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New devices and techniques
Application of deep learning models for detection of subdural hematoma: a systematic review and meta-analysis
  1. Saeed Abdollahifard1,2,
  2. Amirmohammad Farrokhi1,2,
  3. Ashkan Mowla3
  1. 1 Medical School, Shiraz University of Medical Sciences, Shiraz, Iran
  2. 2 Center for Neuromodulation and Pain, Shiraz University of Medical Sciences, Shiraz, Iran
  3. 3 Neurological Surgery, University of Southern California, Los Angeles, California, USA
  1. Correspondence to Dr Ashkan Mowla, Neurological Surgery, University of Southern California, Los Angeles, CA 90033, USA; mowla_a{at}yahoo.com

Abstract

Background This study aimed to investigate the application of deep learning (DL) models for the detection of subdural hematoma (SDH).

Methods We conducted a comprehensive search using relevant keywords. Articles extracted were original studies in which sensitivity and/or specificity were reported. Two different approaches of frequentist and Bayesian inference were applied. For quality and risk of bias assessment we used Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2).

Results We analyzed 22 articles that included 1,997,749 patients. In the first step, the frequentist method showed a pooled sensitivity of 88.8% (95% confidence interval (CI): 83.9% to 92.4%) and a specificity of 97.2% (95% CI 94.6% to 98.6%). In the second step, using Bayesian methods including 11 studies that reported sensitivity and specificity, a sensitivity rate of 86.8% (95% CI: 77.6% to 92.9%) at a specificity level of 86.9% (95% CI: 60.9% to 97.2%) was achieved. The risk of bias assessment was not remarkable using QUADAS-2.

Conclusion DL models might be an appropriate tool for detecting SDHs with a reasonably high sensitivity and specificity.

  • Subdural
  • Intervention
  • Technology

Data availability statement

All data relevant to the study are included in the article or uploaded as supplementary information.

https://doi.org/10.1136/jnis-2022-019627

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    Data availability statement

    All data relevant to the study are included in the article or uploaded as supplementary information.

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    Footnotes

    • Contributors SA, AF and AM were involved in the process of idea development, writing the draft, and finalizing the manuscript. SA and AF gathered the data and analyzed the extracted data. SA, AF, and AM read the final version of the article and approved it. AM is the guarantor of this project.

    • 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 AM: Speakers Bureau/Consultant to Cerenovus, Stryker, Wallaby Medical, RapidAI, BALT USA, LLC. This study did not receive funds from these companies/institutes.

    • 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.

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