Accuracy of artificial intelligence for the detection of intracranial hemorrhage and chronic cerebral microbleeds: a systematic review and pooled analysis

Stavros Matsoukas, Jacopo Scaggiante, Braxton R. Schuldt, Colton J. Smith, Susmita Chennareddy, Roshini Kalagara, Shahram Majidi, Joshua B. Bederson, Johanna T. Fifi, J. Mocco, Christopher P. Kellner

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Background: Artificial intelligence (AI)-driven software has been developed and become commercially available within the past few years for the detection of intracranial hemorrhage (ICH) and chronic cerebral microbleeds (CMBs). However, there is currently no systematic review that summarizes all of these tools or provides pooled estimates of their performance. Methods: In this PROSPERO-registered, PRISMA compliant systematic review, we sought to compile and review all MEDLINE and EMBASE published studies that have developed and/or tested AI algorithms for ICH detection on non-contrast CT scans (NCCTs) or MRI scans and CMBs detection on MRI scans. Results: In total, 40 studies described AI algorithms for ICH detection in NCCTs/MRIs and 19 for CMBs detection in MRIs. The overall sensitivity, specificity, and accuracy were 92.06%, 93.54%, and 93.46%, respectively, for ICH detection and 91.6%, 93.9%, and 92.7% for CMBs detection. Some of the challenges encountered in the development of these algorithms include the laborious work of creating large, labeled and balanced datasets, the volumetric nature of the imaging examinations, the fine tuning of the algorithms, and the reduction in false positives. Conclusions: Numerous AI-driven software tools have been developed over the last decade. On average, they are characterized by high performance and expert-level accuracy for the diagnosis of ICH and CMBs. As a result, implementing these tools in clinical practice may improve workflow and act as a failsafe for the detection of such lesions. Registration-URL: https://www.crd.york.ac.uk/prospero/ Unique Identifier: CRD42021246848.

Original languageEnglish
Pages (from-to)1106-1123
Number of pages18
JournalRadiologia Medica
Volume127
Issue number10
DOIs
StatePublished - Oct 2022
Externally publishedYes

Keywords

  • AI-assisted diagnosis
  • Artificial intelligence
  • Chronic microbleeds
  • Convolutional neural network
  • Intracranial hemorrhage
  • Non-contrast CT scan

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