ArticleThe British journal of ophthalmology2024
Deep learning model for extensive smartphone-based diagnosis and triage of cataracts and multiple corneal diseases.
Article in The British journal of ophthalmology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.
- Image-Based Deep Learning for Cataract Diagnosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Beyond the Barrier: Overcoming Ocular Antimicrobial Resistance Through AI and Novel Therapeutics.International journal of molecular sciences · 2026Review
- YOLOv5 Attention Analysis for Anterior Eye Disease Classification: Grad-CAM++ Feature Importance and Cut-and-Paste Validation.Journal of imaging informatics in medicine · 2026Article
- AI for Anterior Segment Disease Using Transfer Learning: Adapting Slit-Lamp-Trained Model for Analysis of Smartphone Corneal Images.Translational vision science & technology · 2026Article
- Deep learning approach for analyzing regional scheimpflug corneal images to assess keratoconus progression.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
- Artificial Intelligence in Ophthalmology: Current Status, Challenges, and Future Perspectives.Health care science · 2026Review
- Smartphone-based lightweight AI system for real-time multiple anterior segment disease screening: development and real-world validation.BMC medicine · 2026Article
- Artificial Intelligence in Ocular Surface Tumors: Current Advances, Challenges, and Future Directions.Diagnostics (Basel, Switzerland) · 2026Review
- Validated semi-supervised early and accurate screening for anterior segment diseases: a 3PM-guided conceptual and technological innovation.The EPMA journal · 2026Article
- 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
- Neural network for natural language processing to determine treatment urgency in an ophthalmology emergency department.The British journal of ophthalmology · 2025Article
- Smartphone-based biosensing: a review of optical imaging, microfluidic integration, and AI-enhanced analysis.Mikrochimica acta · 2025Review
- Reducing Hallucinations and Trade-Offs in Responses in Generative AI Chatbots for Cancer Information: Development and Evaluation Study.JMIR cancer · 2025Article
- Clinical Applications of Artificial Intelligence in Corneal Diseases.Vision (Basel, Switzerland) · 2025Review
- MCOA: A Comprehensive Multimodal Dataset for Advancing Deep Learning in Corneal Opacity Assessment.Scientific data · 2025Article
- Artificial intelligence support improves diagnosis accuracy in anterior segment eye diseases.Scientific reports · 2025Article
- Artificial Intelligence Applications in Ophthalmology.JMA journal · 2025Review
- The importance of clinical experience in AI-assisted corneal diagnosis: verification using intentional AI misleading.Scientific reports · 2025Article
- Knowledge-enhanced AI drives diagnosis of multiple retinal diseases in fundus fluorescein angiography.Frontiers in cell and developmental biology · 2025Article
Corrections and comments
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Authors and funding
15 authors at 11 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimTo develop an artificial intelligence (AI) algorithm that diagnoses cataracts/corneal diseases from multiple conditions using smartphone images.
methodsThis study included 6442 images that were captured using a slit-lamp microscope (6106 images) and smartphone (336 images). An AI algorithm was developed based on slit-lamp images to differentiate 36 major diseases (cataracts and corneal diseases) into 9 categories. To validate the AI model, smartphone images were used for the testing dataset. We evaluated AI performance that included sensitivity, specificity and receiver operating characteristic (ROC) curve for the diagnosis and triage of the diseases.
resultsThe AI algorithm achieved an area under the ROC curve of 0.998 (95% CI, 0.992 to 0.999) for normal eyes, 0.986 (95% CI, 0.978 to 0.997) for infectious keratitis, 0.960 (95% CI, 0.925 to 0.994) for immunological keratitis, 0.987 (95% CI, 0.978 to 0.996) for cornea scars, 0.997 (95% CI, 0.992 to 1.000) for ocular surface tumours, 0.993 (95% CI, 0.984 to 1.000) for corneal deposits, 1.000 (95% CI, 1.000 to 1.000) for acute angle-closure glaucoma, 0.992 (95% CI, 0.985 to 0.999) for cataracts and 0.993 (95% CI, 0.985 to 1.000) for bullous keratopathy. The triage of referral suggestion using the smartphone images exhibited high performance, in which the sensitivity and specificity were 1.00 (95% CI, 0.478 to 1.00) and 1.00 (95% CI, 0.976 to 1.000) for 'urgent', 0.867 (95% CI, 0.683 to 0.962) and 1.00 (95% CI, 0.971 to 1.000) for 'semi-urgent', 0.853 (95% CI, 0.689 to 0.950) and 0.983 (95% CI, 0.942 to 0.998) for 'routine' and 1.00 (95% CI, 0.958 to 1.00) and 0.896 (95% CI, 0.797 to 0.957) for 'observation', respectively.
conclusionsThe AI system achieved promising performance in the diagnosis of cataracts and corneal diseases.
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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 OpenQuestion graph.