized data management system able to store information from the respective electronic medical records of the plurality of facilities50. The data management module62is configured to collect the respective historical sets from the plurality of facilities50and provide them to the controller C. The data management module62may include a cloud unit64and/or a remote server66and be configured to share data across all clinical sites employing the system10. The cloud unit64may include one or more servers hosted on the Internet to store, manage, and process data. The remote server66may be a private or public source of information maintained by an organization, such as for example, a research institute, a company, a university and/or a hospital.

Referring toFIG. 1, the patient P may be associated with a clinic70. The controller C may be configured to receive and transmit communication with the clinic70through a user interface72. The user interface72may be installed on a smartphone, laptop, tablet, desktop or other electronic device that a care provider at the clinic70may operate, for example with a touch screen interface or I/O device such as a keyboard or mouse. The user interface72may be a mobile application. The circuitry and components of a mobile application (“apps”) available to those skilled in the art may be employed. The user interface72may include an integrated processor74and integrated memory76. The user interface72may in communication with the controller C via second network78such that it has access to the data in the controller C.

Referring toFIG. 1, the user interface72may include a plurality of modules, such as a first module80, second module82and third module84. In one example, the first module80and the second module82is configured to feed input factors (pre-operative objective data and pre-operative questionnaire data, respectively) into a common or different machine learning models40. In another example, the third module84is configured to obtain the output (predicted subjective outcome score) of the machine learning model40. The user interface72may include a database86for storing and comparing the outputs (predicted subjective outcome score) of different types of intraocular lenses L.

Referring toFIG. 1, the first network58and/or second network78may be a Wireless Local Area Network (LAN) which links multiple devices using a wireless distribution method, a Wireless Metropolitan Area Networks (MAN) which connects several wireless LANs or a Wireless Wide Area Network (WAN) which covers large areas such as neighboring towns and cities. Other types of connections may be employed. The first network58and/or second network78may be a bus implemented in various ways, such as for example, a serial communication bus in the form of a local area network. The local area network may include, but is not limited to, a Controller Area Network (CAN), a Controller Area Network with Flexible Data Rate (CAN-FD), Ethernet, blue tooth, WIFI and other forms of data connection.

Referring now toFIG. 2, a flow chart of method100executable by the controller C ofFIG. 1is shown. Method100need not be applied in the specific order recited herein and some blocks may be omitted. The memory M can store controller-executable instruction sets, and the processor P can execute the controller-executable instruction sets stored in the memory M.

Per block102ofFIG. 2, the controller C is configured to collect one or more training datasets, for example, from the remote server40via the long-range network44. The training dataset includes respective historical sets composed of respective pre-operative objective data (block102A), respective pre-operative personality data (block102B), respective intra-operative data (block102C), respective post-operative objective data (block102D), and respective subjective outcome data (block102E). In block104, the controller C is configured to calibrate or train the machine learning model40with the training dataset from block102.

For block102A, the respective pre-operative objective data may include anatomic eye measurements (e.g., eye length, corneal topography and thickness, lens position and thickness, etc.), refractive eye measurements (e.g., classical refraction, wavefront aberrometry) and physiologic eye measurements (e.g., intraocular pressure, tear film health, etc.). The respective pre-operative objective data (block102A) may include further visual function measurements (e.g., photopic/mesopic visual acuity, contrast sensitivity, near vision, etc.). Additionally, the controller C may be configured to identify and screen out the respective historical sets having at least one variable in the respective pre-operative objective data matching with a predefined confounding parameter. For example, if the confounding parameter is previous eye surgery, the corresponding data set may be left out of the training dataset.

For block102B, the respective pre-operative personality data may include a visual needs assessment or lifestyle demands (dominant activities, e.g., needlepoint versus fishing) and a personality trait. A standardized assessment may be used to obtain consistent data across patients and sites. In one example, the Big Five Factor model of personality type, sometimes known as McCrae and Costa, may be employed. The Big Five Factor model posits that the traits of openness, conscientiousness, extraversion, agreeableness and neuroticism (or emotional stability) form the basis of people's personalities (see McCrae, R., Costa, P.,Personality in Adulthood: A Five-Factor Theory Perspective, Guilford Press, New York City (2003). In one example, the personality trait may be represented as a numerical scale of agreeability, e.g., on a scale of 1 to 10, how agreeable is a person. In another example, the personality trait may be expressed as a binary result: either predominantly agreeable or predominantly non-agreeable.

