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

Cell. 2018 Feb 22;172(5):1122-1131.e9. 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. 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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Child
  • Deep Learning*
  • Diagnostic Imaging*
  • Humans
  • Neural Networks, Computer
  • Pneumonia / diagnosis*
  • Pneumonia / diagnostic imaging
  • ROC Curve
  • Reproducibility of Results
  • Tomography, Optical Coherence