Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning

作者：Kermany Daniel S; Goldbaum Michael; Cai Wenjia; Valentim Carolina C S; Liang Huiying; Baxter Sally L; McKeown Alex; Yang Ge; Wu Xiaokang; Yan Fangbing; Dong Justin; Pra***ha Made K; Pei Jacqueline; Ting Magdalena; Zhu Jie; Li Christina; Hewett Sierra; Dong Jason; Ziyar Ian; Shi Alexander; Zhang Runze; Zheng Lianghong; Hou Rui; Shi William; Fu Xin; Duan Yaou; Huu Viet A N; Wen Cindy; Zhang Edward D; Zhang Charlotte L; Li Oulan; Wang Xiaobo; Singer Michael A; Sun Xiaodong; Xu Jie; Tafreshi Ali

来源：Cell, 2018, 172(5): 1122-+.

DOI：10.1016/j.cell.2018.02.010

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.

• 单位
  上海交通大学; 广州医学院; 首都医科大学; 北京市眼科研究所