ArticleTranslational vision science & technology2022
Evaluation of an OCT-AI-Based Telemedicine Platform for Retinal Disease Screening and Referral in a Primary Care Setting.
Article in Translational vision science & technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Opportunities, challenges, and requirements for Artificial Intelligence (AI) implementation in Primary Health Care (PHC): a systematic review.BMC primary care · 2025Pooled it
- Home-monitoring/remote optical coherence tomography in teleophthalmology in patients with eye disorders-a systematic review.Frontiers in medicine · 2024Pooled it
- Prospective real-world implementation of deep learning systems in healthcare: a systematic review guided by implementation science.NPJ digital medicine · 2026Article
- Artificial Intelligence in Neuro-Ophthalmology for Optic Disc Pathologies and Neurodegenerative Disease.Eye and brain · 2026Review
- PAIR: Evaluating the Limits of Agreement Among Non-Retinal Specialist Using PathFinder Artificial Intelligence Tool for Retinal Disease Referrals: A Prospective Observational Study.Clinical ophthalmology (Auckland, N.Z.) · 2026Article
- Compact vision language models enable efficient and interpretable optical coherence tomography through layer-specific multimodal learning.Communications medicine · 2025Article
- Early Detection of Cystoid Macular Edema in Retinitis Pigmentosa Using Longitudinal Deep Learning Analysis of OCT Scans.Diagnostics (Basel, Switzerland) · 2025Article
- AI and Primary Care: Scoping Review.Journal of medical Internet research · 2025Article
- Enhancing Ophthalmic Diagnosis and Treatment with Artificial Intelligence.Medicina (Kaunas, Lithuania) · 2025Review
- Predicting visual acuity of treated ocular trauma based on pattern visual evoked potentials by machine learning models.Frontiers in cell and developmental biology · 2025Article
- Novel Approaches for the Early Detection of Glaucoma Using Artificial Intelligence.Life (Basel, Switzerland) · 2024Review
- Implementing teleophthalmology services to improve cost-effectiveness of the national eye care system.Eye (London, England) · 2024Article
- Effects of artificial intelligence implementation on efficiency in medical imaging-a systematic literature review and meta-analysis.NPJ digital medicine · 2024Review
- Novel Approaches for Early Detection of Retinal Diseases Using Artificial Intelligence.Journal of personalized medicine · 2024Review
- Inter-rater reliability in labeling quality and pathological features of retinal OCT scans: A customized annotation software approach.PloS one · 2024Article
- How Artificial Intelligence Is Shaping Medical Imaging Technology: A Survey of Innovations and Applications.Bioengineering (Basel, Switzerland) · 2023Review
- Accuracy and feasibility with AI-assisted OCT in retinal disorder community screening.Frontiers in cell and developmental biology · 2022Article
- Interpretation of Clinical Retinal Images Using an Artificial Intelligence Chatbot.Ophthalmology scienceArticle
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8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo evaluate the performance of a telemedicine platform integrated with optical coherence tomography (OCT) and artificial intelligence (AI) techniques for retinal disease screening and referral.
methodsWe constructed an OCT-AI-based telemedicine platform and deployed it at four primary care stations located in Jing'an district, Shanghai, to detect retinal disease cases among aged groups and refer them to Shanghai Tenth People's Hospital (TENTH Hospital). Two ophthalmologists jointly graded the data set collected from this pilot application, and then the performance of this platform was analyzed from multiple aspects.
resultsThis study included 1257 participants between July 2020 and September 2020, of whom 394 had retinal pathologies and 146 were even considered urgent cases by the ophthalmologists. The OCT-AI models achieved a sensitivity of 96.6% (95% confidence interval [CI], 91.8%-98.7%) and specificity of 98.8% (95% CI, 98.0%-99.3%) for detecting urgent cases and a sensitivity of 98.5% (95% CI, 96.5%-99.4%) and specificity of 96.2% (95% CI, 94.6%-97.3%) for detecting both urgent and routine cases. Coupled with AI, our platform reduced the workload of human consultation by 96.2% for massive normal cases. The detected disease cases received online medical suggestions at an average time of 21.4 hours via this platform.
conclusionsThis platform can automatically identify patients with retinal disease with high sensitivity and specificity, support timely human consultation, and bring necessary referrals. TRANSLATIONAL RELEVANCE: The OCT-AI-based telemedicine platform shows great practical value for retinal disease screening and referral in a real-world primary care setting.
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