Comprehensive Summary
This study explored how artificial intelligence (AI) can be used with electrocardiography (ECG) and echocardiography (ECHO) to track early, preclinical stages of transthyretin amyloid cardiomyopathy (ATTR-CM). Researchers trained and tested AI models using heart data from patients with confirmed ATTR-CM to see if the system could identify early heart changes before symptoms appeared. The AI detected subtle signs of disease progression, such as thickened heart walls and reduced heart strain, that are often missed by standard diagnostic tools. Overall, the results showed that AI-based ECG and ECHO could predict disease development earlier than current methods. The authors concluded that AI could play an important role in improving early diagnosis and guiding treatment decisions for patients at risk of cardiac amyloidosis.
Outcomes and Implications
Transthyretin amyloid cardiomyopathy is a serious and often underdiagnosed cause of heart failure. Because symptoms usually appear late in the disease, patients often miss the window for the most effective treatment. This research is important because it shows that AI could help catch the disease in its earliest stages using simple, noninvasive heart tests. If adopted in hospitals and clinics, AI-enabled ECG and ECHO could make screening more efficient and help doctors start treatment sooner. With continued testing and validation, this approach could become part of routine cardiac care within the next few years, improving outcomes for patients with or at risk of ATTR-CM.