ArticleScientific reports2023
Artificial intelligence to estimate the tear film breakup time and diagnose dry eye disease.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Benchmarking Statistical, Machine Learning, and Exploratory Deep Learning Models for the Short-Term Forecasting of Monthly Aggregated Adult Ocular Surface Indicators: An Exploratory Hospital-Level Study.Diagnostics (Basel, Switzerland) · 2026Article
- Dry eye detection based on multidimensional temporal features of fluorescein tear film videos.Quantitative imaging in medicine and surgery · 2026Article
- Dry Eye Disease: From Mechanisms to Management and Future Directions.Journal of clinical medicine · 2026Review
- Smartphone-Based Portable Slit Lamp in Anterior Segment Diseases: A Narrative Review of Clinical Assessment and Integration with Artificial Intelligence.Ophthalmology and therapy · 2026Review
- Artificial Intelligence for Diagnostic Guidance in Ocular Surface Disorders.Journal of clinical medicine · 2026Review
- Application of a deep learning-based system for eyelid margin signs identification training and testing in dry eye disease education.Frontiers in medicine · 2026Article
- Validation of an Artificial Intelligence-Based Anterior Chamber Depth Estimation Using a Smartphone-Compatible Slit Lamp Device.Ophthalmology science · 2026Article
- Artificial Intelligence Application in Cornea and External Diseases.Diagnostics (Basel, Switzerland) · 2025Review
- A Comprehensive Review Comparing Artificial Intelligence and Clinical Diagnostic Approaches for Dry Eye Disease.Diagnostics (Basel, Switzerland) · 2025Review
- Development and multicenter validation of an AI driven model for quantitative meibomian gland evaluation.NPJ digital medicine · 2025Article
- Risk of Permanent Corneal Injury in Microgravity: Spaceflight-Associated Hazards, Challenges to Vision Restoration, and Role of Biotechnology in Long-Term Planetary Missions.Life (Basel, Switzerland) · 2025Review
- Advances in AI-assisted quantification of dry eye indicators.Frontiers in medicine · 2025Review
- Artificial Intelligence in Optometry: Current and Future Perspectives.Clinical optometry · 2025Review
- Artificial intelligence in corneal diseases: A narrative review.Contact lens & anterior eye : the journal of the British Contact Lens Association · 2024Review
- The Use of Artificial Intelligence for Estimating Anterior Chamber Depth from Slit-Lamp Images Developed Using Anterior-Segment Optical Coherence Tomography.Bioengineering (Basel, Switzerland) · 2024Article
- Review
- A Case of Traumatic Hyphema Diagnoses by Telemedicine Between a Remote Island and the Mainland of Tokyo.Cureus · 2024Article
- Automated tear film break-up time measurement for dry eye diagnosis using deep learning.Scientific reports · 2024Article
- Development of the AI Pipeline for Corneal Opacity Detection.Bioengineering (Basel, Switzerland) · 2024Article
- Potential applications of artificial intelligence in image analysis in cornea diseases: a review.Eye and vision (London, England) · 2024Review
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Authors and funding
15 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The use of artificial intelligence (AI) in the diagnosis of dry eye disease (DED) remains limited due to the lack of standardized image formats and analysis models. To overcome these issues, we used the Smart Eye Camera (SEC), a video-recordable slit-lamp device, and collected videos of the anterior segment of the eye. This study aimed to evaluate the accuracy of the AI algorithm in estimating the tear film breakup time and apply this model for the diagnosis of DED according to the Asia Dry Eye Society (ADES) DED diagnostic criteria. Using the retrospectively corrected DED videos of 158 eyes from 79 patients, 22,172 frames were annotated by the DED specialist to label whether or not the frame had breakup. The AI algorithm was developed using the training dataset and machine learning. The DED criteria of the ADES was used to determine the diagnostic performance. The accuracy of tear film breakup time estimation was 0.789 (95% confidence interval (CI) 0.769-0.809), and the area under the receiver operating characteristic curve of this AI model was 0.877 (95% CI 0.861-0.893). The sensitivity and specificity of this AI model for the diagnosis of DED was 0.778 (95% CI 0.572-0.912) and 0.857 (95% CI 0.564-0.866), respectively. We successfully developed a novel AI-based diagnostic model for DED. Our diagnostic model has the potential to enable ophthalmology examination outside hospitals and clinics.
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