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Keratoconus Detection-based on Dynamic Corneal Deformation Videos Using Deep Learning.
Hazem Abdelmotaal1, Rossen Mihaylov Hazarbassanov2,3, Ramin Salouti4
1Department of Ophthalmology, Assiut University, Assuit, Egypt.
Ophthalmology Science
|October 23, 2023
Summary
Convolutional neural networks (CNNs) accurately detect keratoconus (KC) from corneal deformation videos. This deep learning approach shows high sensitivity and specificity, proving useful for clinical practice.
Area of Science:
- Ophthalmology
- Medical Imaging
- Artificial Intelligence
Background:
- Keratoconus (KC) is a progressive eye condition affecting corneal shape.
- Accurate detection of KC is crucial for timely intervention and management.
- Current diagnostic methods may have limitations in sensitivity or accessibility.
Purpose of the Study:
- To evaluate the performance of convolutional neural networks (CNNs) for automated keratoconus detection.
- To assess the utility of dynamic corneal deformation videos for KC diagnosis using AI.
Main Methods:
- A retrospective cohort study analyzed data from 734 participants.
- Dynamic corneal deformation videos were converted into 3D pseudoimages.
- A CNN was trained to identify KC directly from these pseudoimages.
Main Results:
- The CNN model achieved high accuracy (0.89) and AUC (0.94) on the test subset.
- External validation demonstrated strong performance with AUC of 0.93 and accuracy of 0.88.
- The model effectively differentiated between normal and keratoconic eyes.
Conclusions:
- Deep learning models can accurately detect keratoconus using dynamic corneal deformation videos.
- The developed approach demonstrates high sensitivity and specificity for clinical application.
- AI-powered analysis of corneal videos offers a promising tool for keratoconus diagnosis.
