Citation

Rajaei F, Cheng S, Williamson CA, Wittrup E, Najarian K. Diagnostics (Basel) 2023; 13(9).

Copyright

(Copyright © 2023, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/diagnostics13091640

PMID

37175031

PMCID

PMC10177859

Abstract

Traumatic brain injury (TBI) is one of the major causes of disability and mortality worldwide. Rapid and precise clinical assessment and decision-making are essential to improve the outcome and the resulting complications. Due to the size and complexity of the data analyzed in TBI cases, computer-aided data processing, analysis, and decision support systems could play an important role. However, developing such systems is challenging due to the heterogeneity of symptoms, varying data quality caused by different spatio-temporal resolutions, and the inherent noise associated with image and signal acquisition. The purpose of this article is to review current advances in developing artificial intelligence-based decision support systems for the diagnosis, severity assessment, and long-term prognosis of TBI complications.

Language: en

Keywords

prognosis; artificial intelligence; traumatic brain injury; diagnosis; decision support systems; severity assessment