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Deep learning versus ophthalmologists for screening for glaucoma on fundus examination: A systematic review and meta-analysis.
Buisson, Mathieu; Navel, Valentin; Labbé, Antoine; Watson, Stephanie L; Baker, Julien S; Murtagh, Patrick; Chiambaretta, Frédéric; Dutheil, Frédéric.
Afiliação
  • Buisson M; CHU Clermont-Ferrand, Ophthalmology, University Hospital of Clermont-Ferrand, Clermont-Ferrand, France.
  • Navel V; CHU Clermont-Ferrand, Ophthalmology, University Hospital of Clermont-Ferrand, Clermont-Ferrand, France.
  • Labbé A; CNRS UMR 6293, INSERM U1103, Genetic Reproduction and Development Laboratory (GReD), Translational Approach to Epithelial Injury and Repair Team, Université Clermont Auvergne, Clermont-Ferrand, France.
  • Watson SL; Department of Ophthalmology III, Quinze-Vingts National Ophthalmology Hospital, IHU FOReSIGHT, Paris, France.
  • Baker JS; Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France.
  • Murtagh P; Department of Ophthalmology, Ambroise Paré Hospital, APHP, Université de Versailles Saint-Quentin en Yvelines, Versailles, France.
  • Chiambaretta F; Save Sight Institute, Discipline of Ophthalmology, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.
  • Dutheil F; Corneal Unit, Sydney Eye Hospital, Sydney, New South Wales, Australia.
Clin Exp Ophthalmol ; 49(9): 1027-1038, 2021 12.
Article em En | MEDLINE | ID: mdl-34506041
BACKGROUND: In this systematic review and meta-analysis, we aimed to compare deep learning versus ophthalmologists in glaucoma diagnosis on fundus examinations. METHOD: PubMed, Cochrane, Embase, ClinicalTrials.gov and ScienceDirect databases were searched for studies reporting a comparison between the glaucoma diagnosis performance of deep learning and ophthalmologists on fundus examinations on the same datasets, until 10 December 2020. Studies had to report an area under the receiver operating characteristics (AUC) with SD or enough data to generate one. RESULTS: We included six studies in our meta-analysis. There was no difference in AUC between ophthalmologists (AUC = 82.0, 95% confidence intervals [CI] 65.4-98.6) and deep learning (97.0, 89.4-104.5). There was also no difference using several pessimistic and optimistic variants of our meta-analysis: the best (82.2, 60.0-104.3) or worst (77.7, 53.1-102.3) ophthalmologists versus the best (97.1, 89.5-104.7) or worst (97.1, 88.5-105.6) deep learning of each study. We did not retrieve any factors influencing those results. CONCLUSION: Deep learning had similar performance compared to ophthalmologists in glaucoma diagnosis from fundus examinations. Further studies should evaluate deep learning in clinical situations.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Glaucoma / Oftalmologistas / Aprendizado Profundo Tipo de estudo: Diagnostic_studies / Prognostic_studies / Screening_studies / Systematic_reviews Limite: Humans Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Glaucoma / Oftalmologistas / Aprendizado Profundo Tipo de estudo: Diagnostic_studies / Prognostic_studies / Screening_studies / Systematic_reviews Limite: Humans Idioma: En Ano de publicação: 2021 Tipo de documento: Article