Real-World Prospective Validation and Economic Evaluation of Deep Learning– Based Diabetic Retinopathy Detection From Fundus Photographs: A Systematic Review and Meta-analysis
Rattachement africain : hk, gb, cn, sg. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
BACKGROUND: Deep learning (DL) has shown promise in delivering diagnostic and economic benefits for detecting diabetic retinopathy (DR) from fundus photographs (FPs). However, evidence synthesis of model validation in prospective, real-world settings remains limited. PURPOSE: To assess the feasibility of implementing DL-DR systems using FPs across different countries by synthesizing prospective validation and economic evidence. DATA SOURCES: Five databases were searched until 13 August 2025. STUDY SELECTION: Studies prospectively assessing diagnostic performance and/or studies conducting economic analyses of DL-DR systems using FPs were selected. DATA EXTRACTION: Characteristics of all studies, performance parameters of prospective validation studies, and economic outcomes of economic analysis studies were extracted. DATA SYNTHESIS: Forty-seven studies were included in the meta-analysis. The pooled performance was the highest in detecting vision-threatening DR (area under the receiver operating characteristic curve [AUROC] 0.974), followed by any DR (AUROC 0.965), then referable DR (RDR) (AUROC 0.959). Study region, clinical pathway, mydriasis, image quality control, sample size, grading criteria, reference standard, and model architecture significantly affected model performance in RDR detection. Fifteen studies were included in the economic commentary, showing that DL-based DR screening was cost-effective in high-income countries, whereas results in middle-income countries were mixed, depending on compliance rates, glycemic control, and initial costs. LIMITATIONS: A paucity of studies assessing multiple severities of DR or diabetic macular edema restricted our ability to perform subgroup analyses. Insights into low-income countries were limited by a lack of studies in these regions. CONCLUSIONS: DL-DR systems using FPs had high discriminative performance in prospective real-world settings and hold promise to improve cost-effectiveness, especially in high-income countries.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Real-World Prospective Validation and Economic Evaluation of Deep Learning– Based Diabetic Retinopathy Detection From Fundus Photographs: A Systematic Review and Meta-analysis
- Date Crossref
- 19/11/2025
- Éditeur
- American Diabetes Association
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Chinese University of Hong Kong Department of Ophthalmology and Visual Sciences pays non établi dans la noticeUniversité ou école supérieure
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The London College pays non établi dans la noticeUniversité ou école supérieure
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Imperial College London pays non établi dans la noticeUniversité ou école supérieure
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University Hospitals Birmingham NHS Foundation Trust pays non établi dans la noticeÉtablissement de santé
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Sun Yat-sen University State Key Laboratory of Ophthalmology pays non établi dans la noticeUniversité ou école supérieure
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Hainan Eye Hospital pays non établi dans la noticeÉtablissement de santé
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Hong Kong Eye Hospital pays non établi dans la noticeÉtablissement de santé
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Singapore National Eye Center pays non établi dans la noticeÉtablissement de santé
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Beijing Tsinghua Chang Gung Hospital pays non établi dans la noticeÉtablissement de santé
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Tsinghua University Beijing Visual Science and Translational Eye Research Institute pays non établi dans la noticeUniversité ou école supérieure
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Moorfields Eye Hospital NHS Foundation Trust pays non établi dans la noticeÉtablissement de santé
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Moorfields Eye Hospital pays non établi dans la noticeÉtablissement de santé
Department of Ophthalmology and Visual Sciences — Chinese University of Hong Kong, The London College et Imperial College London, avec 9 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.