SynthesisTranslational vision science & technology2025
Diagnostic Accuracy of AI Models in Detecting Different Inherited Retinal Diseases: A Systematic Review and Meta-Analysis.
Synthesis in Translational vision science & technology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Deep learning model development and clinical validation for radiographic surrogate markers of implant esthetic risk.NPJ digital medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: The purpose of this study was to evaluate the diagnostic accuracy of artificial intelligence (AI) models in detecting different types of inherited retinal diseases (IRDs), including retinitis pigmentosa (RP), Stargardt disease, and familial exudative vitreoretinopathy (FEVR). Methods: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic search of databases, such as ScienceDirect, PubMed, Scopus, WoS, and IEEE, was conducted. Studies were included in meta-analysis if data on true positive, false positive, true negative, and false negative could be extracted for AI models, diagnosing IRDs. Data extraction included study characteristics, diagnostic accuracy metrics, and imaging modalities, with QUADAS-2 used for risk of bias assessment. Pooled sensitivity and specificity were calculated using random-effects models, and a summary receiver operating characteristic curve was generated. Results: Overall, 5412 articles were identified through the database search. After screening, 22 studies were included, with 21 analyzed quantitatively. The meta-analysis showed a pooled sensitivity of 94% and specificity of 99% for RP, 96% sensitivity and 99% specificity for Stargardt disease, and 85% sensitivity and 99% specificity for FEVR. The diagnostic odds ratio was 2486 for RP and 2236 for Stargardt disease. Subgroup analyses indicated that wide-field imaging had higher specificity for RP compared to standard fundus photography. Conclusions: AI models demonstrate high diagnostic accuracy for IRDs, particularly for RP and Stargardt disease. These findings underscore the potential of AI as an adjunct diagnostic tool, although real-world validation is needed for broader applicability across diverse clinical settings. Translational Relevance: These findings support the integration of AI into ophthalmic care to improve early detection and patient management.
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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.