Why the study?
Transthyretin amyloid cardiomyopathy is often underdiagnosed, and early detection is essential to enable timely therapy.
Does a deep-learning 12-lead ECG model accurately detect transthyretin amyloid cardiomyopathy (ATTR-CM)?
Observational
Yes
Does a deep-learning 12-lead ECG model accurately detect transthyretin amyloid cardiomyopathy (ATTR-CM)?
A deep-learning model for 12-lead ECGs is being validated as a scalable, low-cost strategy for the early detection of transthyretin amyloid cardiomyopathy.
Answers are grounded in this paper and cite their sources.
AI-ECG screening for ATTR-CM requires prospective validation; leaves open its role in routine detection.
Transthyretin amyloid cardiomyopathy (ATTR-CM) is often underdiagnosed, yet early detection is essential to enable timely therapy. Artificial intelligence (AI)-enabled analysis of the 12-lead electrocardiogram (ECG) offers a scalable, low-cost strategy to identify at-risk patients. We present preliminary results from CONCERTO, an external validation study evaluating the performance of a deep-learning ECG model for ATTR-CM detection.
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Ahmad et al. (2026) conducted an observational in Transthyretin amyloid cardiomyopathy (ATTR-CM). Deep-learning ECG model was evaluated on ATTR-CM detection.
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