Resources


Challenge Open Access

Classification of 12-lead ECGs: The PhysioNet/Computing in Cardiology Challenge 2020

Erick Andres Perez Alday, Annie Gu, Amit Shah, et al.

The goal of the 2020 PhysioNet - Computing in Cardiology Challenge is to design and implement a working, open-source algorithm that can automatically identify cardiac abnormalities in 12-lead ECG recordings.

Published: July 29, 2022. Version: 1.0.2

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Challenge Open Access

Will Two Do? Varying Dimensions in Electrocardiography: The PhysioNet/Computing in Cardiology Challenge 2021

Matthew Reyna, Nadi Sadr, Annie Gu, et al.

Will Two Do? Varying Dimensions in Electrocardiography: the PhysioNet/Computing in Cardiology Challenge 2021

challenge cardiac abnormalities multilead ecgs classification competition

Published: July 29, 2022. Version: 1.0.3

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Challenge Open Access

Will Two Do? Varying Dimensions in Electrocardiography: The PhysioNet/Computing in Cardiology Challenge 2021

Matthew Reyna, Nadi Sadr, Annie Gu, et al.

Will Two Do? Varying Dimensions in Electrocardiography: the PhysioNet/Computing in Cardiology Challenge 2021

challenge cardiac abnormalities multilead ecgs classification competition

Published: July 29, 2022. Version: 1.0.3

Visualize waveforms

Challenge Open Access

Heart Murmur Detection from Phonocardiogram Recordings: The George B. Moody PhysioNet Challenge 2022

Matthew Reyna, Yashar Kiarashi, Andoni Elola, et al.

2022 Physionet Challenge is devoted to detecting the presence or absence of murmurs from multiple heart sound recordings from multiple auscultation locations, as well as detecting the clinical outcomes.

challenge competition cardiac auscultation congenital heart diseases

Published: Sept. 28, 2023. Version: 1.0.0

Challenge Open Access

Heart Murmur Detection from Phonocardiogram Recordings: The George B. Moody PhysioNet Challenge 2022

Matthew Reyna, Yashar Kiarashi, Andoni Elola, et al.

2022 Physionet Challenge is devoted to detecting the presence or absence of murmurs from multiple heart sound recordings from multiple auscultation locations, as well as detecting the clinical outcomes.

challenge competition cardiac auscultation congenital heart diseases

Published: Sept. 28, 2023. Version: 1.0.0

Database Restricted Access

CheXchoNet: A Chest Radiograph Dataset with Gold Standard Echocardiography Labels

Pierre Elias, Shreyas Bhave

Early detection of heart failure is vital for improving outcomes. The dataset contains 71,589 CXRs paired with gold standard labels from echocardiograms to enable the training of models to detect pathologies indicative of early stage heart failure.

chest x-rays heart failure early detection cardiac structural abnormalties deep learning

Published: March 20, 2024. Version: 1.0.0

Database Open Access

Image-derived cardiomegaly biomarker values for 96K chest X-rays in MIMIC-CXR/MIMIC-CXR-JPG

Benjamin Duvieusart, Felix Krones, Guy Parsons, et al.

Automatically extracted cardiomegaly biomarkers - cardiothoracic ratio (CTR) and cardiopulmonary area ratio (CPAR) - for all posterior-anterior chest x-ray scans in MIMIC-CXR/MIMIC-CXR-JPG.

biomarkers mimic-cxr cpar ctr cardiomegaly

Published: Aug. 23, 2024. Version: 1.0.0

Database Credentialed Access

EchoNotes Structured Database derived from MIMIC-III (ECHO-NOTE2NUM)

Gloria Hyunjung Kwak, Dana Moukheiber, Mira Moukheiber, et al.

A structured echocardiogram database derived from 43,472 observational notes obtained during echocardiogram studies conducted in the intensive care unit at the Beth Israel Deaconess Medical Center between 2001 and 2012.

Published: Feb. 23, 2024. Version: 1.0.0