For block102C, the respective intra-operative data (block102C) may include information related to the actual treatment performed. This information may be captured electronically and fed into the data management module62. Examples of intra-operative data include, but are not limited to, the type of refractive surgery procedure performed, the model of the implanted intraocular lens and its prescription. The intra-operative data may include intra-operative aberrometry measurements. The intra-operative data may further include the surgical machine settings and parameters of the procedure, such as procedure time, the temperature of the operating room, the total phaco power consumed to emulsify the original lens, the time duration that the phaco energy was applied, and the effective phaco time (as a product of phaco time multiplied by an average phaco power). The intra-operative data may further include: the type of delivery device used to implant the intraocular lens, the presence or absence of any occlusion breaks, the quantity and degree of the occlusion breaks, and whether or not assistive devices (such as capsular hooks) were employed. The intra-operative data may further include an intra-operative grade of nuclear hardness of the original lens, which may be graded according to a lens opacity classification.

For block102D, the respective post-operative objective data may include objective, measurable information obtained post-operatively for each patient in the training dataset. The respective post-operative objective data may include anatomic eye measurements (e.g., eye length, corneal topography and thickness, lens position and thickness, etc.), refractive eye measurements (e.g., classical refraction, wavefront aberrometry), physiologic eye measurements (e.g., intraocular pressure, tear film health, etc.) and visual function measurements (e.g., photopic/mesopic visual acuity, contrast sensitivity, near vision, etc.).

For block102E, the respective subjective outcome data in the respective historical sets may include one or more numerical satisfaction scale that reflects satisfaction with the post-operative visual outcome. The patient's satisfaction with their surgical outcome may be captured at one or more specific time periods (e.g. at 1 month and at 3 months post-surgery). In one example, a single overall satisfaction is employed, based on the following question: “on a scale of 1-5 (with 5 being best), how happy are you with your vision now?” In another example, separate satisfaction scales may be employed for near vision, far vision, night/dim light vision, “outdoor sports vision” (e.g. playing golf) and overall satisfaction.

The controller C may be configured to quantify correlation of the respective post-operative objective data to the respective subjective outcome score in the respective historical sets and identify the respective post-operative objective data most strongly correlating with the respective subjective outcome score. In other words, the system10will look at the objective post-operative measurements and assess how much those influence the questionnaire responses (respective subjective outcome data). This provides two technical advantages. First, this enables identification of objective post-operative measurements that drive patient satisfaction/dissatisfaction, and second, this enables screening of patients with confounding outcome parameters. For example, he controller C may be configured to screen out the respective historical set if the respective post-operative objective data exceeds a threshold, e.g., if the post-operative refraction was more than a half diopter from intended.

Referring now to block106ofFIG. 2, the controller C is configured to obtain pre-operative objective data for the patient P at the clinic70. The pre-operative objective data may include anatomic eye measurements (e.g., eye length, corneal topography and thickness, lens position and thickness, etc.), refractive eye measurements (e.g., classical refraction, wavefront aberrometry), physiologic eye measurements (e.g., intraocular pressure, tear film health, etc.) and visual function measurements (e.g., photopic/mesopic visual acuity, contrast sensitivity, near vision, etc.).

Per block108ofFIG. 2, the controller C is configured to obtain pre-operative questionnaire data for the patient, including at least one personality trait. As noted above, standardized methods may be used to assess the personality trait of the patient P. In one example, the personality trait of the patient P may be represented as a numerical scale of agreeability. In another example, the personality trait may be expressed as a binary result such that the patient P is either predominantly agreeable or predominantly non-agreeable. The pre-operative questionnaire data may further include a lifestyle needs assessment for the patient P.

Per block110ofFIG. 2, the method100includes entering the pre-operative objective data and the pre-operative questionnaire data as respective inputs to the machine learning model40and executing the machine learning model40. Per block112ofFIG. 2, the controller C is configured to generate a predicted subjective outcome score for the patient P for the first intraocular lens12as an output of the machine learning model40. Blocks102to112may be repeated to obtain a predicted subjective outcome score for the patient P for the second intraocular lens22and other types of intraocular lenses. Alternatively, the training datasets may encompass multiple types of intraocular lenses, with the type of intraocular lens incorporated as an element of block102C.

Per block114, the method100includes selecting the appropriate intraocular lens L based in part on a comparison of the predicted subjective outcome score for the first intraocular lens12, second intraocular lens22and other lenses. For example, if the predicted subjective outcome score is 85% for the first intraocular lens12, and 30% for the second intraocular lens22, a bilateral implantation of the first intraocular lens12may be optimal. If the predicted subjective outcome score is 55% for the first intraocular lens12, and 60% for the second intraocular lens22, the refractive outcome may be optimized with a “blended” solution, i.e., implanting the second intraocular lens22in the dominant eye and the first intraocular lens12in the non-dominant eye.

