Head CT deep learning model is highly accurate for early infarct estimation

Sci Rep. 2023 Jan 5;13(1):189. doi: 10.1038/s41598-023-27496-5.


Non-contrast head CT (NCCT) is extremely insensitive for early (< 3-6 h) acute infarct identification. We developed a deep learning model that detects and delineates suspected early acute infarcts on NCCT, using diffusion MRI as ground truth (3566 NCCT/MRI training patient pairs). The model substantially outperformed 3 expert neuroradiologists on a test set of 150 CT scans of patients who were potential candidates for thrombectomy (60 stroke-negative, 90 stroke-positive middle cerebral artery territory only infarcts), with sensitivity 96% (specificity 72%) for the model versus 61-66% (specificity 90-92%) for the experts; model infarct volume estimates also strongly correlated with those of diffusion MRI (r2 > 0.98). When this 150 CT test set was expanded to include a total of 364 CT scans with a more heterogeneous distribution of infarct locations (94 stroke-negative, 270 stroke-positive mixed territory infarcts), model sensitivity was 97%, specificity 99%, for detection of infarcts larger than the 70 mL volume threshold used for patient selection in several major randomized controlled trials of thrombectomy treatment.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Deep Learning*
  • Humans
  • Infarction, Middle Cerebral Artery
  • Magnetic Resonance Imaging
  • Stroke* / diagnostic imaging
  • Tomography, X-Ray Computed