公开(公告)号：US20240188819A1

公开(公告)日：2024-06-13

申请号：US18444164

申请日：2024-02-16

Applicant: Alcon Inc.

Inventor： Ramesh Sarangapani ,  Mark VonTress

IPC: A61B3/10 ,  A61B3/00 ,  G06N3/08

CPC classification number: A61B3/1015 ,  A61B3/0025 ,  A61B3/0033 ,  G06N3/08

Abstract: A system and method of assessing vision quality of an eye is presented, with a controller having a processor and tangible, non-transitory memory on which instructions are recorded. The controller is configured to selectively execute at least one machine learning model. Execution of the instructions by the processor causes the controller to: receive wavefront aberration data of the eye and express the wavefront aberration data as a collection of Zernike polynomials. The controller is configured to obtain a plurality of input factors based on the collection of Zernike polynomials. The plurality of input factors is fed into the at least one machine learning model, which is trained to analyze the plurality of input factors. The machine learning model generates at least one vision correction factor based in part on the plurality of input factors.

公开(公告)号：US11931104B2

公开(公告)日：2024-03-19

申请号：US17127693

申请日：2020-12-18

Applicant: Alcon Inc.

Inventor： Ramesh Sarangapani ,  Mark VonTress

IPC: A61B3/10 ,  A61B3/00 ,  A61B3/02 ,  G06N3/08

CPC classification number: A61B3/1015 ,  A61B3/0025 ,  A61B3/0033 ,  G06N3/08

Abstract: A system and method of assessing vision quality of an eye is presented, with a controller having a processor and tangible, non-transitory memory on which instructions are recorded. The controller is configured to selectively execute at least one machine learning model. Execution of the instructions by the processor causes the controller to: receive wavefront aberration data of the eye and express the wavefront aberration data as a collection of Zernike polynomials. The controller is configured to obtain a plurality of input factors based on the collection of Zernike polynomials. The plurality of input factors is fed into the at least one machine learning model, which is trained to analyze the plurality of input factors. The machine learning model generates at least one vision correction factor based in part on the plurality of input factors.

公开(公告)号：US20210186323A1

公开(公告)日：2021-06-24

申请号：US17127693

申请日：2020-12-18

Applicant: Alcon Inc.

Inventor： Ramesh Sarangapani ,  Mark VonTress

IPC: A61B3/10 ,  A61B3/00 ,  G06N3/08

Abstract: A system and method of assessing vision quality of an eye is presented, with a controller having a processor and tangible, non-transitory memory on which instructions are recorded. The controller is configured to selectively execute at least one machine learning model. Execution of the instructions by the processor causes the controller to: receive wavefront aberration data of the eye and express the wavefront aberration data as a collection of Zernike polynomials. The controller is configured to obtain a plurality of input factors based on the collection of Zernike polynomials. The plurality of input factors is fed into the at least one machine learning model, which is trained to analyze the plurality of input factors. The machine learning model generates at least one vision correction factor based in part on the plurality of input factors.

公开(公告)号：US20200229870A1

公开(公告)日：2020-07-23

申请号：US16746231

申请日：2020-01-17

Applicant: ALCON INC.

Inventor： Ramesh Sarangapani ,  Mark VonTress

IPC: A61B34/10 ,  A61F2/16 ,  A61B3/117 ,  G16H50/50 ,  G16H50/20

Abstract: Systems and methods for intraocular lens (IOL) selection using emmetropia zone prediction include determining pre-operative measurements of an eye, estimating a post-operative anterior chamber depth (ACD) of an intraocular lens based on the pre-operative measurements, estimating a post-operative manifest refraction in spherical equivalent (MRSE) of the eye with the IOL implanted based on the pre-operative measurements and the estimated post-operative ACD, determining whether the eye with the IOL implanted is likely to be in an emmetropia zone based on the estimated post-operative MRSE, re-estimating the post-operative MRSE of the eye with the IOL implanted using an emmetropia zone prediction model or a non-emmetropia zone prediction model based on the emmetropia zone determining, and providing the re-estimated post-operative MRSE to a user to aid in selection of an IOL for implantation in the eye.