DETECTION OF MORPHOLOGIC PATTERNS OF DIABETIC MACULAR EDEMA USING A DEEP LEARNING APPROACH BASED ON OPTICAL COHERENCE TOMOGRAPHY IMAGES. (May 2021)

• Record Type: Journal Article
• Authors: Wu, Qiaowei; Zhang, Bin; Hu, Yijun; Liu, Baoyi; Cao, Dan; Yang, Dawei; Peng, Qingsheng; Zhong, Pingting; Zeng, Xiaomin; Xiao, Yu; Li, Cong; Fang, Ying; Feng, Songfu; Huang, Manqing; Cai, Hongmin; Yang, Xiaohong; Yu, Honghua
• Is Part Of: Retina. Volume 41: Supplement 5(2021)
• Publication Date: 2021-05
• Subjects: artificial intelligence -- deep learning -- complication of diabetic retinopathy -- diabetic macular edema -- optical coherence tomography; Retina -- Diseases -- Periodicals; Retinal Diseases; Vitreous Body; 617.735
• DOI: 10.1097/IAE.0000000000002992
• Languages: English
• ISSNs: 0275-004X

Abstract

Abstract : Purpose: To develop a deep learning (DL) model to detect morphologic patterns of diabetic macular edema (DME) based on optical coherence tomography (OCT) images. Methods: In the training set, 12, 365 OCT images were extracted from a public data set and an ophthalmic center. A total of 656 OCT images were extracted from another ophthalmic center for external validation. The presence or absence of three OCT patterns of DME, including diffused retinal thickening, cystoid macular edema, and serous retinal detachment, was labeled with 1 or 0, respectively. A DL model was trained to detect three OCT patterns of DME. The occlusion test was applied for the visualization of the DL model. Results: Applying 5-fold cross-validation method in internal validation, the area under the receiver operating characteristic curve for the detection of three OCT patterns (i.e., diffused retinal thickening, cystoid macular edema, and serous retinal detachment) was 0.971, 0.974, and 0.994, respectively, with an accuracy of 93.0%, 95.1%, and 98.8%, respectively, a sensitivity of 93.5%, 94.5%, and 96.7%, respectively, and a specificity of 92.3%, 95.6%, and 99.3%, respectively. In external validation, the area under the receiver operating characteristic curve was 0.970, 0.997, and 0.997, respectively, with an accuracy of 90.2%, 95.4%, and 95.9%, respectively, a sensitivity of 80.1%, 93.4%, and 94.9%, respectively, and a specificity of 97.6%, 97.2%, and 96.5%, respectively. The occlusion test showed that the DL model could successfully identify the pathologic regions most critical for detection. Conclusion: Our DL model demonstrated high accuracy and transparency in the detection of OCT patterns of DME. These results emphasized the potential of artificial intelligence in assisting clinical decision-making processes in patients with DME. Abstract : An image-based deep learning model was developed to detect different morphological patterns of diabetic macular edema based on optical coherence tomography images with high accuracy and transparency. This model could help ophthalmologists to make personalized therapeutic strategies for patients with diabetic macular edema.