The system10may be configured to be “adaptive” and may be updated periodically after the collection of additional data for the training datasets. In other words, the machine learning models40may be configured to be “adaptive machine learning” algorithms that are not static and that improve after additional training datasets are collected. The machine learning models40ofFIG. 1may be configured to find parameters, weights or a structure that minimizes a respective cost function and may incorporate respective regression models. The machine learning models40ofFIG. 1may include a neural network, an example of which is shown inFIG. 3.

Referring toFIG. 3, the neural network200is a feedforward artificial neural network having at least three layers, including an input layer201, at least one hidden layer220and an output layer240. Each layer is composed of respective nodes N configured to perform an affine transformation of a linear sum of inputs. The respective nodes N are characterized by a respective bias and respective weighted links. The parameters of each respective node N may be independent of others, i.e., characterized by a unique set of weights. The input layer201may include first input node202, second input node204, third input node206, fourth input node208, fifth input node210and sixth input node212. The respective nodes N in the input layer201receive the input, normalize them and forward them to respective nodes N in the hidden layer220.

Referring toFIG. 3, the hidden layer220may include first hidden node222, second hidden node224, third hidden node226, fourth hidden node228and fifth hidden node230. Each respective node N in a subsequent layer computes a linear combination of the outputs of the previous layer. A network with three layers would form an activation function f(x)=f(3)(f(2)(f(1)(x))). The activation functionfmay be linear for the respective nodes N in the output layer240. The activation function f may be a sigmoid for the hidden layer220. A linear combination of sigmoids may be used to approximate a continuous function characterizing the output vector y. The patterns recognized by the neural network200may be translated or converted into numerical form and embedded in vectors or matrices.

The machine learning models40may employ deep learning maps to match an input vector x to an output vector y by learning an activation function f such that f(x) maps toy. A training process enables the machine learning models40to correlate the appropriate activation function f(x) for transforming the input vector x to the output vectory. For example, in the case of a simple linear regression model, two parameters are learned: a bias and a slope. The bias is the level of the output vector y when the input vector x is0and the slope is the rate of predicted increase or decrease in the output vectory for each unit increase in the input vector x. Once the machine learning models40are trained, estimated values of the output vectory may be computed with new values of the input vector x.

In summary, the system10and method100optimize the selection process for an intraocular lens L, utilizing parameters from both an objective and subjective standpoint. The system10and method100provide objective guidance to both clinicians and patients, based on the patient's detailed pre-operative information as well as a database of prior cases, incorporating both subjective and objective information as well as details of the surgical procedure applied in each case.

The controller C ofFIG. 1includes a computer-readable medium (also referred to as a processor-readable medium), including a non-transitory (e.g., tangible) medium that participates in providing data (e.g., instructions) that may be read by a computer (e.g., by a processor of a computer). Such a medium may take many forms, including, but not limited to, non-volatile media and volatile media. Non-volatile media may include, for example, optical or magnetic disks and other persistent memory. Volatile media may include, for example, dynamic random-access memory (DRAM), which may constitute a main memory. Such instructions may be transmitted by one or more transmission media, including coaxial cables, copper wire and fiber optics, including the wires that comprise a system bus coupled to a processor of a computer. Some forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, other magnetic medium, a CD-ROM, DVD, other optical medium, punch cards, paper tape, other physical medium with patterns of holes, a RAM, a PROM, an EPROM, a FLASH-EEPROM, other memory chip or cartridge, or other medium from which a computer can read.

Look-up tables, databases, data repositories or other data stores described herein may include various kinds of mechanisms for storing, accessing, and retrieving various kinds of data, including a hierarchical database, a set of files in a file system, an application database in a proprietary format, a relational database management system (RDBMS), etc. Each such data store may be included within a computing device employing a computer operating system such as one of those mentioned above and may be accessed via a network in one or more of a variety of manners. A file system may be accessible from a computer operating system and may include files stored in various formats. An RDBMS may employ the Structured Query Language (SQL) in addition to a language for creating, storing, editing, and executing stored procedures, such as the PL/SQL language mentioned above.

The detailed description and the drawings or FIGS. are supportive and descriptive of the disclosure, but the scope of the disclosure is defined solely by the claims. While some of the best modes and other embodiments for carrying out the claimed disclosure have been described in detail, various alternative designs and embodiments exist for practicing the disclosure defined in the appended claims.

Furthermore, the embodiments shown in the drawings or the characteristics of various embodiments mentioned in the present description are not necessarily to be understood as embodiments independent of each other. Rather, it is possible that each of the characteristics described in one of the examples of an embodiment can be combined with one or a plurality of other desired characteristics from other embodiments, resulting in other embodiments not described in words or by reference to the drawings. Accordingly, such other embodiments fall within the framework of the scope of the appended claims.