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Related Experiment Video

Updated: Nov 15, 2025

Three Different Protocols of Corneal Collagen Crosslinking in Keratoconus: Conventional, Accelerated and Iontophoresis

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Published on: November 12, 2015

Predicting Keratoconus Progression and Need for Corneal Crosslinking Using Deep Learning.

Naoko Kato¹, Hiroki Masumoto², Mao Tanabe²

¹Department of Ophthalmology, School of Medicine, Keio University, Tokyo 160-8582, Japan.

Journal of Clinical Medicine

|March 6, 2021

Summary

Deep learning models can predict keratoconus progression using corneal tomography maps and patient age. This technology aids in identifying patients who may need corneal crosslinking (CXL) treatment.

Keywords:

corneal crosslinking deep learning keratoconus patients’ age prediction progression tomography

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Keratoconus is a progressive corneal ectasia.
Early prediction of keratoconus progression is crucial for timely intervention.
Corneal crosslinking (CXL) is a standard treatment to halt progression.

Purpose of the Study:

To develop and evaluate a deep learning (DL) model for predicting keratoconus progression.
To assess the need for corneal crosslinking (CXL) based on predictive modeling.
To determine the efficacy of corneal tomography maps and patient age in prediction.

Main Methods:

Utilized 274 corneal tomography images from 158 keratoconus patients (Pentacam HR®).
Trained a convolutional neural network (CNN) using axial maps, pachymetry maps, and their combination with patient age.
Classified patients into progression and non-progression groups based on serial examinations.

Main Results:

The combined map and age model achieved the highest AUC of 0.814 (95% CI: 0.755-0.872).
Sensitivity and specificity for the combined model were 77.8% and 69.6%, respectively.
The DL model demonstrated significant predictive capability for keratoconus progression.

Conclusions:

Deep learning models can effectively predict keratoconus progression using corneal tomography data.
Integrating patient age with imaging data enhances predictive accuracy.
This approach may assist in clinical decision-making for CXL treatment.