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PhysioNet News

Delays in reviewing applications for credentialed access PhysioNet as a global platform for biomedical research The Open-Source Engine Behind Modern AI in Medicine – MIT Jameel Clinic Bridge2AI-Voice Adult Cohort Audio Dataset Bridge2AI-Voice Pediatric Cohort Audio Dataset George B. Moody PhysioNet Challenge Seeking Applications for Exceptional Candidates for the Director, National Institute of General Medical Sciences (NIGMS), NIH Use of MIMIC Data with Large Language Models and Online Services Roger Mark and George Moody Receive the 2026 IEEE Biomedical Engineering Award Access Restrictions Under DOJ Data Security Program This repository is under review by NIH for potential modification in compliance with U.S. federal Administration directives. A Dataset for Addressing Patient's Information Needs related to Clinical Course of Hospitalization v1.3 The George B. Moody PhysioNet Challenge 2025 has begun An ethically-sourced, diverse voice dataset linked to health information v3.1.0 MIMIC-IV v3.1 is now available on BigQuery Upgrading MIMIC-IV on BigQuery Delays in reviewing applications for credentialed access George B. Moody PhysioNet Challenge Guidelines for creating derived datasets and models Network issues at MIT, impacting the availability of PhysioNet Duke Critical Care Datathon: 13-14 April 2024 CHIL 2024: Submit your paper by Friday, 16 February 7th Annual Conference on Health, Inference, and Learning George B. Moody PhysioNet Challenge 2024: Challenge Opening Open Source and Validated Computational Tools for Physiological Time Series Analysis SNOMED CT Entity Linking Benchmark RECRUITMENT | inside-heart DARPA Triage Challenge: Qualification extended through Nov 27 Triage Challenge | DARPA Call for partners interested in synthetic patient data
George B. Moody PhysioNet Challenge 2024: Challenge Opening
2024-03-14 · via PhysioNet News

Jan. 26, 2024

We are delighted to announce the opening of the George B. Moody PhysioNet Challenge 2024. The 2024 Challenge invites teams to develop algorithms for digitizing and classifying electrocardiograms (ECGs) captured from images or paper printouts.

Despite recent advances in digital ECG devices, paper or physical ECGs remain common, especially in the Global South. These paper ECGs document the history and diversity of cardiovascular diseases (CVDs), and algorithms that can digitize and classify these images have the potential to improve our understanding and treatment of CVDs, especially for underrepresented and underserved populations.

We have shared example code and scoring code in both MATLAB and Python and synthetic ECG generation code in Python. While last year’s Challenge had the largest dataset yet, this year’s Challenge begins with a much more tractable dataset that you may already have on your machine, and you can use the provided code to create ECG images with realistic artifacts. We will also augment these data to create a much richer and more representative dataset, so stay tuned for more announcements. We will open the scoring system in the coming days.

See the Challenge website for more information, rules and deadlines: https://physionetchallenges.org/2024/

As in previous years, we have divided the Challenge into two phases: an unofficial phase and an official phase. The unofficial phase solicits feedback from the research community (i.e., you) to help us to improve the Challenge for the official phase, so 

we require teams to register and participate in the unofficial phase of the Challenge to be eligible for a prize

. Please enter early and often – we need you to look for quirks in our data, our scoring system, and otherwise. We are imperfect (and bandwidth-limited), so please send us suggestions via the forum (see below). We rely on the community to help us to improve the quality of the Challenge each year.

More information will be posted on the PhysioNet Challenge website and the Challenge forum as it becomes available. Please post questions and comments to the Challenge forum as well. However, if your question reveals information about your entry, then please email info [at] physionetchallenge.org instead to help us safeguard the diversity of approaches to the Challenge. We may post parts of our replies publicly if we feel that all Challengers should benefit from the information contained in our responses. We will not answer emails about the Challenge sent to other email addresses.

Many thanks again for your continued support of this event, and we hope that you enjoy the 2024 Challenge!