Algorithm of automatic identification of diabetic retinopathy foci based on ultra-widefield scanning laser ophthalmoscopy.

Autor:	Wang J; Aier Eye Hospital (East of Chengdu), Chengdu 610051, Sichuan Province, China., Wang SZ; Department of Ophthalmology, Chengdu First People's Hospital, Chengdu 610095, Sichuan Province, China., Qin XL; Aier Eye Hospital (East of Chengdu), Chengdu 610051, Sichuan Province, China., Chen M; Aier Eye Hospital (East of Chengdu), Chengdu 610051, Sichuan Province, China., Zhang HM; School of Computer and Artificial Intelligence, Southwest Jiaotong University, Chengdu 610097, Sichuan Province, China., Liu X; Aier Eye Hospital (East of Chengdu), Chengdu 610051, Sichuan Province, China., Xiang MJ; Aier Eye Hospital (East of Chengdu), Chengdu 610051, Sichuan Province, China., Hu JB; Chengdu Aier Eye Hospital, Chengdu 610041, Sichuan Province, China., Huang HY; School of Computer and Artificial Intelligence, Southwest Jiaotong University, Chengdu 610097, Sichuan Province, China., Lan CJ; Aier Eye Hospital (East of Chengdu), Chengdu 610051, Sichuan Province, China.
Jazyk:	angličtina
Zdroj:	International journal of ophthalmology [Int J Ophthalmol] 2024 Apr 18; Vol. 17 (4), pp. 610-615. Date of Electronic Publication: 2024 Apr 18 (Print Publication: 2024).
DOI:	10.18240/ijo.2024.04.02
Abstrakt:	Aim: To propose an algorithm for automatic detection of diabetic retinopathy (DR) lesions based on ultra-widefield scanning laser ophthalmoscopy (SLO). Methods: The algorithm utilized the FasterRCNN (Faster Regions with CNN features)+ResNet50 (Residua Network 50)+FPN (Feature Pyramid Networks) method for detecting hemorrhagic spots, cotton wool spots, exudates, and microaneurysms in DR ultra-widefield SLO. Subimage segmentation combined with a deeper residual network FasterRCNN+ResNet50 was employed for feature extraction to enhance intelligent learning rate. Feature fusion was carried out by the feature pyramid network FPN, which significantly improved lesion detection rates in SLO fundus images. Results: By analyzing 1076 ultra-widefield SLO images provided by our hospital, with a resolution of 2600×2048 dpi, the accuracy rates for hemorrhagic spots, cotton wool spots, exudates, and microaneurysms were found to be 87.23%, 83.57%, 86.75%, and 54.94%, respectively. Conclusion: The proposed algorithm demonstrates intelligent detection of DR lesions in ultra-widefield SLO, providing significant advantages over traditional fundus color imaging intelligent diagnosis algorithms. Competing Interests: Conflicts of Interest: Wang J, None; Wang SZ, None; Qin XL, None; Chen M, None; Zhang HM, None; Liu X, None; Xiang MJ, None; Hu JB, None; Huang HY, None; Lan CJ, None. (International Journal of Ophthalmology Press.)
Databáze:	MEDLINE
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