PanEcho

Perform a survival analysis based on the time-to-event (death event) for the subjects. Compare machine learning models to assess the likelihood of a death by heart failure condition. This can be used to help hospitals in assessing the severity of patients with cardiovascular diseases and heart failure condition.

A Toolkit to Benchmark Methods for Ring-Based Cardiovascular Metrics

An ECG Foundation Model: Boosting Masked ECG-Text Auto-Encoders as Discriminative Learners (ICML 2025)

R package for Cardiovascular Risk Dataset and Data generation script

? Code for "Neural network-based integration of polygenic and clinical information: Development and validation of a prediction model for 10 year risk of major adverse cardiac events in the UK Biobank cohort" ?

CardioPulse ❤️? is an Android app using machine learning to predict cardiovascular (heart) diseases. Created as a final year project by University of Sialkot students, it combines predictive modeling with Firebase cloud storage. The app offers personalized health assessments and intuitive tracking for early detection and better health outcomes.

Official implementation of Echo-DND: A Dual Noise Diffusion Model for Robust and Precise Left Ventricle Segmentation in Echocardiography

BD2K Workshop for exploring predictive variables in Cardiovascular Risk

Analysis of temporal network architectures for heart phase detection with echocardiogram imaging ???

A neural network for image segmentation of cardiovascular anatomies. MOVED to AIS Training Codeset, Jan 3, 2020.