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

Author

Listed:
  • 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
    Department of Neurosurgery, Kasturba Medical College, Manipal Academy of Higher Education, Manipal 576104, India)

  • 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
    Department of Biomedical Engineering, School of Science and Technology, SUSS University, 463 Clementi Road, Singapore 599491, Singapore
    Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan)

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.

Suggested Citation

  • Vidhya V. & Anjan Gudigar & U. Raghavendra & Ajay Hegde & Girish R. Menon & Filippo Molinari & Edward J. Ciaccio & U. Rajendra Acharya, 2021. "Automated Detection and Screening of Traumatic Brain Injury (TBI) Using Computed Tomography Images: A Comprehensive Review and Future Perspectives," IJERPH, MDPI, vol. 18(12), pages 1-29, June.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:12:p:6499-:d:575927
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    References listed on IDEAS

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    Cited by:

    1. Anjan Gudigar & Sneha Nayak & Jyothi Samanth & U Raghavendra & Ashwal A J & Prabal Datta Barua & Md Nazmul Hasan & Edward J. Ciaccio & Ru-San Tan & U. Rajendra Acharya, 2021. "Recent Trends in Artificial Intelligence-Assisted Coronary Atherosclerotic Plaque Characterization," IJERPH, MDPI, vol. 18(19), pages 1-27, September.

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