Deep Learning Models for Segmenting Non-perfusion Area of Color Fundus Photographs in Patients With Branch Retinal Vein Occlusion

Frontiers in Medicine

article

Jinxin Miao¹  Jiale Yu²  Wenjun Zou¹  Na Su¹  Zongyi Peng⁴  Xinjing Wu¹  Junlong Huang¹  Yuan Fang¹  Songtao Yuan¹  Ping Xie¹  Kun Huang²  Qiang Chen²  Zizhong Hu¹  Qinghuai Liu¹ 

[1] Department of Ophthalmology, The First Affiliated Hospital of Nanjing Medical University;School of Computer Science and Engineering, Nanjing University of Science & Technology;Department of Ophthalmology, The Affiliated Wuxi No.2 People's Hospital of Nanjing Medical University;The First School of Clinical Medicine, Nanjing Medical University

关键词: deep learning;    non-perfusion area;    color fundus photograph;    branch retinal vein occlusion;    artificial intelligence;    automatic segmentation;   

DOI  :  10.3389/fmed.2022.794045

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

Purpose To develop artificial intelligence (AI)-based deep learning (DL) models for automatically detecting the ischemia type and the non-perfusion area (NPA) from color fundus photographs (CFPs) of patients with branch retinal vein occlusion (BRVO). Methods This was a retrospective analysis of 274 CFPs from patients diagnosed with BRVO. All DL models were trained using a deep convolutional neural network (CNN) based on 45 degree CFPs covering the fovea and the optic disk. We first trained a DL algorithm to identify BRVO patients with or without the necessity of retinal photocoagulation from 219 CFPs and validated the algorithm on 55 CFPs. Next, we trained another DL algorithm to segment NPA from 104 CFPs and validated it on 29 CFPs, in which the NPA was manually delineated by 3 experienced ophthalmologists according to fundus fluorescein angiography. Both DL models have been cross-validated 5-fold. The recall, precision, accuracy, and area under the curve (AUC) were used to evaluate the DL models in comparison with three types of independent ophthalmologists of different seniority. Results In the first DL model, the recall, precision, accuracy, and area under the curve (AUC) were 0.75 ± 0.08, 0.80 ± 0.07, 0.79 ± 0.02, and 0.82 ± 0.03, respectively, for predicting the necessity of laser photocoagulation for BRVO CFPs. The second DL model was able to segment NPA in CFPs of BRVO with an AUC of 0.96 ± 0.02. The recall, precision, and accuracy for segmenting NPA was 0.74 ± 0.05, 0.87 ± 0.02, and 0.89 ± 0.02, respectively. The performance of the second DL model was nearly comparable with the senior doctors and significantly better than the residents. Conclusion These results indicate that the DL models can directly identify and segment retinal NPA from the CFPs of patients with BRVO, which can further guide laser photocoagulation. Further research is needed to identify NPA of the peripheral retina in BRVO, or other diseases, such as diabetic retinopathy.

【 授权许可】

CC BY