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Updated: Sep 14, 2025

$Author Spotlight: Advancements in Refractive Surgical Correction for Presbyopia and Exploring Postoperative Visual Acuity$

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Machine Learning Model for Predicting Visual Acuity Improvement After Intrastromal Corneal Ring Surgery in Patients

Eva Perez¹, Nassim Louissi¹, Sofiene Kallel¹

¹GRC 32, Transplantation et Thérapies Innovantes de La Cornée, TTIC, Hôpital National des 15-20, Sorbonne Université, Paris, France ; and.

|July 23, 2025

Summary

This summary is machine-generated.

Machine learning accurately predicts visual improvement after keratoconus (corneal disease) surgery. This AI tool helps select patients and forecast outcomes for intrastromal ring segment implantation, improving surgical decisions.

Keywords:

artificial intelligence corneal topography intrastromal corneal ring segments keratoconus machine learning

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Binocular Dynamic Visual Acuity in Eyeglass-Corrected Myopic Patients

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Published on: March 29, 2022

Area of Science:

Ophthalmology
Corneal Surgery
Artificial Intelligence in Medicine

Background:

Keratoconus is a progressive corneal disease causing vision loss.
Intrastromal ring segment implantation reshapes the cornea but has variable visual outcomes.
Predicting postoperative visual gain after surgery is challenging.

Purpose of the Study:

Investigate machine learning for predicting postoperative visual acuity in keratoconus patients.
Enhance surgical decision-making for intrastromal ring segment implantation.
Identify key predictors of visual improvement.

Main Methods:

Retrospective analysis of 120 eyes from 102 keratoconus patients.
Collected preoperative and postoperative refraction, corneal topography, and tomographic data.
Trained various machine learning models to predict visual acuity improvements.

Main Results:

XGBoost model achieved perfect prediction of visual improvement (R2 = 1.0, Youden Index = 1.0).
CatBoost model showed strong performance in predicting visual acuity (R2=0.59), keratometry (R2=0.76), and asphericity (R2=0.54).
Key predictors included preoperative keratometry, corneal asphericity, and visual acuity.

Conclusions:

Machine learning demonstrates significant potential for patient selection in keratoconus surgery.
AI models can accurately predict postoperative visual outcomes after ring segment implantation.
This approach can optimize surgical planning and patient management.