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Application of deep learning models for detection of subdural hematoma: a systematic review and meta-analysis
  1. Siddharth Agarwal1,
  2. David A Wood1,
  3. Marc Modat1,
  4. Thomas C Booth1,2
  1. 1 School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
  2. 2 Department of Neuroradiology, King's College Hospital NHS Foundation Trust, London, UK
  1. Correspondence to Dr Thomas C Booth, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK; thomas.booth{at}

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We read with interest the recent article by Abdollahifard et al.1 The ability of artificial intelligence (AI) to detect critical abnormalities, such as subdural hematomas, is relevant globally, not only for the potential gains in diagnostic accuracy as the authors discuss, but also for imaging triage.2 The authors have undertaken a considerable effort to screen 9485 abstracts. They report the accuracy of 22 deep learning algorithms that detect subdural hematomas and included 11 of these studies in a meta-analysis. We do, however, have concerns regarding which studies have been included and the risk of bias assessment.

Our biggest concern was that the authors have incorrectly conflated detection accuracy and segmentation accuracy. Detection is the ability of an AI model to be able to take an examination as an input and give a (usually) binary output …

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  • Contributors SA drafted the letter. All authors contributed to the response and approved the final version.

  • Funding SA is supported by an Engineering and Physical Sciences Research Council (EPSRC) funded PhD studentship (EP/R513064/1). TCB and MM are supported by the Wellcome/EPSRC Centre for Medical Engineering (WT 203148/Z/16/Z), the Royal College of Radiologists (TCB), and King’s College Hospital Research and Innovation (TCB).

  • Competing interests None declared.

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

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