Selection of intraocular lens based on a predicted subjective outcome score

Patent metadata

Field	Value
Publication number	US-2025281039-A1
Application number	US-202519219064-A
Country	US
Kind code	A1
Filing date	May 27, 2025
Priority date	May 29, 2020
Publication date	Sep 11, 2025
Grant date	—

Abstract

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.

First claim

What is claimed is:

1 . A system for selecting an intraocular lens 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; wherein the controller is configured to selectively execute at least one machine learning model, the at least one machine learning model being trained with a training dataset; wherein 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;
obtain pre-operative questionnaire data for the patient, including at least one personality trait;
enter the pre-operative objective data and the pre-operative questionnaire data as respective inputs to the at least one machine learning model and generate a predicted subjective outcome score for the patient as an output of the at least one machine learning model; and
select the intraocular lens based in part on the predicted subjective outcome score.

2 . The system of claim 1 , wherein:
the at least one machine learning model includes a neural network.

3 . The system of claim 1 , wherein:
the at least one personality trait of the patient is 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.

4 . The system of claim 1 , wherein:
the pre-operative questionnaire data further includes a lifestyle needs assessment for the patient.

5 . The system of claim 1 , further comprising:
an integrated diagnostic device configured to obtain the pre-operative objective data; and wherein the pre-operative objective data further includes refractive eye measurements and physiologic eye measurements.

6 . The system of claim 1 , wherein:
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.

7 . The system of claim 6 , further comprising:
a data management module accessible to the controller, the data management module being configured to collect the respective historical sets from a plurality of electronic medical record units.

8 . The system of claim 6 , wherein:
the respective subjective outcome data in the respective historical sets includes a numerical satisfaction scale.

9 . The system of claim 6 , wherein the controller is 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.

10 . The system of claim 6 , wherein the controller is 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.

11 . A method of selecting an intraocular lens for implantation in an eye, the method comprising:
receiving, via the controller, pre-operative objective data for the patient, including one or more anatomic eye measurements; receiving, via the controller, pre-operative questionnaire data for the patient, including at least one personality trait; executing the at least one machine learning model with the pre-operative objective data and the pre-operative questionnaire data as respective inputs, via the controller, the at least one machine learning model being trained with a training dataset; generating, as an output of the at least one machine learning model, a predicted subjective outcome score for the patient; and selecting the intraocular lens based in part on the predicted subjective outcome score.

12 . The method of claim 11 , wherein:
representing the at least one personality trait of the patient 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.

13 . The method of claim 11 , wherein:
the pre-operative questionnaire data further includes a lifestyle needs assessment for the patient.

14 . The method of claim 11 , further comprising:
an integrated diagnostic device configured to obtain the pre-operative objective data; and wherein the pre-operative objective data further includes refractive eye measurements and physiologic eye measurements.

15 . The method of claim 11 , wherein:
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.

16 . The method of claim 16 , further comprising:
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.

17 . The method of claim 16 , wherein:
the respective subjective outcome data in the respective historical sets includes a numerical satisfaction scale.

18 . The method of claim 16 , further comprising:
assessing a respective correlation of the respective post-operative objective data to the respective subjective outcome score in the respective historical sets, via the controller; and identifying the respective post-operative objective data most strongly correlating with the respective subjective outcome score, via the controller.

Assignees

• Alcon Inc

Inventors

• Campin John Alfred
• Gillen Brant
• Gründig Martin
• Moore Robert
• Pettit George Hunter
• Zielke Mark Andrew

Classifications

• G06N3/09

  Supervised learning · CPC title

• G06N3/0499

  Feedforward networks · CPC title

• A61B2034/108

  Computer aided selection or customisation of medical implants or cutting guides · CPC title

• G16H50/20

  for computer-aided diagnosis, e.g. based on medical expert systems · CPC title

• A61B3/0025 Primary

  characterised by electronic signal processing, e.g. eye models · CPC title