Multimodal artificial intelligence to predict cardiomyopathy development in hereditary transthyretin amyloidosis

Background: Hereditary transthyretin amyloidosis (ATTRv) is a rare multisystem disorder with marked phenotypic heterogeneity. Cardiac involvement, a major prognostic determinant, often develops years after neurological onset and remains clinically silent in early stages, delaying diagnosis and treatment. We developed a multimodal artificial intelligence (AI) model integrating clinical, electrocardiographic and echocardiographic data to predict cardiomyopathy in patients with ATTRv but without baseline cardiac involvement. Methods: Adult patients with genetically confirmed ATTRv amyloidosis evaluated at four Italian tertiary referral centres between 2011 and 2026 were retrospectively included. Only patients without evidence of cardiac involvement at baseline were eligible. A machine learning model was developed using structured clinical variables, while deep learning models were trained on electrocardiographic and echocardiographic data. These models were integrated into a multimodal ensemble framework. Primary endpoint was development of ATTRv cardiomyopathy (ATTRv-CM), defined as scintigraphy-based diagnostic criteria for ATTR-CM in the absence of monoclonal gammopathy and evidence of left ventricular maximal wall thickness exceeding 12 mm. Results: The cohort included 97 patients (63 mutation carriers and 34 with only mild neurological involvement). Over a mean follow-up of 2.5±2.2 years (IQR 1–3), 15 patients developed cardiomyopathy. Unimodal models showed good predictive performance (all training set area under the receiver operating characteristic curve (AUC) in the range of 0.86–0.94, all test set AUC within 0.74–0.89). The multimodal ensemble achieved optimal performance, with 100% accuracy and an F1 score of 1.0, in predicting ATTR-CM. Conclusions: Multimodal AI enables early prediction of cardiac involvement in ATTRv, supporting personalised surveillance and optimised clinical management.