Intelligent diagnosis of retinal vein occlusion based on color fundus photographs

AIM:To develop an artificial intelligence(AI)diagnosis model based on deep learning(DL)algorithm to diagnose different types of retinal vein occlusion(RVO)by recognizing color fundus photographs(CFPs).METHODS:Totally 914 CFPs of healthy people and patients with RVO were collected as experimental data sets,and used to train,verify and test the diagnostic model of RVO.All the images were divided into four categories[normal,central retinal vein occlusion(CRVO),branch retinal vein occlusion(BRVO),and macular retinal vein occlusion(MRVO)]by three fundus disease experts.Swin Transformer was used to build the RVO diagnosis model,and different types of RVO diagnosis experiments were conducted.The model’s performance was compared to that of the experts.RESULTS:The accuracy of the model in the diagnosis of normal,CRVO,BRVO,and MRVO reached 1.000,0.978,0.957,and 0.978;the specificity reached 1.000,0.986,0.982,and 0.976;the sensitivity reached 1.000,0.955,0.917,and 1.000;the F1-Sore reached 1.000,0.9550.943,and 0.887 respectively.In addition,the area under curve of normal,CRVO,BRVO,and MRVO diagnosed by the diagnostic model were 1.000,0.900,0.959 and 0.970,respectively.The diagnostic results were highly consistent with those of fundus disease experts,and the diagnostic performance was superior.CONCLUSION:The diagnostic model developed in this study can well diagnose different types of RVO,effectively relieve the work pressure of clinicians,and provide help for the follow-up clinical diagnosis and treatment of RVO patients.

Different fundus imaging modalities and technical factors in AI screening for diabetic retinopathy:a review

Background:Effective screening is a desirable method for the early detection and successful treatment for diabetic retinopathy,and fundus photography is currently the dominant medium for retinal imaging due to its convenience and accessibility.Manual screening using fundus photographs has however involved considerable costs for patients,clinicians and national health systems,which has limited its application particularly in less-developed countries.The advent of artificial intelligence,and in particular deep learning techniques,has however raised the possibility of widespread automated screening.Main text:In this review,we first briefly survey major published advances in retinal analysis using artificial intelligence.We take care to separately describe standard multiple-field fundus photography,and the newer modalities of ultrawide field photography and smartphone-based photography.Finally,we consider several machine learning concepts that have been particularly relevant to the domain and illustrate their usage with extant works.Conclusions:In the ophthalmology field,it was demonstrated that deep learning tools for diabetic retinopathy show clinically acceptable diagnostic performance when using colour retinal fundus images.Artificial intelligence models are among the most promising solutions to tackle the burden of diabetic retinopathy management in a comprehensive manner.However,future research is crucial to assess the potential clinical deployment,evaluate the cost-effectiveness of different DL systems in clinical practice and improve clinical acceptance.