Artificial intelligence-based detection of epimacular membrane from color fundus photographs.


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
29 09 2021
Historique:
received: 30 09 2020
accepted: 01 09 2021
entrez: 30 9 2021
pubmed: 1 10 2021
medline: 28 12 2021
Statut: epublish

Résumé

Epiretinal membrane (ERM) is a common ophthalmological disorder of high prevalence. Its symptoms include metamorphopsia, blurred vision, and decreased visual acuity. Early diagnosis and timely treatment of ERM is crucial to preventing vision loss. Although optical coherence tomography (OCT) is regarded as a de facto standard for ERM diagnosis due to its intuitiveness and high sensitivity, ophthalmoscopic examination or fundus photographs still have the advantages of price and accessibility. Artificial intelligence (AI) has been widely applied in the health care industry for its robust and significant performance in detecting various diseases. In this study, we validated the use of a previously trained deep neural network based-AI model in ERM detection based on color fundus photographs. An independent test set of fundus photographs was labeled by a group of ophthalmologists according to their corresponding OCT images as the gold standard. Then the test set was interpreted by other ophthalmologists and AI model without knowing their OCT results. Compared with manual diagnosis based on fundus photographs alone, the AI model had comparable accuracy (AI model 77.08% vs. integrated manual diagnosis 75.69%, χ

Identifiants

pubmed: 34588493
doi: 10.1038/s41598-021-98510-x
pii: 10.1038/s41598-021-98510-x
pmc: PMC8481557
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

19291

Informations de copyright

© 2021. The Author(s).

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Auteurs

Enhua Shao (E)

Department of Ophthalmology, Beijing Tisnghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.

Congxin Liu (C)

Beijing Eaglevision Technology Co., Ltd, Beijing, China.

Lei Wang (L)

Department of Ophthalmology, Beijing Tisnghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.

Dan Song (D)

Department of Ophthalmology, Beijing Tisnghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.

Libin Guo (L)

Department of Ophthalmology, Beijing Tisnghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.

Xuan Yao (X)

Beijing Eaglevision Technology Co., Ltd, Beijing, China.

Jianhao Xiong (J)

Beijing Eaglevision Technology Co., Ltd, Beijing, China.

Bin Wang (B)

Beijing Eaglevision Technology Co., Ltd, Beijing, China.

Yuntao Hu (Y)

Department of Ophthalmology, Beijing Tisnghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China. ythu@mail.tsinghua.edu.cn.

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