SynthesisJournal of imaging informatics in medicine2026
Accuracy of AI-Based Algorithms in Pulmonary Embolism Detection on Computed Tomographic Pulmonary Angiography: An Updated Systematic Review and Meta-analysis.
Synthesis in Journal of imaging informatics in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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12 authors.
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Abstract
Several artificial intelligence (AI) algorithms have been designed for detection of pulmonary embolism (PE) using computed tomographic pulmonary angiography (CTPA). Due to the rapid development of this field and the lack of an updated meta-analysis, we aimed to systematically review the available literature about the accuracy of AI-based algorithms to diagnose PE via CTPA. We searched EMBASE, PubMed, Web of Science, and Cochrane for studies assessing the accuracy of AI-based algorithms. Studies that reported sensitivity and specificity were included. The R software was used for univariate meta-analysis and drawing summary receiver operating characteristic (sROC) curves based on bivariate analysis. To explore the source of heterogeneity, sub-group analysis was performed (PROSPERO: CRD42024543107). A total of 1722 articles were found, and after removing duplicated records, 1185 were screened. Twenty studies with 26 AI models/population met inclusion criteria, encompassing 11,950 participants. Univariate meta-analysis showed a pooled sensitivity of 91.5% (95% CI 85.5-95.2) and specificity of 84.3 (95% CI 74.9-90.6) for PE detection. Additionally, in the bivariate sROC, the pooled area under the curved (AUC) was 0.923 out of 1, indicating a very high accuracy of AI algorithms in the detection of PE. Also, subgroup meta-analysis showed geographical area as a potential source of heterogeneity where the I
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