Machine-learning algorithms for detecting intracranial hemorrhage on head computed tomography.
Authors
Affiliations (6)
Affiliations (6)
- Department of Diagnostic and Interventional Radiology, BG Klinikum Unfallkrankenhaus Berlin gGmbH, Berlin, Germany.
- Institute for Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany.
- Business Division Medicine, BG Kliniken Group - Hospitals of the Federal Statutory Accident Insurance, Berlin, Germany.
- Centre for Clinical Research, BG Klinikum Unfallkrankenhaus Berlin gGmbH, Berlin, Germany.
- Department of Hand-, Replantation-, and Microsurgery, BG Klinikum Unfallkrankenhaus Berlin gGmbH, Berlin, Germany.
- Department of Trauma and Orthopaedic Surgery, BG Klinikum Unfallkrankenhaus Berlin gGmbH, Berlin, Germany.
Abstract
This is a protocol for a Cochrane Review (diagnostic). The objectives are as follows: The primary objective of this review is to determine the diagnostic accuracy of any form of ML algorithm for detecting ICH in participants who underwent hCT. We will assess diagnostic accuracy by individual and pooled indicators such as sensitivity and specificity with 95% confidence intervals, positive and negative likelihood ratios, and the summary receiver operating characteristic curve. Secondary objectives To investigate the following potential sources of heterogeneity in the diagnostic accuracy of ML: type of ML algorithm; year of development of the ML algorithm; certification of ML algorithm (Yes/No); type of reference standard; participant age (adults only/children only/mixed); hCT quality; hCT protocol; type of study (retrospective cohort study/prospective cohort study/RCT).