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Machine learning-based methods in diagnosing cardiac amyloidosis: a meta-analysis
Yuchen Song1, Qun Wang2, Lianqun Jia2,3
1College of Integrated Chinese and Western Medicine, Liaoning University of Traditional Chinese Medicine, Shenyang, Liaoning, China.
Insights
Machine learning (ML) shows good accuracy in diagnosing cardiac amyloidosis (CA), a condition often misdiagnosed as hypertrophic cardiomyopathy. Further research is needed to validate these findings for developing AI-driven diagnostic tools.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence
Background:
- Cardiac amyloidosis (CA) is a restrictive cardiomyopathy caused by amyloid deposition, often mimicking hypertrophic cardiomyopathy.
- Nonspecific early symptoms lead to misdiagnosis and delayed treatment for this progressive disease.
- Machine learning (ML) shows promise for CA diagnosis, but evidence on its accuracy is limited.
Purpose of the Study:
- To systematically evaluate the diagnostic accuracy of ML algorithms for CA.
- To provide evidence for developing intelligent CA detection tools.
Main Methods:
- A meta-analysis of 30 studies was conducted, searching major databases up to September 2025.
- Study quality was assessed using QUADAS-2.
- Subgroup analyses included disease types (AL-CA, ATTR-CA) and imaging modality (echocardiography).
Main Results:
- ML for overall CA demonstrated high diagnostic accuracy (AUC 0.93).
- Specific performance metrics (sensitivity, specificity, PLR, NLR) were reported for overall CA, AL-CA, ATTR-CA, and echocardiography-based models.
- ML models showed consistent diagnostic performance across different CA subtypes and imaging modalities.
Conclusions:
- ML exhibits favorable diagnostic accuracy for CA.
- Methodological limitations necessitate cautious interpretation of current findings.
- Further validation with diverse datasets is crucial for advancing AI-based CA diagnostic tools.
Background:
Cardiac amyloidosis (CA) is an infiltrative restrictive cardiomyopathy characterized by the deposition of β-fold amyloid, often presenting as left ventricular hypertrophy. Early nonspecific symptoms lead to frequent misdiagnosis as hypertrophic cardiomyopathy, delaying care for this progressive disease. While machine learning (ML) has been applied to the diagnosis of CA, systematic evidence of its accuracy remains lacking, hindering the development of intelligent detection tools.
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.