ArticleDiagnostics (Basel, Switzerland)2023
Dry Eye Subtype Classification Using Videokeratography and Deep Learning.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Artificial Intelligence for Diagnostic Guidance in Ocular Surface Disorders.Journal of clinical medicine · 2026Review
- Emerging Technologies in Corneal Nerve Evaluation for Dry Eye and Ocular Surface Disease: A Review.Journal of clinical medicine · 2026Review
- A Comprehensive Review Comparing Artificial Intelligence and Clinical Diagnostic Approaches for Dry Eye Disease.Diagnostics (Basel, Switzerland) · 2025Review
- An efficient enhanced stacked auto encoder assisted optimized deep neural network for forecasting Dry Eye Disease.Scientific reports · 2024Article
- Artificial Intelligence in Clinical Diagnosis and Treatment of Dry Eye: Workflows, Effectiveness, and Evaluation.Journal of current ophthalmologyReview
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Authors and funding
6 authors.
Funding
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Abstract
We previously reported on 'Tear Film Oriented Diagnosis' (TFOD), a method for the dry eye (DE) subtype classification using fluorescein staining and an examination of fluorescein breakup patterns via slit-lamp biomicroscopy. Here, we report 'AI-supported TFOD', a novel non-invasive method for DE subtype classification using videokeratography (VK) and "Blur Value" (BV), a new VK indicator of the extent of blur in Meyer-ring images and deep learning (DL). This study involved 243 eyes of 243 DE cases (23 males and 220 females; mean age: 64.4 ± 13.9 (SD) years)-i.e., 31 severe aqueous-deficient DE (sADDE) cases, 73 mild-to-moderate ADDE (m/mADDE) cases, 84 decreased wettability DE (DWDE) cases, and 55 increased evaporation DE (IEDE) cases diagnosed via the fluorescein-supported TFOD pathway. For DL, a 3D convolutional neural network classification model was used (i.e., the original image and BV data of eyes kept open for 7 s were randomly divided into training data (146 cases) and the test data (97 cases), with the training data increased via data augmentation and corresponding to 2628 cases). Overall, the DE classification accuracy was 78.40%, and the accuracies for the subtypes sADDE, m/mADDE, DWDE, and IEDE were 92.3%, 79.3%, 75.8%, and 72.7%, respectively. 'AI-supported TFOD' may become a useful tool for DE subtype classification.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.