Analysis of the diagnostic value of artificial intelligence in retinal vein occlu-sion
10.13389/j.cnki.rao.2025.0009
- VernacularTitle:人工智能技术对视网膜静脉阻塞的诊断价值分析
- Author:
Songtao LIU
1
;
Hui LÜ
;
Xiangling LIU
Author Information
1. 454000 河南省焦作市,焦作煤业集团有限责任公司中央医院眼科
- Publication Type:Journal Article
- Keywords:
artificial intelligence;
retinal vein occlusion;
medical image analysis;
diagnostic value analysis
- From:
Recent Advances in Ophthalmology
2025;45(1):46-49
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the diagnostic value of artificial intelligence(AI)in retinal vein occlusion(RVO).Methods The fundus images from 9,000 patients who visited our ophthalmology department and underwent non-mydriat-ic fundus photography from July 2016 to June 2022 were selected.Among these images,700 fundus images of patients with RVO were screened.These fundus images were processed using AI in collaboration with School of Electrical Engineering and Automation of Henan Polytechnic University.Besides,the state-of-the-art two-stage algorithms(the Faster-RCNN model),advanced one-stage detection algorithms(the YOLOV4 and YOLOV5 models),and a modified YOLOV5 model spe-cifically designed for RVO were employed in this study to process the collected RVO data.In addition,a comparative analy-sis of the results was conducted.Moreover,the sensitivity and specificity of the YOLOV5 model for the diagnosis of RVO were evaluated based on the receiver operating characteristic(ROC)curve.Results The Faster-RCNN model demon-strated the highest detection accuracy;however,its parameter scale was significantly larger than that of other models,and its frame rate was only 8 frames per second,which did not meet real-time detection requirements.The detection accuracy of the modified YOLOV5 model was only 3％lower than that of the Faster-RCNN model,but it had a significantly smaller parameter scale compared with the Faster-RCNN model,achieving a frame rate of 22 frames per second higher than the Faster-RCNN model.The area under the ROC curve(AUC)plotted based on the Faster-RCNN,YOLOV4,YOLOV5,and modified YOLOV5 models was 0.946(95％CI:0.933-0.959),0.794(95％CI:0.772-0.816),0.864(95％CI:0.845-0.884),and 0.930(95％CI:0.915-0.944),respectively.The sensitivity and specificity of the modified YOLOV5 model for the diagnosis of RVO were 87.0％and 98.9％,respectively.Conclusion The modified YOLOV5 model,with its high detection accuracy,smaller parameter scale,and high sensitivity and specificity for the diagnosis of RVO,can be consid-ered a new AI-assisted diagnostic method for RVO.
