Automated machine learning of echocardiographic strain enables identification of early myocardial changes in pre-symptomatic <i>TTR</i> carriers

Machine learning applied to routinely acquired echocardiographic data can identify subtle myocardial abnormalities associated with <i>TTR</i> V142I carrier status prior to development of CA. Key model features are physiologically relevant to known echocardiographic characteristics of overt CA. Genotype-guided echocardiographic surveillance may be a scalable strategy for early detection of CA risk.