Using an artificial neural network to predict traumatic brain injury.

Hale AT, Stonko DP, Lim J, Guillamondegui OD, Shannon CN, Patel MB
J Neurosurg Pediatr. 2018 23 (2): 219-226

PMID: 30485240 · DOI:10.3171/2018.8.PEDS18370

In BriefPediatric traumatic brain injury (TBI) is common, but not all injuries require hospitalization. A computational tool for ruling-in patients who will have clinically relevant TBI (CRTBI) would be valuable, providing an evidence-based mechanism for safe discharge. Here, using data from 12,902 patients from the Pediatric Emergency Care Applied Research Network (PECARN) TBI data set, the authors utilize artificial intelligence to predict CRTBI using radiologist-interpreted CT information with > 99% sensitivity and an AUC of 0.99.

MeSH Terms (13)

Adolescent Algorithms Area Under Curve Brain Injuries, Traumatic Child False Positive Reactions Female Humans Male Neural Networks, Computer Predictive Value of Tests Sensitivity and Specificity Tomography, X-Ray Computed

Connections (1)

This publication is referenced by other Labnodes entities:

Links