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Optimizing Predictive Models for Distance and Near Vision Outcomes in Presbyopic Surgery Using Machine Learning.

Soodabeh Darzi¹, Pierre Baudu², Franck Penin³

¹SCHWIND eye-tech-solutions, Kleinostheim, Germany.

Translational Vision Science & Technology

|October 24, 2025

Summary

This summary is machine-generated.

Machine learning models predict visual outcomes after presbyopic corneal refractive surgery. The distance-corrected visual gain (DCVG) metric helps balance near and distance vision for personalized surgical planning.

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Area of Science:

Ophthalmology
Data Science
Biomedical Engineering

Background:

Presbyopic corneal refractive surgery aims to restore both near and distance vision.
Predicting visual outcomes remains challenging due to inherent trade-offs.
Personalized surgical planning is crucial for optimizing patient satisfaction.

Purpose of the Study:

Develop predictive models for postoperative visual acuity (near, distance, monocular, binocular).
Create a composite outcome metric, distance-corrected visual gain (DCVG).
Identify key factors influencing outcomes in presbyopic corneal refractive surgery.

Main Methods:

Retrospective analysis of 914 eyes from 457 patients treated with PresbyMAX.
Application of machine learning algorithms (Random Forest) for predicting visual acuity.
Evaluation of feature importance using SHAP and PDPs.

Main Results:

Binocular predictive models showed higher accuracy than monocular models.
Corrected distance visual acuity (CDVA) was more predictable than distance-corrected near visual acuity (DCNVA).
Optimal planned addition for balancing vision was +1.50 D to +2.25 D; patients aged 45-56 had better near vision potential.

Conclusions:

Predictive modeling and DCVG metric enable personalized surgical planning.
This approach aids in managing visual trade-offs in presbyopic surgery.
Machine learning facilitates individualized decision-making for better patient counseling and optimized visual outcomes.