Heart failure & cardiomyopathies
Multimodal Artificial Intelligence for Cardiac Amyloidosis Diagnosis: Integrating Echocardiography With Clinical and Laboratory Data for Improved Detection
Authors' conclusion
A multiparametric AI model integrating basic clinical, laboratory, and TTE data with the deep learning Us2.Ca improved performance for CA detection over Us2.Ca alone. This approach represents a step toward scalable, AI-guided precision diagnostics for CA in diverse populations.
Study context
- Population
- Patients with cardiac amyloidosis and controls from the Amyloidosis Imaging International Consortium727 patients with cardiac amyloidosis (CA) and 316 controls · patients with CA 727 · controls 316 · controls: suspected transthyretin-CA with negative diagnostic evaluation 202 · controls: biopsy-proven extracardiac light chain amyloidosis without cardiac involvement 114
- Comparison
- AI echo-clinical model (AI-ECM) vs Us2.Ca
Methodological detailsglobal multiethnic registry, internal validation
- Global multiethnic registry
- Internal validation
Performance
As reported in the abstract
Diagnostic performance for CA detection
| Test or model | Area under the curve | Sensitivity | Specificity | Accuracy |
|---|---|---|---|---|
| AI-ECM | 0.94 | 93% | 85% | 90% |
| Us2.Ca | 0.89 | 76% | 91% | 80% |
Area under the curve
AI-ECM0.94
Us2.Ca0.89
Sensitivity
AI-ECM93%
Us2.Ca76%
Specificity
AI-ECM85%
Us2.Ca91%
Accuracy
AI-ECM90%
Us2.Ca80%
- AI-ECM vs Us2.Ca: P = 0.006 — superior performance of AI-ECM
Further findings
| Outcome | Us2.Ca |
|---|---|
| Indeterminate classification | indeterminate cases 9% |
Feature importance
- Us2.Ca prediction scores, relative wall thickness, sex, and estimated glomerular filtration rate contributed most to performance
Indeterminate classification
- AI-ECM allowed classification of all cases
Subtypes and control groups
- AI-ECM improved sensitivity for light chain-CA detection and maintained high accuracy across subtypes and control groups
Original publication
Multimodal Artificial Intelligence for Cardiac Amyloidosis Diagnosis: Integrating Echocardiography With Clinical and Laboratory Data for Improved Detection. Circ Cardiovasc Imaging. Published 27 May 2026. doi:10.
Summarised from the published abstract; every number is checked against it