Open AccessArtificial intelligence can assist with diagnosing retinal vein occlusion
Author(s) -
Qiong Chen,
Shoutzu Lin,
Boshi Liu,
Yong Wang,
Qijie Wei,
Xixi He,
Fei Ding,
Gang Yang,
Youxin Chen,
Xiaorong Li,
BinJie Hu,
Visionary Intelligence Ltd Vistel Ai Lab
Publication year - 2021
Publication title -
international journal of ophthalmology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.634
H-Index - 29
eISSN - 2227-4898
pISSN - 2222-3959
DOI - 10.18240/ijo.2021.12.13
Subject(s) - medicine , retinal vein , occlusion , artificial intelligence , segmentation , fundus (uterus) , lesion , cotton wool spots , ophthalmology , pattern recognition (psychology) , diabetic retinopathy , surgery , computer science , diabetes mellitus , endocrinology
AIM: To assist with retinal vein occlusion (RVO) screening, artificial intelligence (AI) methods based on deep learning (DL) have been developed to alleviate the pressure experienced by ophthalmologists and discover and treat RVO as early as possible.METHODS: A total of 8600 color fundus photographs (CFPs) were included for training, validation, and testing of disease recognition models and lesion segmentation models. Four disease recognition and four lesion segmentation models were established and compared. Finally, one disease recognition model and one lesion segmentation model were selected as superior. Additionally, 224 CFPs from 130 patients were included as an external test set to determine the abilities of the two selected models.RESULTS: Using the Inception-v3 model for disease identification, the mean sensitivity, specificity, and F1 for the three disease types and normal CFPs were 0.93, 0.99, and 0.95, respectively, and the mean area under the curve (AUC) was 0.99. Using the DeepLab-v3 model for lesion segmentation, the mean sensitivity, specificity, and F1 for four lesion types (abnormally dilated and tortuous blood vessels, cotton-wool spots, flame-shaped hemorrhages, and hard exudates) were 0.74, 0.97, and 0.83, respectively.CONCLUSION: DL models show good performance when recognizing RVO and identifying lesions using CFPs. Because of the increasing number of RVO patients and increasing demand for trained ophthalmologists, DL models will be helpful for diagnosing RVO early in life and reducing vision impairment.