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

Autor: Romane Gauriau, Bernardo C. Bizzo, Donnella S. Comeau, James M. Hillis, Christopher P. Bridge, John K. Chin, Jayashri Pawar, Ali Pourvaziri, Ivana Sesic, Elshaimaa Sharaf, Jinjin Cao, Flavia T. C. Noro, Walter F. Wiggins, M. Travis Caton, Felipe Kitamura, Keith J. Dreyer, John F. Kalafut, Katherine P. Andriole, Stuart R. Pomerantz, Ramon G. Gonzalez, Michael H. Lev
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Scientific Reports, Vol 13, Iss 1, Pp 1-11 (2023)
Druh dokumentu: article
ISSN: 2045-2322
DOI: 10.1038/s41598-023-27496-5
Popis: Abstract Non-contrast head CT (NCCT) is extremely insensitive for early ( 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.
Databáze: Directory of Open Access Journals
Nepřihlášeným uživatelům se plný text nezobrazuje