EP-4738378-A2 - SYSTEM AND METHOD FOR SELECTION OF A PREFERRED INTRAOCULAR LENS

EP4738378A2EP 4738378 A2EP4738378 A2EP 4738378A2EP-4738378-A2

Abstract

A system for selecting a preferred intraocular lens for implantation into an eye includes a controller having a processor and a tangible, non-transitory memory. The controller is configured to obtain diagnostic data of the eye, and obtain historical data composed of historical sets of patient data. The controller is configured to analyze individual risk factors based on the diagnostic data and obtain a weighted combination of the individual risk factors. A respective satisfaction metric for the plurality of intraocular lenses is generated based on the historical data. A preferred intraocular lens may be selected based in part on the respective satisfaction metric and the weighted combination. A visual simulation for each of the plurality of intraocular lenses may be performed, based in part on the diagnostic data. The visual simulation may incorporate an impact of the tear film data.

Inventors

CAMPIN, JOHN ALFRED
GRUENDIG, MARTIN
HERNANDEZ, Victor Manuel
PETTIT, George Hunter
ZIELKE, MARK ANDREW
NEKRASSOV, Daniil

Assignees

Alcon Inc.

Dates

Publication Date: 20260506
Application Date: 20220630

Claims (15)

A system for selecting a preferred intraocular lens, from a plurality of intraocular lenses, for implantation into an eye of a patient, the system comprising: a controller having a processor and a tangible, non-transitory memory on which instructions are recorded, execution of the instructions causing the controller to: obtain diagnostic data of the eye; obtain historical data composed of respective historical sets of patient data; analyze individual risk factors based on the diagnostic data and obtain a weighted combination of the individual risk factors based in part on the historical data; and generate a respective satisfaction metric for the plurality of intraocular lenses based in part the historical data.
The system of claim 1, wherein the controller is configured to: select the preferred intraocular lens based on the respective satisfaction metric and the weighted combination of the individual risk factors.
The system of claim 1, wherein the controller is configured to: perform a visual simulation for each of the plurality of intraocular lenses based in part on the diagnostic data, the respective satisfaction metric being based in part on the visual simulation.
The system of claim 3, wherein: the diagnostic data includes tear film data, the visual simulation incorporating an impact of the tear film data, including: detecting a respective location where the tear film data exhibits at least one of a change in a signal-to-noise ratio and a relatively lower signal-to-noise ratio than that of surrounding locations; and identifying the respective location as a respective irregularity of a tear film in the eye.
The system of claim 3, wherein: the diagnostic data includes tear film data, the visual simulation incorporating an impact of the tear film data, including: identifying a respective location where the tear film data exhibits at least one of missing information and a varying point distribution; and identifying the respective location as a respective irregularity of a tear film.
The system of claim 1, wherein: the diagnostic data include corneal data represented as at least one of a binary result or as a numerical scale of irregular corneal aberrations, the eye being scanned to generate the diagnostic data; and the binary result is either a presence of a threshold level of corneal aberrations or an absence of the threshold level of corneal aberrations.
The system of claim 1, wherein: the diagnostic data include macular data represented as at least one of a binary result or as a numerical scale of macular degeneration the eye being scanned to generate the diagnostic data; and the binary result is either a presence of a threshold level of degeneration or an absence of the threshold level of degeneration.
The system of claim 1, wherein the diagnostic data include: a respective location, orientation, and size of a pupil of the eye in a three-dimensional coordinate system, the pupil being under photopic conditions; and the respective location and respective profile of an anterior corneal surface and a posterior corneal surface of the eye.
The system of claim 1, wherein: the diagnostic data includes lens capsule stability data represented by one or more wobble parameters, obtaining the lens capsule stability data including: acquiring a plurality of images of the eye while presenting different accommodative demands to the eye; and generating a motion trace of a lens capsule of the eye using the plurality of images.
The system of claim 9, wherein obtaining the lens capsule stability data further includes: extracting normalized lens oscillation traces based on the motion trace; model-fitting a curve to the normalized lens oscillation traces; and obtaining the one or more wobble parameters as a maximum amplitude and/or a time constant of the curve.
The system of claim 1, wherein: the diagnostic data includes at least one of the following: (i) lens capsule stability data represented by one or more wobble parameters, obtaining the lens capsule stability data including: directing electromagnetic energy in a predetermined spectrum onto the eye concurrently with induced eye saccades, via an energy source; acquiring a plurality of images of the eye indicative of the induced eye saccades, via a camera; generating a motion trace of a lens capsule using the plurality of images and extracting normalized lens oscillation traces based on the motion trace; model-fitting a curve to the normalized lens oscillation traces; and obtaining the one or more wobble parameters based on the curve. (ii) the diagnostic data includes an angle kappa factor; (iii) the diagnostic data includes questionnaire data for the patient with at least one personality trait, the at least one personality trait being 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 system of claim 1, wherein: determining the respective satisfaction metric includes selectively executing at least one machine learning model trained with the respective historical sets; and the respective historical sets include pre-operative objective data, pre-operative personality data, intra-operative data, post-operative objective data, and subjective outcome data, and optionally wherein the subjective outcome data in the respective historical sets include a numerical satisfaction scale.
The system of claim 12, wherein: the controller is configured to quantify a correlation of the post-operative objective data to the subjective outcome score in the respective historical sets and identify the post-operative objective data most strongly correlating with the subjective outcome score.
A system for selecting a preferred intraocular lens for implantation into an eye, the system comprising: a controller having a processor and a tangible, non-transitory memory on which instructions are recorded, execution of the instructions causing the controller to: the diagnostic data of the eye, including tear film data, the eye being scanned to generate the diagnostic data; perform a visual simulation for each of the plurality of intraocular lenses based in part on the diagnostic data, the visual simulation incorporating an impact of the tear film data; obtain historical data composed of respective historical sets of patient data; and generate a respective satisfaction metric for the plurality of intraocular lenses based in part on the visual simulation and the historical data.
The system of claim 14, further comprising: detecting a respective location where the tear film data exhibits at least one of a change in a signal-to-noise ratio and a relatively lower signal-to-noise ratio than that of surrounding locations; and identifying the respective location as a respective irregularity of a tear film in the eye;

