{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T05:52:25Z","timestamp":1790833945905,"version":"4.1.0"},"reference-count":37,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2021,3,13]],"date-time":"2021-03-13T00:00:00Z","timestamp":1615593600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Diabetes Metabolism Res"],"published-print":{"date-parts":[[2021,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Aims<\/jats:title><jats:p>To establish an automated method for identifying referable diabetic retinopathy (DR), defined as moderate nonproliferative DR and above, using deep learning\u2010based lesion detection and stage grading.<\/jats:p><\/jats:sec><jats:sec><jats:title>Materials and Methods<\/jats:title><jats:p>A set of 12,252 eligible fundus images of diabetic patients were manually annotated by 45 licenced ophthalmologists and were randomly split into training, validation, and internal test sets (ratio of 7:1:2). Another set of 565 eligible consecutive clinical fundus images was established as an external test set. For automated referable DR identification, four deep learning models were programmed based on whether two factors were included: DR\u2010related lesions and DR stages. Sensitivity, specificity and the area under the receiver operating characteristic curve (AUC) were reported for referable DR identification, while precision and recall were reported for lesion detection.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Adding lesion information to the five\u2010stage grading model improved the AUC (0.943 vs. 0.938), sensitivity (90.6% vs. 90.5%) and specificity (80.7% vs. 78.5%) of the model for identifying referable DR in the internal test set. Adding stage information to the lesion\u2010based model increased the AUC (0.943 vs. 0.936) and sensitivity (90.6% vs. 76.7%) of the model for identifying referable DR in the internal test set. Similar trends were also seen in the external test set. DR lesion types with high precision results were preretinal haemorrhage, hard exudate, vitreous haemorrhage, neovascularisation, cotton wool spots and fibrous proliferation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The herein described automated model employed DR lesions and stage information to identify referable DR and displayed better diagnostic value than models built without this information.<\/jats:p><\/jats:sec>","DOI":"10.1002\/dmrr.3445","type":"journal-article","created":{"date-parts":[[2021,3,13]],"date-time":"2021-03-13T18:30:17Z","timestamp":1615660217000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Deep learning\u2010based detection and stage grading for optimising diagnosis of diabetic retinopathy"],"prefix":"10.1002","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9843-6280","authenticated-orcid":false,"given":"Yuelin","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Ophthalmology Peking Union Medical College Hospital Chinese Academy of Medical Sciences  Beijing China"},{"name":"Key Lab of Ocular Fundus Disease Chinese Academy of Medical Sciences  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7232-8150","authenticated-orcid":false,"given":"Miao","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Endocrinology Key Laboratory of Endocrinology National Health Commission Peking Union Medical College Hospital Peking Union Medical College and Chinese Academy of Medical Sciences  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bojie","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology Tianjin Medical University Eye Hospital  Tianjin China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuemin","family":"Jin","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology The First Affiliated Hospital of Zhengzhou University  Zhengzhou Henan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yibin","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology Beijing Institute of Ophthalmology Beijing Tongren Hospital Capital Medical University  Beijing China"},{"name":"Beijing Key Laboratory of Ophthalmology and Visual Science Beijing Tongren Eye Center Beijing Tongren Hospital Capital Medical University  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology Peking Union Medical College Hospital Chinese Academy of Medical Sciences  Beijing 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