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Establishment of A Mouse Model of Aqueous Deficiency Dry Eye

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Detecting dry eye from ocular surface videos based on deep learning.

Hazem Abdelmotaal¹, Rossen Hazarbasanov², Suphi Taneri³

¹Department of Ophthalmology, Assiut University, Assuit, Egypt.

The Ocular Surface

|January 28, 2023

Summary

Convolutional neural networks (CNNs) accurately diagnose dry eye (DE) from ocular surface videos. This deep learning approach shows promise for clinical application in diagnosing DE.

Keywords:

Convolutional neural networks Corneal video-topography Deep learning Dry eye disease Machine learning Ocular surface Video classification

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Area of Science:

Ophthalmology
Artificial Intelligence
Medical Imaging

Background:

Dry eye (DE) diagnosis can be challenging.
Automated diagnostic tools are needed to improve efficiency and accuracy.
Video keratoscopy provides ocular surface video data for analysis.

Purpose of the Study:

To evaluate the performance of convolutional neural networks (CNNs) for automated dry eye (DE) diagnosis.
To assess the utility of single ocular surface video frames in DE detection.
To determine the clinical relevance of CNN-based DE diagnosis.

Main Methods:

A retrospective cohort study included 244 ocular surface videos (116 normal, 128 with DE).
A deep transfer learning model (CNN) was developed to identify DE from video frames.
Model performance was evaluated using accuracy metrics and class activation maps.

Main Results:

The CNN model achieved an Area Under the Curve (AUC) of 0.98 for discriminating between normal eyes and eyes with DE.
Network activation maps indicated the lower paracentral cornea as a key region for DE detection.
The model demonstrated high diagnostic accuracy, specificity, and sensitivity.

Conclusions:

Deep transfer learning effectively diagnoses dry eye (DE) using non-invasive ocular surface videos.
The high diagnostic accuracy suggests potential clinical utility for this automated method.
CNNs offer a promising tool for objective and efficient DE assessment.