Accuracy of artificial intelligence for the detection of intracranial hemorrhage and chronic cerebral microbleeds: a systematic review and pooled analysis
Matsoukas S, Scaggiante J, Schuldt BR, Smith CJ, Chennareddy S, Kalagara R, Majidi S, Bederson JB, Fifi JT, Mocco J, et al
La Radiologia medica. 2022
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.
Admission CT radiomic signatures outperform hematoma volume in predicting baseline clinical severity and functional outcome in the ATACH-2 trial intracerebral hemorrhage population
Haider SP, Qureshi AI, Jain A, Tharmaseelan H, Berson ER, Zeevi T, Majidi S, Filippi CG, Iseke S, Gross M, et al
European journal of neurology. 2021
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BACKGROUND Radiomics provides a framework for automated extraction of high-dimensional feature sets from medical images. We aimed to determine radiomics signature correlates of admission clinical severity and medium-term outcome from intracerebral hemorrhage (ICH) lesions on baseline head CTs. METHODS We used the ATACH-2 (Antihypertensive-Treatment-of-Acute-Cerebral-Hemorrhage-II) trial dataset. Patients included in this analysis (n=895) were randomly allocated to discovery (n=448) and independent validation (n=447) cohorts. We extracted 1130 radiomics features from hematoma lesions on baseline non-contrast head CTs and generated radiomics signatures associated with admission Glasgow Coma Scale (GCS), admission National Institutes of Health Stroke Scale (NIHSS), and 3-month modified Rankin Scale (mRS) scores. Spearman's correlation between radiomics signatures and corresponding target variables was compared with hematoma volume. RESULTS In the discovery cohort, radiomics signatures - compared to ICH volume - had significantly stronger association with admission GCS (0.47 vs 0.44, p=0.008), admission NIHSS (0.69 vs 0.57, p<0.001), and 3-month mRS scores (0.44 vs 0.32, p<0.001). Similarly, in independent validation, radiomics signatures - compared to ICH volume - had significantly stronger association with admission GCS (0.43 vs 0.41, p=0.02), NIHSS (0.64 vs 0.56, p<0.001), and 3-month mRS scores (0.43 vs 0.33, p<0.001). In multiple regression analysis adjusted for known predictors of ICH outcome, the radiomics signature was an independent predictor of 3-month mRS in both cohorts. CONCLUSIONS Limited by the enrollment criteria of the ATACH-2 trial, we showed that radiomics features quantifying hematoma texture, density and shape on baseline CT can provide imaging correlates for clinical presentation and medium-term outcome.