Description

INTRODUCTION The disclosure relates generally to a system for selecting a preferred 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. Traditionally, cataract patients who underwent surgery received an artificial lens designed to enhance distance vision only. Many patients suffered from varying levels of post-surgical presbyopia, which required the use of reading glasses or bifocals. Today different types of advanced technology intraocular lenses, such as multifocal intraocular lenses, are available for correcting a number of vision variances. Current screening methods for advanced technology intraocular lenses require in-depth expertise of the surgeon and are time consuming to carry out. As a result, surgeons may be hesitant to prescribe advanced technology intraocular lenses. SUMMARY Disclosed herein is a system for selecting a preferred intraocular lens for implantation into an eye. The system includes a controller having a processor and a tangible, non-transitory memory on which instructions are recorded. The controller is configured to obtain diagnostic data of the eye. The controller is configured to obtain historical data composed of historical sets of patient data. The controller is configured to analyze individual risk factors based on the diagnostic data and obtain a weighted combination of the individual risk factors. A respective satisfaction metric for the plurality of intraocular lenses is generated based on the historical data. The controller is configured to select the preferred intraocular lens based in part on the respective satisfaction metric and the weighted combination. A visual simulation for each of the plurality of intraocular lenses may be performed, based in part on the diagnostic data. The respective satisfaction metric may be based in part on the visual simulation. The diagnostic data may include tear film data. The visual simulation may incorporate an impact of the tear film data, including detecting a respective location where the tear film data exhibits at least one of a change in a signal-to-noise ratio and a relatively lower signal-to-noise ratio than that of surrounding locations, and identifying the respective location as a respective irregularity of a tear film in the eye. Incorporating the impact of the tear film data may include identifying a respective location where the tear film data exhibits at least one of missing information and a varying point distribution, and identifying the respective location as a respective irregularity of a tear film. The diagnostic data may include corneal data represented as at least one of a binary result or as a numerical scale of irregular corneal aberrations, the eye being scanned to generate the diagnostic data. The binary result may be either a presence of a threshold level of corneal aberrations or an absence of the threshold level of corneal aberrations. The diagnostic data may include macular data represented as at least one of a binary result or as a numerical scale of macular degeneration the eye being scanned to generate the diagnostic data. The binary result may be either a presence of a threshold level of degeneration or an absence of the threshold level of degeneration. The diagnostic data may include a respective location, orientation, and size of a pupil of the eye in a three-dimensional coordinate system, the pupil being under photopic conditions, and the respective location and respective profile of an anterior corneal surface and a posterior corneal surface of the eye. The diagnostic data may include lens capsule stability data represented by one or more wobble parameters. Obtaining the lens capsule stability data may include acquiring a plurality of images of the eye while presenting different accommodative demands to the eye and generating a motion trace of a lens capsule of the eye using the plurality of images. Obtaining the lens capsule stability data may further include extracting normalized lens oscillation traces based on the motion trace, model-fitting a curve to the normalized lens oscillation traces and obtaining the one or more wobble parameters as a maximum amplitude and/or a time constant of the curve. Obtaining the lens capsule stability data may include directing electromagnetic energy in a predetermined spectrum onto the eye concurrently with induced eye saccades, via an energy source, and acquiring a plurality of images of the eye indicative of the induced eye saccades, via a camera. Obtaining the lens capsule stability data may further include generating a motion trace of a lens capsule using the plu