Deep Learning for Automated Diabetic Retinopathy Screening Fused With Heterogeneous Data From EHRs Can Lead to Earlier Referral Decisions

PURPOSE: Fundus images are typically used as the sole training input for automated diabetic retinopathy (DR) classification. In this study, we considered several well-known DR risk factors and attempted to improve the accuracy of DR screening. METPHODS: Fusing nonimage data (e.g., age, gender, smoki...

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Detalles Bibliográficos
Autores principales: Hsu, Min-Yen, Chiou, Jeng-Yuan, Liu, Jung-Tzu, Lee, Chee-Ming, Lee, Ya-Wen, Chou, Chien-Chih, Lo, Shih-Chang, Kornelius, Edy, Yang, Yi-Sun, Chang, Sung-Yen, Liu, Yu-Cheng, Huang, Chien-Ning, Tseng, Vincent S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Association for Research in Vision and Ophthalmology 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8374997/
https://www.ncbi.nlm.nih.gov/pubmed/34403475
http://dx.doi.org/10.1167/tvst.10.9.18

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8374997/
https://www.ncbi.nlm.nih.gov/pubmed/34403475
http://dx.doi.org/10.1167/tvst.10.9.18

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