SynthesisFrontiers in cardiovascular medicine2026
Machine learning-based methods in diagnosing cardiac amyloidosis: a meta-analysis.
Synthesis in Frontiers in cardiovascular medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Artificial Intelligence for the Diagnosis of Transthyretin Amyloid Cardiomyopathy: A Systematic Review of Machine Learning Application.Cardiology research and practice · 2026Review
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4 authors.
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Abstract
Background: Cardiac amyloidosis (CA) is an infiltrative restrictive cardiomyopathy characterized by the deposition of Objectives: To explore the diagnostic accuracy of ML, providing evidence-based data to advance smart detection tools for CA. Methods: We searched the Cochrane Library, PubMed, Embase, and Web of Science up to September 25, 2025, adhering to PRISMA 2020 guidelines. Study quality was evaluated using the QUADAS-2 instrument. Subgroup analyses were stratified by disease type [light chain CA (AL-CA), transthyretin CA (ATTR-CA)] and imaging modality (echocardiography) to explore sources of heterogeneity and assess diagnostic performance across different clinical scenarios. Results: The current meta-analysis incorporated 30 studies. In validation sets, ML for overall CA showed sensitivity 0.87 [95% confidence interval (CI) 0.83-0.91], specificity 0.88 (95% CI: 0.81-0.92), positive likelihood ratio (PLR) 7.0 (95% CI: 4.4-11.4), negative likelihood ratio (NLR) 0.14 (95% CI: 0.10-0.20), and SROC AUC 0.93 (95% CI: 0.91-0.95). For AL-CA, ML demonstrated sensitivity 0.85 (95% CI: 0.76-0.91), specificity 0.82 (95% CI: 0.75-0.87), PLR 4.8 (95% CI: 3.4-6.7), NLR 0.18 (95% CI: 0.11-0.30), and SROC AUC 0.88 (95% CI: 0.85-0.91). For ATTR-CA, ML revealed sensitivity 0.84 (95% CI: 0.77-0.89), specificity 0.85 (95% CI: 0.78-0.91), PLR 5.7 (95% CI: 3.6-9.2), NLR 0.19 (95% CI: 0.12-0.28), and SROC AUC 0.91 (95% CI: 0.88-0.93). Echocardiography-only ML models showed sensitivity 0.83 (95% CI: 0.81-0.85), specificity 0.86 (95% CI: 0.82-0.89), PLR 5.9 (95% CI: 4.4-7.9), NLR 0.20 (95% CI: 0.17-0.23), and SROC AUC 0.88 (95% CI: 0.85-0.91). Conclusions: ML demonstrates favorable diagnostic accuracy for CA. Nevertheless, the aggregated findings warrant cautious interpretation owing to inherent methodological limitations in the existing evidence. Future investigations incorporating diverse cases from broader geographic regions are needed to further validate the diagnostic performance of ML for CA and to advance the subsequent development of assessment tools based on artificial intelligence. Systematic Review Registration: PROSPERO CRD42024536601.
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