AI-Based Decision Support System for Traumatic Brain Injury: A Survey

Author:

Rajaei Flora1,Cheng Shuyang1,Williamson Craig A.23,Wittrup Emily1ORCID,Najarian Kayvan134567

Affiliation:

1. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA

2. Department of Neurosurgery, University of Michigan, Ann Arbor, MI 48109, USA

3. Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor, MI 48109, USA

4. Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI 48109, USA

5. Department of Emergency Medicine, University of Michigan, Ann Arbor, MI 48109, USA

6. Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA

7. Center for Data-Driven Drug Development and Treatment Assessment (DATA), University of Michigan, Ann Arbor, MI 48109, USA

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.

Publisher

MDPI AG

Subject

Clinical Biochemistry

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