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Systematic Review of Artificial Intelligence for Abnormality Detection in High-volume Neuroimaging and Subgroup Meta-analysis for Intracranial Hemorrhage Detection

School of Biomedical Engineering & Imaging Sciences, King's College London, Rayne Institute, 4th Floor, Lambeth Wing, SE1 7EH, London, UK. siddharth.1.agarwal@kcl.ac.uk. School of Biomedical Engineering & Imaging Sciences, King's College London, Rayne Institute, 4th Floor, Lambeth Wing, SE1 7EH, London, UK. Leicester Medical School, University of Leicester, LE1 7RH, Leicester, UK. Centre for Medical Image Computing, Department of Computer Science, University College London, WC1V 6LJ, London, UK. School of Biomedical Engineering & Imaging Sciences, King's College London, Rayne Institute, 4th Floor, Lambeth Wing, SE1 7EH, London, UK. thomas.booth@kcl.ac.uk. Department of Neuroradiology, Ruskin Wing, King's College Hospital NHS Foundation Trust, SE5 9RS, London, UK. thomas.booth@kcl.ac.uk.

Clinical neuroradiology. 2023;:1-14
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Abstract
PURPOSE Most studies evaluating artificial intelligence (AI) models that detect abnormalities in neuroimaging are either tested on unrepresentative patient cohorts or are insufficiently well-validated, leading to poor generalisability to real-world tasks. The aim was to determine the diagnostic test accuracy and summarise the evidence supporting the use of AI models performing first-line, high-volume neuroimaging tasks. METHODS Medline, Embase, Cochrane library and Web of Science were searched until September 2021 for studies that temporally or externally validated AI capable of detecting abnormalities in first-line computed tomography (CT) or magnetic resonance (MR) neuroimaging. A bivariate random effects model was used for meta-analysis where appropriate. This study was registered on PROSPERO as CRD42021269563. RESULTS Out of 42,870 records screened, and 5734 potentially eligible full texts, only 16 studies were eligible for inclusion. Included studies were not compromised by unrepresentative datasets or inadequate validation methodology. Direct comparison with radiologists was available in 4/16 studies and 15/16 had a high risk of bias. Meta-analysis was only suitable for intracranial hemorrhage detection in CT imaging (10/16 studies), where AI systems had a pooled sensitivity and specificity 0.90 (95% confidence interval [CI] 0.85-0.94) and 0.90 (95% CI 0.83-0.95), respectively. Other AI studies using CT and MRI detected target conditions other than hemorrhage (2/16), or multiple target conditions (4/16). Only 3/16 studies implemented AI in clinical pathways, either for pre-read triage or as post-read discrepancy identifiers. CONCLUSION The paucity of eligible studies reflects that most abnormality detection AI studies were not adequately validated in representative clinical cohorts. The few studies describing how abnormality detection AI could impact patients and clinicians did not explore the full ramifications of clinical implementation.
Study details
Study Design : Systematic Review
Language : eng
Credits : Bibliographic data from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine