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Review
. 2025 Mar 30;14(7):2377.
doi: 10.3390/jcm14072377.

Diagnostic Accuracy of Deep Learning for Intracranial Hemorrhage Detection in Non-Contrast Brain CT Scans: A Systematic Review and Meta-Analysis

Armin Karamian  1 , Ali Seifi  2
Affiliations
Review

Diagnostic Accuracy of Deep Learning for Intracranial Hemorrhage Detection in Non-Contrast Brain CT Scans: A Systematic Review and Meta-Analysis

Armin Karamian et al. J Clin Med. .

Abstract

Background: Intracranial hemorrhage (ICH) is a life-threatening medical condition that needs early detection and treatment. In this systematic review and meta-analysis, we aimed to update our knowledge of the performance of deep learning (DL) models in detecting ICH on non-contrast computed tomography (NCCT). Methods: The study protocol was registered with PROSPERO (CRD420250654071). PubMed/MEDLINE and Google Scholar databases and the reference section of included studies were searched for eligible studies. The risk of bias in the included studies was assessed using the QUADAS-2 tool. Required data was collected to calculate pooled sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with the corresponding 95% CI using the random effects model. Results: Seventy-three studies were included in our qualitative synthesis, and fifty-eight studies were selected for our meta-analysis. A pooled sensitivity of 0.92 (95% CI 0.90-0.94) and a pooled specificity of 0.94 (95% CI 0.92-0.95) were achieved. Pooled PPV was 0.84 (95% CI 0.78-0.89) and pooled NPV was 0.97 (95% CI 0.96-0.98). A bivariate model showed a pooled AUC of 0.96 (95% CI 0.95-0.97). Conclusions: This meta-analysis demonstrates that DL performs well in detecting ICH from NCCTs, highlighting a promising potential for the use of AI tools in various practice settings. More prospective studies are needed to confirm the potential clinical benefit of implementing DL-based tools and reveal the limitations of such tools for automated ICH detection and their impact on clinical workflow and outcomes of patients.

Keywords: ICH; NCCT; artificial intelligence; deep learning; intracranial hemorrhage; non-contrast computed tomography.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
PRISMA flow diagram.
Figure 2
Figure 2
Sensitivity and specificity of deep learning models in detecting intracranial hemorrhage on non-contrast computed tomography in included studies [7,19,20,24,25,27,28,29,32,33,35,36,37,38,39,40,42,45,46,47,48,49,50,51,53,55,56,57,58,59,60,61,62,65,66,67,68,69,72,73,74,75,76,78,80,81,82,84,85,86,87,88,89,90,91,92,94].
Figure 3
Figure 3
Positive and negative predictive values of deep learning models in detecting intracranial hemorrhage on non-contrast computed tomography in included studies [7,19,20,24,25,27,28,29,32,33,35,36,37,38,39,40,42,45,46,47,48,49,50,51,53,55,56,57,58,59,60,61,62,65,66,67,68,69,72,73,74,75,76,78,80,81,82,84,85,86,87,88,89,90,91,92,94].
Figure 4
Figure 4
Bivariate summary receiver operating characteristic (SROC) curve of deep learning models for detecting intracranial hemorrhage on non-contrast computed tomography in included studies.
Figure 5
Figure 5
Sensitivity and specificity of deep learning models in detecting intracranial hemorrhage on non-contrast computed tomography according to retrospective studies [7,19,24,25,27,28,29,32,33,35,36,38,39,40,42,45,46,47,53,56,57,58,59,60,61,62,65,66,68,69,72,73,75,78,80,81,82,84,85,86,87,88,89,90,91,92].
Figure 6
Figure 6
Positive and negative predictive values of deep learning models in detecting intracranial hemorrhage on non-contrast computed tomography according to retrospective studies [7,19,24,25,27,28,29,32,33,35,36,38,39,40,42,45,46,47,53,56,57,58,59,60,61,62,65,66,68,69,72,73,75,78,80,81,82,84,85,86,87,88,89,90,91,92].
Figure 7
Figure 7
Bivariate summary receiver operating characteristic (SROC) curve of deep learning models for detecting intracranial hemorrhage on non-contrast computed tomography according to retrospective studies.
Figure 8
Figure 8
Sensitivity and specificity of deep learning models in detecting intracranial hemorrhage on non-contrast computed tomography according to prospective studies [20,37,48,49,50,51,55,67,74,76,94].
Figure 9
Figure 9
Positive and negative predictive values of deep learning models in detecting intracranial hemorrhage on non-contrast computed tomography according to prospective studies [20,37,48,49,50,51,55,67,74,76,94].
Figure 10
Figure 10
Bivariate summary receiver operating characteristic (SROC) curve of deep learning models for detecting intracranial hemorrhage on non-contrast computed tomography according to prospective studies.
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