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New devices and techniques
Review
Review of deep learning algorithms for the automatic detection of intracranial hemorrhages on computed tomography head imaging
  1. Melissa Yeo1,
  2. Bahman Tahayori2,3,
  3. Hong Kuan Kok4,5,
  4. Julian Maingard5,6,
  5. Numan Kutaiba7,
  6. Jeremy Russell8,
  7. Vincent Thijs9,10,
  8. Ashu Jhamb11,
  9. Ronil V Chandra6,12,
  10. Mark Brooks9,13,
  11. Christen D. Barras14,15,
  12. Hamed Asadi9,12
  1. 1 Melbourne Medical School, The University of Melbourne Faculty of Medicine Dentistry and Health Sciences, Melbourne, Victoria, Australia
  2. 2 Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia
  3. 3 IBM Research Australia, Melbourne, Victoria, Australia
  4. 4 Department of Radiology, Northern Health, Epping, Victoria, Australia
  5. 5 School of Medicine, Deakin University Faculty of Health, Burwood, Victoria, Australia
  6. 6 Interventional Neuroradiology Unit, Monash Health, Clayton, Victoria, Australia
  7. 7 Department of Radiology, Austin Health, Heidelberg, Victoria, Australia
  8. 8 Department of Neurosurgery, Austin Health, Heidelberg, Victoria, Australia
  9. 9 Stroke Theme, Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia
  10. 10 Department of Neurology, Austin Health, Heidelberg, Victoria, Australia
  11. 11 Department of Radiology, St Vincent's Hospital Melbourne Pty Ltd, Fitzroy, Victoria, Australia
  12. 12 Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
  13. 13 Interventional Neuroradiology Service, Austin Health, Heidelberg, Victoria, Australia
  14. 14 School of Medicine, The University of Adelaide, Adelaide, South Australia, Australia
  15. 15 South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
  1. Correspondence to Melissa Yeo, Melbourne Medical School, The University of Melbourne Faculty of Medicine Dentistry and Health Sciences, Melbourne, VIC 3010, Australia; melissayeoxw{at}gmail.com

Abstract

Artificial intelligence is a rapidly evolving field, with modern technological advances and the growth of electronic health data opening new possibilities in diagnostic radiology. In recent years, the performance of deep learning (DL) algorithms on various medical image tasks have continually improved. DL algorithms have been proposed as a tool to detect various forms of intracranial hemorrhage on non-contrast computed tomography (NCCT) of the head. In subtle, acute cases, the capacity for DL algorithm image interpretation support might improve the diagnostic yield of CT for detection of this time-critical condition, potentially expediting treatment where appropriate and improving patient outcomes. However, there are multiple challenges to DL algorithm implementation, such as the relative scarcity of labeled datasets, the difficulties in developing algorithms capable of volumetric medical image analysis, and the complex practicalities of deployment into clinical practice. This review examines the literature and the approaches taken in the development of DL algorithms for the detection of intracranial hemorrhage on NCCT head studies. Considerations in crafting such algorithms will be discussed, as well as challenges which must be overcome to ensure effective, dependable implementations as automated tools in a clinical setting.

  • brain
  • CT
  • hemorrhage
  • stroke
  • technology

https://doi.org/10.1136/neurintsurg-2020-017099

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    Footnotes

    • Twitter @Dr Ashu Jhamb

    • Contributors Conceptualization: HA, MY. Literature review: MY. Manuscript draft preparation: MY. Review and editing: BT, HKK, JM, NK, JR, VT, AJ, RVC, MB, CB, HA. All authors reviewed and approved the final manuscript.

    • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

    • Competing interests None declared.

    • Patient consent for publication Not required.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.