Subtype specific artificial intelligence modelling of cardiac amyloidosis using echocardiography and electrocardiography.

AIMS: We aimed to develop and validate echocardiography-based prediction models for light chain (AL) and transthyretin (ATTR) cardiac amyloidosis and to quantify the incremental value of integrating artificial intelligence-derived electrocardiography (ECG) probabilities. METHODS AND RESULTS: We conducted a retrospective, multisite study within a single health system including patients with AL or ATTR cardiac amyloidosis and matched control subjects. AL and ATTR cohorts were handled independently. For each subtype, patients were randomly split into training and test cohorts, with an additional temporally distinct test cohort. Logistic regression models were developed using structured echocardiographic variables with parsimonious core and extended feature sets, as well as models integrating these features with a previously validated AI ECG probability. Model performance was assessed using receiver operating characteristic and precision-recall (PR) analyses. In AL amyloidosis, echocardiography-based models demonstrated moderate discrimination in the primary test cohort (area under the PR curve, AUPRC 0.674) but were inferior to ECG alone (AUPRC 0.824, P < 0.001), with no significant improvement from model combination (AUPRC 0.841 vs. ECG, P = 0.267). In contrast, for ATTR amyloidosis, echocardiography alone (AUPRC 0.687) performed worse than ECG (AUPRC 0.745, P = 0.140), while the combined model showed substantial improvement (AUROC 0.845, P < 0.001). Absolute performance declined in temporally distinct cohorts, but AUPRC comparisons were preserved. CONCLUSION: Subtype-specific echocardiography-based models demonstrate robust discrimination for cardiac amyloidosis, with divergent contributions of echocardiographic features by subtype. These results support tailored modelling strategies and prospective concurrent deployment of AL and ATTR models.