Machine Learning Approaches in Diagnosis, Prognosis and Treatment Selection of Cardiac Amyloidosis

Area of Science:

• Cardiology
• Medical Imaging
• Artificial Intelligence

Background:

• Cardiac amyloidosis is a rare restrictive cardiomyopathy caused by amyloid protein deposition, leading to impaired heart function.
• Diagnosis is often delayed due to overlapping symptoms with more common hypertrophic diseases, leading to underdiagnosis.
• Accurate differentiation of amyloidosis subtypes is crucial for effective therapeutic strategies.

Purpose of the Study:

• To review current diagnostic approaches for cardiac amyloidosis.
• To assess the role of artificial intelligence (AI) and machine learning (ML) in improving early detection.
• To highlight the potential of automated algorithms in overcoming diagnostic challenges.

Main Methods:

• Review of existing literature on cardiac amyloidosis diagnosis.
• Analysis of clinical, electrocardiographic, and imaging findings.
• Evaluation of AI and ML computational techniques for data analysis and pattern recognition.

Main Results:

• Traditional diagnostic work-up involves identifying suggestive clinical and imaging features, often requiring histological confirmation.
• Automated diagnostic algorithms, particularly ML, can extract salient information from raw data without extensive pre-processing.
• AI offers a promising avenue to overcome the diagnostic delay associated with cardiac amyloidosis.

Conclusions:

• Early and accurate diagnosis of cardiac amyloidosis remains a significant clinical challenge.
• AI and ML techniques show potential to enhance diagnostic accuracy and timeliness.
• Further research into AI-driven diagnostic tools is warranted to improve patient outcomes and prognosis.