Artificial Intelligence Across the Cardiac Amyloidosis Diagnostic Pathway: From Single-Modality Detection to Multimodal Clinical Integration

2026-07-10

AI Summary

Cardiac amyloidosis is frequently underdiagnosed due to phenotypic overlap with other cardiomyopathies, and its subtype differentiation and management require integration of multimodal evidence. This study presents the first clinically oriented systematic review of artificial intelligence applications in screening, detection, quantification, prognosis, and treatment response monitoring for this condition, encompassing multimodal data including electrocardiography, echocardiography, electronic health records, cardiac magnetic resonance, and SPECT/CT. The analysis reveals substantial differences across clinical tasks in terms of data requirements, evaluation metrics, and technological maturity: binary detection and AI-assisted quantification on bone scintigraphy and SPECT/CT are closest to clinical translation, whereas subtype classification, risk stratification, and therapy response assessment remain limited by small sample sizes and insufficient validation, necessitating real-world calibration and prospective evaluation.

Abstract

Cardiac amyloidosis (CA) is increasingly recognized but remains substantially underdiagnosed, because its clinical and imaging phenotype overlaps with more common cardiomyopathies. Definitive subtype assignment and management further require integration of multimodal evidence to distinguish transthyretin from light chain disease. Machine learning and deep learning have been applied across the diagnostic and management pathway. These applications span ECG, echocardiography, and health record-based case finding, as well as CMR and nuclear interpretation, including SPECT/CT biomarker quantification, prognostic modeling, and treatment response assessment. This narrative review synthesizes these studies by clinical tasks, namely screening, detection, quantification, prognosis, and treatment response monitoring, rather than by input modality. This task-based organization clarifies why apparently similar AI models require different cohorts, reference standards, evaluation metrics, and implementation thresholds. The evidence reveals a maturity gradient. Binary detection and AI assisted quantification on bone scintigraphy and SPECT/CT are closest to clinical translation. Detection is supported by large externally validated cohorts, and quantification by interpretable, outcome linked measurement of myocardial tracer burden. By contrast, subtype aware classification, prognostic risk stratification, and treatment response monitoring remain at an early stage. These tasks are limited by small cohorts, enriched retrospective designs, heterogeneous labels, incomplete external validation, and uncertain calibration in realistic prevalence settings. Across tasks, high discrimination alone is insufficient.

Problem

• cardiac amyloidosis
• underdiagnosis
• subtype classification
• multimodal integration
• diagnostic pathway

Innovation

• task-based AI framework
• multimodal integration
• myocardial tracer quantification
• subtype-aware classification
• clinical translation