Details

Autor(en) / Beteiligte

• 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
• Prasadha, Made K.
• Pei, Jacqueline
• Ting, Magdalene Y.L.
• 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
• Lewis, M. Anthony
• Xia, Huimin
• Zhang, Kang

Titel

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

Ist Teil von

• Cell, 2018-02, Vol.172 (5), p.1122-1131.e9

Ort / Verlag

United States: Elsevier Inc

Erscheinungsjahr

2018

Quelle

Access via ScienceDirect (Elsevier)

Beschreibungen/Notizen

• 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. [Display omitted] [Display omitted] •An artificial intelligence system using transfer learning techniques was developed•It effectively classified images for macular degeneration and diabetic retinopathy•It also accurately distinguished bacterial and viral pneumonia on chest X-rays•This has potential for generalized high-impact application in biomedical imaging Image-based deep learning classifies macular degeneration and diabetic retinopathy using retinal optical coherence tomography images and has potential for generalized applications in biomedical image interpretation and medical decision making.