Artificial Intelligence in Cardiac Amyloidosis: A State-of-the-Art Review

Area of Science:

• Cardiology
• Medical Imaging
• Artificial Intelligence

Background:

• Cardiac amyloidosis (CA) is frequently underdiagnosed, often presenting with symptoms overlapping other heart conditions like hypertrophic cardiomyopathy.
• Diagnostic delays in CA occur because key indicators can be subtle, atypical, or missed, especially in early disease stages.

Purpose of the Study:

• To review the current applications of artificial intelligence (AI) in diagnosing cardiac amyloidosis (CA) using multimodal testing.
• To highlight AI's potential in overcoming diagnostic challenges associated with CA's subtle and overlapping clinical features.

Main Methods:

• Review of current literature on AI applications in cardiovascular imaging for CA diagnosis.
• Synthesis of AI's role in electrocardiography, echocardiography, cardiac magnetic resonance, and nuclear scintigraphy for CA evaluation.

Main Results:

• AI-enhanced electrocardiography shows high performance for scalable CA screening and early detection.
• AI in echocardiography improves standardization and reduces variability in video-based analysis.
• AI tools for cardiac MRI and nuclear imaging enable automated quantification and reproducible assessment of amyloid burden.

Conclusions:

• AI offers a powerful approach to detect subtle disease signatures in CA, integrating multimodal and longitudinal data.
• AI applications are advancing CA diagnosis, phenotyping, risk stratification, and treatment monitoring, leading to earlier and more accurate patient management.