2,500 speckle-tracking echocardiography studies with global longitudinal strain values

Overview

Global longitudinal strain (GLS) derived from speckle-tracking echocardiography (STE) is an emerging biomarker for subclinical left ventricular dysfunction, cardiotoxicity monitoring in oncology patients, and early cardiomyopathy detection before overt systolic impairment develops. We are building a regression model that predicts GLS directly from standard B-mode apical cine loops, eliminating the dependency on proprietary vendor speckle-tracking software and enabling GLS estimation at sites without dedicated post-processing workstations. We require high-quality B-mode cine-loop acquisitions from the apical 4-chamber, apical 2-chamber, and apical 3-chamber (apical long-axis, A3C) views, captured at a frame rate of at least 50–80 frames per second to ensure adequate speckle coherence and tracking stability across frames throughout the cardiac cycle. Spatial resolution should be 600×800 pixels or higher. Each study should provide at least five consecutive cardiac cycles free from respiratory motion artefact and with consistent probe position. DICOM files with uncompressed or losslessly compressed pixel data are mandatory; lossy JPEG compression must not be applied, as it irreversibly degrades the high-frequency speckle patterns that are critical for accurate myocardial tracking and strain computation. The mandatory label for each study is the GLS value expressed as a negative percentage (for example −18.5%) computed by the acquiring institution using their validated STE software platform — EchoPAC, TOMTEC 2D Cardiac Performance Analysis, or an equivalent vendor-validated tool — with the software name and version number recorded in the accompanying JSON metadata sidecar file. Segmental longitudinal strain values for all 18 ASE myocardial segments are requested where available to support regional dysfunction mapping. Segmentation masks of the myocardial wall delineating both the endocardial and epicardial borders at end-diastole are requested for a minimum of 500 studies to support geometric normalisation and wall-thickness estimation experiments. Oncology patients undergoing anthracycline chemotherapy or trastuzumab (Herceptin) therapy represent a particularly valuable subpopulation for cardiotoxicity surveillance applications; institutions are encouraged to flag such cases with a treatment-context label — drug class, cumulative dose, and number of cycles completed — while fully preserving patient anonymity in compliance with HIPAA and GDPR requirements. Baseline and follow-up studies from the same pseudonymised patient are highly sought. Demographic balance across age decades (30–49, 50–69, 70+), biological sex, and underlying cardiomyopathy aetiology (ischaemic, dilated, hypertrophic, normal) should be targeted to ensure model generalisability.

Progress

Data Specifications

Use Cases

• Training and validating Medical imaging AI/ML models
• Benchmarking Medical imaging detection and segmentation algorithms
• Building de-identified Medical imaging research datasets for academic studies
• Augmenting existing Medical imaging datasets to reduce class imbalance