Automated Detection and Screening of Traumatic Brain Injury (TBI) Using Computed Tomography Images: A Comprehensive Review and Future Perspectives

Vidhya V., Anjan Gudigar, U. Raghavendra, Ajay Hegde, Girish R. Menon, Filippo Molinari, Edward J. Ciaccio and U. Rajendra Acharya
Additional contact information
Vidhya V.: Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
Anjan Gudigar: Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
U. Raghavendra: Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
Ajay Hegde: Institute of Neurological Sciences, Glasgow G51 4LB, UK
Girish R. Menon: Department of Neurosurgery, Kasturba Medical College, Manipal Academy of Higher Education, Manipal 576104, India
Filippo Molinari: Department of Electronics, Politecnico di Torino, 24 Corso Duca degli Abruzzi, 10129 Torino, Italy
Edward J. Ciaccio: Department of Medicine, Columbia University, New York, NY 10032, USA
U. Rajendra Acharya: School of Engineering, Ngee Ann Polytechnic, 535 Clementi Road, Singapore 599489, Singapore

IJERPH, 2021, vol. 18, issue 12, 1-29

Abstract: Traumatic brain injury (TBI) occurs due to the disruption in the normal functioning of the brain by sudden external forces. The primary and secondary injuries due to TBI include intracranial hematoma (ICH), raised intracranial pressure (ICP), and midline shift (MLS), which can result in significant lifetime disabilities and death. Hence, early diagnosis of TBI is crucial to improve patient outcome. Computed tomography (CT) is the preferred modality of choice to assess the severity of TBI. However, manual visualization and inspection of hematoma and its complications from CT scans is a highly operator-dependent and time-consuming task, which can lead to an inappropriate or delayed prognosis. The development of computer aided diagnosis (CAD) systems could be helpful for accurate, early management of TBI. In this paper, a systematic review of prevailing CAD systems for the detection of hematoma, raised ICP, and MLS in non-contrast axial CT brain images is presented. We also suggest future research to enhance the performance of CAD for early and accurate TBI diagnosis.

Keywords: traumatic brain injury (TBI); CAD; computed tomography; intracranial hematoma; elevated ICP; midline shift (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1660-4601/18/12/6499/pdf (application/pdf)
https://www.mdpi.com/1660-4601/18/12/6499/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:18:y:2021:i:12:p:6499-:d:575927

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().