Expert-level detection of acute intracranial hemorrhage on head computed tomography using deep learning.
Weicheng KuoChristian HӓnePratik MukherjeeJitendra MalikEsther L YuhPublished in: Proceedings of the National Academy of Sciences of the United States of America (2019)
Computed tomography (CT) of the head is used worldwide to diagnose neurologic emergencies. However, expertise is required to interpret these scans, and even highly trained experts may miss subtle life-threatening findings. For head CT, a unique challenge is to identify, with perfect or near-perfect sensitivity and very high specificity, often small subtle abnormalities on a multislice cross-sectional (three-dimensional [3D]) imaging modality that is characterized by poor soft tissue contrast, low signal-to-noise using current low radiation-dose protocols, and a high incidence of artifacts. We trained a fully convolutional neural network with 4,396 head CT scans performed at the University of California at San Francisco and affiliated hospitals and compared the algorithm's performance to that of 4 American Board of Radiology (ABR) certified radiologists on an independent test set of 200 randomly selected head CT scans. Our algorithm demonstrated the highest accuracy to date for this clinical application, with a receiver operating characteristic (ROC) area under the curve (AUC) of 0.991 ± 0.006 for identification of examinations positive for acute intracranial hemorrhage, and also exceeded the performance of 2 of 4 radiologists. We demonstrate an end-to-end network that performs joint classification and segmentation with examination-level classification comparable to experts, in addition to robust localization of abnormalities, including some that are missed by radiologists, both of which are critically important elements for this application.
Keyphrases
- computed tomography
- deep learning
- dual energy
- artificial intelligence
- convolutional neural network
- contrast enhanced
- image quality
- optic nerve
- positron emission tomography
- machine learning
- magnetic resonance imaging
- magnetic resonance
- cross sectional
- liver failure
- risk factors
- healthcare
- high resolution
- respiratory failure
- photodynamic therapy
- diffusion weighted imaging
- soft tissue
- clinical practice
- acute respiratory distress syndrome