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Machine Learning in Neuroimaging of Traumatic Brain Injury: Current Landscape, Research Gaps, and Future Directions

Authors :
Kevin Pierre
Jordan Turetsky
Abheek Raviprasad
Seyedeh Mehrsa Sadat Razavi
Michael Mathelier
Anjali Patel
Brandon Lucke-Wold
Source :
Trauma Care, Vol 4, Iss 1, Pp 31-43 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

In this narrative review, we explore the evolving role of machine learning (ML) in the diagnosis, prognosis, and clinical management of traumatic brain injury (TBI). The increasing prevalence of TBI necessitates advanced techniques for timely and accurate diagnosis, and ML offers promising tools to meet this challenge. Current research predominantly focuses on integrating clinical data, patient demographics, lab results, and imaging findings, but there remains a gap in fully harnessing the potential of image features. While advancements have been made in areas such as subdural hematoma segmentation and prognosis prediction, the translation of these techniques into clinical practice is still in its infancy. This is further compounded by challenges related to data privacy, clinician trust, and the interoperability of various health systems. Despite these hurdles, FDA-approved ML applications for TBI and their subsequent promising results underscore the potential of ML in revolutionizing TBI care. This review concludes by emphasizing the importance of bridging the gap between theoretical research and real-world clinical application and the necessity of addressing the ethical and privacy implications of integrating ML into healthcare.

Details

Language :
English
ISSN :
2673866X
Volume :
4
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Trauma Care
Publication Type :
Academic Journal
Accession number :
edsdoj.b3befe1adef1418c86388e51ca523534
Document Type :
article
Full Text :
https://doi.org/10.3390/traumacare4010004