Diagnosis of hypertrophic cardiomyopathy by artificial intelligence using standard transthoracic echocardiography

Misdiagnosis of hypertrophic cardiomyopathy (HCM) is not uncommon among patients with left ventricular hypertrophy (LVH).

We hypothesized that a convolutional neural network (CNN) could be trained to identify HCM patients among individuals with LVH using standard transthoracic echocardiography (TTE).

Echocardiographic reports of 121,767 unique patients linked to clinical data from the largest tertiary medical centre in the country (2007-2022) were reviewed. Patients with no septal hypertrophy were excluded. All HCM cases were retrieved from electronic medical records and were adjudicated by the authors. Data was then divided into 3 mutually exclusive cohorts: training (N=7,368), testing (N=2,456) and validation (2,456) cohorts. Deep learning models were used in order to train a model that identifies HCM patients. The model was based solely on parasternal long axis and apical four chamber views.

Final study population included 12,281 patients who underwent a total of 27,242 echocardiography studies. There were 1535 (12%) patients with HCM. Mean age of study population was 72±14 and 7,943 (65%) were men. Overall, the model obtained 98.1% classification accuracy with an area under the curve of 0.851 in the independent test data (FIGURE) which could translate to sensitivity and specificity of 68% and 99%, respectively with positive and negative predictive values of 50% and 99%, respectively.

Applying artificial intelligence to the standard TTE can identify HCM patients with a reasonable accuracy. With further evaluation, this tool can be used to assist in HCM diagnosis.