This technology is a deep learning model, termed EchoNext, that uses standard electrocardiogram (ECG) data to predict multiple forms of structural heart disease.
Current methods for diagnosing structural heart diseases rely heavily on imaging techniques like echocardiography, which can be time-consuming, costly, and not readily accessible in all clinical settings. Although low-cost and accessible, standard electrocardiogram (ECG) analysis is limited in its ability to detect a full spectrum of heart diseases, often missing conditions like ventricular dysfunction and valvular diseases. There is a need for a more comprehensive, accessible, and efficient method to identify patients with latent structural heart diseases who would benefit from further diagnostic evaluation.
This technology utilizes advanced deep learning algorithms to analyze standard 12-lead ECG data, along with patient age, sex, and key ECG-derived measurements, to predict the presence of various structural heart diseases. By detecting subtle patterns in ECG signals indicative of conditions such as ventricular dysfunction, increased wall thickness, valvular diseases, pericardial effusion, and pulmonary hypertension, the model provides a comprehensive assessment from a single ECG test.
This technology was developed using a dataset of over 1 million ECGs.
Patent Pending
IR CU24199
Licensing Contact: Sara Gusik