Open AccessSmartphone-based diabetic macula edema screening with an offline artificial intelligence
Author(s) -
DeKuang Hwang,
Wen-Chung Yu,
TzuChin Lin,
Shih Jie Chou,
Aliaksandr A. Yarmishyn,
Zih-Kai Kao,
Chung Lan Kao,
Yi Yang,
ShihJen Chen,
Chih-Chien Hsu,
Ying Chun Jheng
Publication year - 2020
Publication title -
journal of the chinese medical association
Language(s) - English
Resource type - Journals
eISSN - 1728-7731
pISSN - 1726-4901
DOI - 10.1097/jcma.0000000000000355
Subject(s) - medicine , convolutional neural network , optical coherence tomography , confusion matrix , artificial intelligence , confusion , machine learning , computer science , ophthalmology , psychology , psychoanalysis
Diabetic 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.