Supervised Machine Learning Models for Ocular Sagittal Height Prediction Incorporating Corneoscleral Profile Data.

Area of Science:

• Ophthalmology
• Biomedical Engineering
• Data Science

Background:

• Accurate prediction of ocular sagittal height (OC-SAG) is crucial for various ophthalmic applications, including contact lens fitting.
• Traditional methods may lack precision, necessitating advanced predictive tools.

Purpose of the Study:

• To develop and evaluate supervised machine learning (ML) models for predicting OC-SAG.
• To assess the utility of anterior eye topography and tomography data in these predictions.

Main Methods:

• Retrospective analysis of 100 eyes using anterior segment optical coherence tomography (CASIA 2).
• Development of four supervised ML models (including Random Forest) to predict OC-SAG at 10 mm and 14 mm chord lengths.
• Comparison of models using topographical data alone versus combined topographical and tomographical data.

Main Results:

• The Random Forest model demonstrated the highest predictive accuracy for OC-SAG at both 10 mm (r=0.88) and 14 mm (r=0.77) chords using topography data.
• Inclusion of tomographical data, such as corneoscleral junction (CSJ) metrics, did not significantly enhance model performance.
• Mean absolute errors were clinically acceptable for soft contact lens fitting, especially at shorter chord lengths.

Conclusions:

• Supervised ML models, particularly Random Forest, effectively predict OC-SAG using anterior eye topography.
• Tomographical data did not provide significant additional predictive value over topographical data alone.
• These ML models offer a promising, accessible method for clinicians to estimate OC-SAG and aid in decision-making for contact lens fitting.