Differentiating hypertrophy phenocopies from afterload-induced left ventricular hypertrophy using artificial intelligence-enabled electrocardiography

Abstract Background Left ventricular hypertrophy (LVH) is mostly caused due to increased afterload, often caused by hypertension or valvular heart disease. However, it may also be caused by LVH-phenocopies, including hypertrophic cardiomyopathy (HCM), cardiac amyloidosis (CA), or Anderson-Fabry disease (AFD). Distinguishing these etiologies is clinically crucial, as phenocopies require advanced diagnostics to enable appropriate risk stratification, genetic counseling, and timely initiation of disease-specific therapies. While existing artificial intelligence-enabled electrocardiography (AI-ECG) algorithms can detect HCM and CA individually, there remains a clinical need for a tool capable of differentiating these phenocopies from other causes of LVH in broader clinical practice. Purpose We aimed to develop and validate a deep-learning algorithm that differentiaties LVH-phenocopies from afterload-associated LVH using 12-lead ECG-data. Methods We retrospectively analysed paired ECG and transthoracic echocardiography (TTE) data from patients with afterload-associated LVH and LVH phenocopies at a Dutch academic centre. Additional LVH-phenocopy cases were included from two other Dutch academic centres and one Italian academic centre. A deep convolutional neural network, fine-tuned from the pre-trained ECGFounder model, was trained to discriminate LVH phenocopies from afterload-associated LVH. The model was evaluated in a multicentre cohort of patients with established LVH, defined as a maximal wall thickness ≥11 mm and/or an increased left ventricular mass index. This cohort solely included individuals with severe hypertension (defined by a history of hypertension-mediated organ damage or hypertensive emergency), moderate-to-severe aortic stenosis, and confirmed LVH phenocopies. All available ECGs were used for training, validation, and testing, with a strict per-patient split to prevent data leakage. Results The model was trained on 68,075 ECGs from 33,525 patients and validated on 5,625 ECGs from 2,245 patients with LVH (median age 58 [IQR 49–69], 58% male). In the test cohort, 860 patients (38%) had severe hypertension and 680 (30%) had moderate-to-severe aortic stenosis. LVH phenocopies included HCM (n=830, 37%), CA (n=103, 5%), and AFD (n=10, 0.4%). The algorithm achieved an area under the receiver operating characteristic curve (AUC) of 0.85 (95% CI 0.84–0.87) for distinguishing phenocopies from afterload-associated LVH. Conclusion Our convolutional neural network accurately distinguishes LVH-phenocopies from afterload-associated LVH within a multicentre framework. Its use may facilitate earlier identification of rare cardiomyopathies and support more efficient, precision-guided diagnostic pathways.Graphical AbstractFor image description, please refer to the figure legend and surrounding text.