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This study evaluates deep learning (DL) algorithms that are playing an increasingly important role in automatic medical image analysis. The DL algorithm used was trained and externally evaluated on open-source, multi-centre retrospective data that contained radiologist-annotated non-contrast CT head studies. The authors concluded that the DL model has applications in a triage role with the potential to improve diagnostic yield and efficiency of CT reporting, resulting in expediting treatment and improving outcomes for intracranial haemorrhage.

Key points

  • The deep learning model detected intracranial haemorrhages on computed tomography with high accuracy.
  • Image preprocessing, such as windowing, plays a large role in improving deep learning model performance.
  • Implementations which enable an analysis of interslice dependencies can improve deep learning model performance.
  • Visual saliency maps can facilitate explainable artificial intelligence systems.
  • Deep learning within a triage system may expedite earlier intracranial haemorrhage detection.

Article: Evaluation of techniques to improve a deep learning algorithm for the automatic detection of intracranial haemorrhage on CT head imaging

Authors: Melissa Yeo, Bahman Tahayori, Hong Kuan Kok, Julian Maingard, Numan Kutaiba, Jeremy Russell, Vincent Thijs, Ashu Jhamb, Ronil V. Chandra, Mark Brooks, Christen D. Barras & Hamed Asadi

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