• De-Kuang Hwang, Wei-Kuang Yu, Tai-Chi Lin, Shih-Jie Chou, Aliaksandr Yarmishyn, Zih-Kai Kao, Chung-Lan Kao, Yi-Ping Yang, Shih-Jen Chen, Chih-Chien Hsu, and Ying-Chun Jheng.
• Department of Ophthalmology, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.
• J Chin Med Assoc. 2020 Dec 1; 83 (12): 1102-1106.

BackgroundDiabetic macular edema (DME) is a sight-threatening condition that needs regular examinations and remedies. Optical coherence tomography (OCT) is the most common used examination to evaluate the structure and thickness of the macula, but the software in the OCT machine does not tell the clinicians whether DME exists directly. Recently, artificial intelligence (AI) is expected to aid in diagnosis generation and therapy selection. We thus develop a smartphone-based offline AI system that provides diagnostic suggestions and medical strategies through analyzing OCT images from diabetic patients at the risk of developing DME.MethodsDME patients receiving treatments in 2017 at Taipei Veterans General Hospital were included in this study. We retrospectively collected the OCT images of these patients from January 2008 to July 2018. We established the AI model based on MobileNet architecture to classify the OCT images conditions. The confusion matrix has been applied to present the performance of the trained AI model.ResultsBased on the convolutional neural network with the MobileNet model, our AI system achieved a high DME diagnostic accuracy of 90.02%, which is comparable to other AI systems such as InceptionV3 and VGG16. We further developed a mobile-application based on this AI model available at https://aicl.ddns.net/DME.apk.ConclusionWe successful integrated an AI model into the mobile device to provide an offline method to provide the diagnosis for quickly screening the risk of developing DME. With the offline property, our model could help those nonophthalmological healthcare providers in offshore islands or underdeveloped countries.

14 keywords...

hide…