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CC BY 4.0 · Indian Journal of Neurotrauma 2023; 20(02): 081-088
DOI: 10.1055/s-0043-1770770
Review Article

Automated Detection of Intracranial Hemorrhage from Head CT Scans Applying Deep Learning Techniques in Traumatic Brain Injuries: A Comparative Review

Deepak Agrawal 
1   Department of Neurosurgery, All India Institute of Medical Sciences, New Delhi, India
,
Latha Poonamallee 
2   Behavioral Sciences, In-Med Prognostics Inc, San Diego, California, United States
,
Sharwari Joshi 
3   Department of Research, In-Med Prognostics Inc, Pune, Maharashtra, India
› Author Affiliations Funding None.
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Abstract

Traumatic brain injury (TBI) is not only an acute condition but also a chronic disease with long-term consequences. Intracranial hematomas are considered the primary consequences that occur in TBI and may have devastating effects that may lead to mass effect on the brain and eventually cause secondary brain injury. Emergent detection of hematoma in computed tomography (CT) scans and assessment of three major determinants, namely, location, volume, and size, is crucial for prognosis and decision-making, and artificial intelligence (AI) using deep learning techniques, such as convolutional neural networks (CNN) has received extended attention after demonstrations that it could perform at least as well as humans in imaging classification tasks. This article conducts a comparative review of medical and technological literature to update and establish evidence as to how technology can be utilized rightly for increasing the efficiency of the clinical workflow in emergency cases. A systematic and comprehensive literature search was conducted in the electronic database of PubMed and Google Scholar from 2013 to 2023 to identify studies related to the automated detection of intracranial hemorrhage (ICH). Inclusion and exclusion criteria were set to filter out the most relevant articles. We identified 15 studies on the development and validation of computer-assisted screening and analysis algorithms that used head CT scans. Our review shows that AI algorithms can prioritize radiology worklists to reduce time to screen for ICH in the head scans sufficiently and may also identify subtle ICH overlooked by radiologists, and that automated ICH detection tool holds promise for introduction into routine clinical practice.

Keywords

intracranial hemorrhage - traumatic brain injury - deep learning - AI/ML - convolutional neural network - screening/detection tool - automated intracranial hemorrhage


Publication History

Article published online:
10 July 2023

© 2023. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

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