ArticleJournal of medical Internet research2023
Developing and Evaluating an AI-Based Computer-Aided Diagnosis System for Retinal Disease: Diagnostic Study for Central Serous Chorioretinopathy.
Article in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.
- A comprehensive overview: deep learning approaches to central serous chorioretinopathy diagnosis.BMC ophthalmology · 2025Pooled it
- Safety design guidelines for clinician-AI interaction in computer-aided diagnosis systems using system-theoretic framework with explainability validation.Scientific reports · 2026Article
- Utility of artificial intelligence for the diagnosis, prognosis, and management of central serous chorioretinopathy: a narrative review.International journal of retina and vitreous · 2026Review
- Factors influencing the effectiveness of artificial intelligence-assisted decision-making in medicine: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Factors Influencing the Effectiveness of AI-Assisted Decision-Making in Medicine: A Scoping Review.medRxiv : the preprint server for health sciences · 2025Article
- Research Progress in Artificial Intelligence for Central Serous Chorioretinopathy: A Systematic Review.Ophthalmology and therapy · 2025Review
- Artificial intelligence applications in ophthalmic optical coherence tomography: a 12-year bibliometric analysis.International journal of ophthalmology · 2024Article
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlthough previous research has made substantial progress in developing high-performance artificial intelligence (AI)-based computer-aided diagnosis (AI-CAD) systems in various medical domains, little attention has been paid to developing and evaluating AI-CAD system in ophthalmology, particularly for diagnosing retinal diseases using optical coherence tomography (OCT) images.
objectiveThis diagnostic study aimed to determine the usefulness of a proposed AI-CAD system in assisting ophthalmologists with the diagnosis of central serous chorioretinopathy (CSC), which is known to be difficult to diagnose, using OCT images.
methodsFor the training and evaluation of the proposed deep learning model, 1693 OCT images were collected and annotated. The data set included 929 and 764 cases of acute and chronic CSC, respectively. In total, 66 ophthalmologists (2 groups: 36 retina and 30 nonretina specialists) participated in the observer performance test. To evaluate the deep learning algorithm used in the proposed AI-CAD system, the training, validation, and test sets were split in an 8:1:1 ratio. Further, 100 randomly sampled OCT images from the test set were used for the observer performance test, and the participants were instructed to select a CSC subtype for each of these images. Each image was provided under different conditions: (1) without AI assistance, (2) with AI assistance with a probability score, and (3) with AI assistance with a probability score and visual evidence heatmap. The sensitivity, specificity, and area under the receiver operating characteristic curve were used to measure the diagnostic performance of the model and ophthalmologists.
resultsThe proposed system achieved a high detection performance (99% of the area under the curve) for CSC, outperforming the 66 ophthalmologists who participated in the observer performance test. In both groups, ophthalmologists with the support of AI assistance with a probability score and visual evidence heatmap achieved the highest mean diagnostic performance compared with that of those subjected to other conditions (without AI assistance or with AI assistance with a probability score). Nonretina specialists achieved expert-level diagnostic performance with the support of the proposed AI-CAD system.
conclusionsOur proposed AI-CAD system improved the diagnosis of CSC by ophthalmologists, which may support decision-making regarding retinal disease detection and alleviate the workload of ophthalmologists.
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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.