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Published on: April 1, 2019
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Dry Eye Subtype Classification Using Videokeratography and Deep Learning
Norihiko Yokoi1, Natsuki Kusada1, Hiroaki Kato1
1Department of Ophthalmology, Kyoto Prefectural University of Medicine, Kyoto 602-0841, Japan.
Diagnostics (Basel, Switzerland)
|January 11, 2024
Summary
AI-supported Tear Film Oriented Diagnosis (TFOD) offers a new non-invasive method for classifying dry eye subtypes. This approach uses videokeratography and deep learning, achieving high accuracy in identifying specific dry eye conditions.
Area of Science:
- Ophthalmology
- Medical Imaging
- Artificial Intelligence
Background:
- Dry eye (DE) diagnosis relies on methods like Tear Film Oriented Diagnosis (TFOD) using fluorescein staining.
- Accurate DE subtype classification is crucial for effective treatment.
Purpose of the Study:
- To introduce and evaluate AI-supported TFOD, a novel non-invasive method for DE subtype classification.
- To assess the performance of a deep learning model utilizing videokeratography and Blur Value (BV).
Main Methods:
- Employed videokeratography (VK) and a new indicator, Blur Value (BV), for DE analysis.
- Utilized a 3D convolutional neural network deep learning model for classification.
- Trained and tested the model on 243 eyes from diagnosed DE cases across four subtypes.
Main Results:
- Achieved an overall DE classification accuracy of 78.40%.
- Specific subtype accuracies included severe aqueous-deficient DE (sADDE) at 92.3%, mild-to-moderate ADDE (m/mADDE) at 79.3%, decreased wettability DE (DWDE) at 75.8%, and increased evaporation DE (IEDE) at 72.7%.
Conclusions:
- AI-supported TFOD presents a promising non-invasive tool for accurate dry eye subtype classification.
- This AI-driven approach may enhance clinical diagnosis and management of dry eye disease.
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