Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning
Daniel S Kermany, Michael Goldbaum, Wenjia Cai, Carolina C S Valentim, Huiying Liang, Sally L Baxter, Alex McKeown, Ge Yang, Xiaokang Wu, Fangbing Yan, Justin Dong, Made K Prasadha, Jacqueline Pei, Magdalene Y L Ting, Jie Zhu, Christina Li, Sierra Hewett, Jason Dong, Ian Ziyar, Alexander Shi, Runze Zhang, Lianghong Zheng, Rui Hou, William Shi, Xin Fu, Yaou Duan, Viet A N Huu, Cindy Wen, Edward D Zhang, Charlotte L Zhang, Oulan Li, Xiaobo Wang, Michael A Singer, Xiaodong Sun, Jie Xu, Ali Tafreshi, M Anthony Lewis, Huimin Xia, Kang Zhang, Daniel S Kermany, Michael Goldbaum, Wenjia Cai, Carolina C S Valentim, Huiying Liang, Sally L Baxter, Alex McKeown, Ge Yang, Xiaokang Wu, Fangbing Yan, Justin Dong, Made K Prasadha, Jacqueline Pei, Magdalene Y L Ting, Jie Zhu, Christina Li, Sierra Hewett, Jason Dong, Ian Ziyar, Alexander Shi, Runze Zhang, Lianghong Zheng, Rui Hou, William Shi, Xin Fu, Yaou Duan, Viet A N Huu, Cindy Wen, Edward D Zhang, Charlotte L Zhang, Oulan Li, Xiaobo Wang, Michael A Singer, Xiaodong Sun, Jie Xu, Ali Tafreshi, M Anthony Lewis, Huimin Xia, Kang Zhang
Abstract
The implementation of clinical-decision support algorithms for medical imaging faces challenges with reliability and interpretability. Here, we establish a diagnostic tool based on a deep-learning framework for the screening of patients with common treatable blinding retinal diseases. Our framework utilizes transfer learning, which trains a neural network with a fraction of the data of conventional approaches. Applying this approach to a dataset of optical coherence tomography images, we demonstrate performance comparable to that of human experts in classifying age-related macular degeneration and diabetic macular edema. We also provide a more transparent and interpretable diagnosis by highlighting the regions recognized by the neural network. We further demonstrate the general applicability of our AI system for diagnosis of pediatric pneumonia using chest X-ray images. This tool may ultimately aid in expediting the diagnosis and referral of these treatable conditions, thereby facilitating earlier treatment, resulting in improved clinical outcomes. VIDEO ABSTRACT.
Keywords: age-related macular degeneration; artificial intelligence; choroidal neovascularization; deep learning; diabetic macular edema; diabetic retinopathy; optical coherence tomography; pneumonia; screening; transfer learning.
Copyright © 2018 Elsevier Inc. All rights reserved.
Source: PubMed