A MULTITASK DEEP-LEARNING SYSTEM FOR ASSESSMENT OF DIABETIC MACULAR ISCHEMIA ON OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY IMAGES. Issue 1 (January 2022)

• Record Type: Journal Article
• Authors: Yang, Dawei; Sun, Zihan; Shi, Jian; Ran, Anran; Tang, Fangyao; Tang, Ziqi; Lok, Jerry; Szeto, Simon; Chan, Jason; Yip, Fanny; Zhang, Liang; Meng, Qianli; Rasmussen, Martin; Grauslund, Jakob; Cheung, Carol Y.
• Is Part Of: Retina. Volume 42:Issue 1(2022)
• Publication Date: 2022-01
• Subjects: diabetic macular ischemia -- deep learning -- image quality assessment -- optical coherence tomography angiography; Retina -- Diseases -- Periodicals; Retinal Diseases; Vitreous Body; 617.735
• DOI: 10.1097/IAE.0000000000003287
• Languages: English
• ISSNs: 0275-004X

Abstract

Abstract : Purpose: We aimed to develop and test a deep-learning system to perform image quality and diabetic macular ischemia (DMI) assessment on optical coherence tomography angiography (OCTA) images. Methods: This study included 7, 194 OCTA images with diabetes mellitus for training and primary validation and 960 images from three independent data sets for external testing. A trinary classification for image quality assessment and the presence or absence of DMI for DMI assessment were labeled on all OCTA images. Two DenseNet-161 models were built for both tasks for OCTA images of superficial and deep capillary plexuses, respectively. External testing was performed on three unseen data sets in which one data set using the same model of OCTA device as of the primary data set and two data sets using another brand of OCTA device. We assessed the performance by using the area under the receiver operating characteristic curves with sensitivities, specificities, and accuracies and the area under the precision-recall curves with precision. Results: For the image quality assessment, analyses for gradability and measurability assessment were performed. Our deep-learning system achieved the area under the receiver operating characteristic curves >0.948 and area under the precision-recall curves >0.866 for the gradability assessment, area under the receiver operating characteristic curves >0.960 and area under the precision-recall curves >0.822 for the measurability assessment, and area under the receiver operating characteristic curves >0.939 and area under the precision-recall curves >0.899 for the DMI assessment across three external validation data sets. Grad-CAM demonstrated the capability of our deep-learning system paying attention to regions related to DMI identification. Conclusion: Our proposed multitask deep-learning system might facilitate the development of a simplified assessment of DMI on OCTA images among individuals with diabetes mellitus at high risk for visual loss. Abstract : Supplemental Digital Content is Available in the Text.We developed and tested a deep-learning system that achieved good performance in performing image quality assessment as well as classifying the presence or absence of diabetic macular ischemia in both superficial and deep capillary plexuses in two widely used optical coherence tomography angiography devices for primary validation and external testing.