Expert-level detection of acute intracranial hemorrhage on head computed tomography using deep learning

• DOI: 10.1073/pnas.1908021116
• Publication Date: 2019-10-22T00:40:46Z
• Authors: Weicheng Kuo; Christian Hӓne; Pratik Mukherjee; Jitendra Malik; Esther L. Yuh
• Keywords: Neural Networks; Clinical Sciences; 610; Bioengineering; Computer; 03 medical and health sciences; Deep Learning; 0302 clinical medicine; 616; Humans; Tomography; screening and diagnosis; Biomedical and Clinical Sciences; Prevention; Neurosciences; deep learning; Biological Sciences; radiology; X-Ray Computed; 3. Good health; Detection; Acute Disease; Biomedical Imaging; Neural Networks, Computer; Tomography, X-Ray Computed; head computed tomography; Intracranial Hemorrhages; intracranial hemorrhage; Algorithms; 4.2 Evaluation of markers and technologies

Significance Computed tomography (CT) of the head is the workhorse medical imaging modality used worldwide to diagnose neurologic emergencies. However, these gray scale images are limited by low signal-to-noise, poor contrast, and a high incidence of image artifacts. A unique challenge is to identify tiny subtle abnormalities in a large 3D volume with near-perfect sensitivity. We used a single-stage, end-to-end, fully convolutional neural network to achieve accuracy levels comparable to that of highly trained radiologists, including both identification and localization of abnormalities that are missed by radiologists.