5,000 transthoracic echocardiogram studies with LVEF measurements for heart-failure screening AI

Overview

We are developing a deep-learning model to automate left ventricular ejection fraction (LVEF) estimation from transthoracic echocardiography (TTE) studies acquired in routine clinical care. The primary intended use case is population-level heart-failure screening integrated into existing cardiology workflows, reducing the reporting burden on sonographers and cardiologists while enabling earlier intervention in at-risk patients. We require full cine-loop acquisitions from the apical 4-chamber (A4C) and apical 2-chamber (A2C) views, recorded at a minimum of 25 frames per second with spatial resolution no lower than 224×224 pixels. Each study should include at least three complete cardiac cycles per view. Harmonic B-mode imaging is preferred over fundamental mode to improve endocardial border delineation. Data must be delivered in DICOM format with all protected health information removed or replaced per HIPAA Safe Harbor or equivalent GDPR pseudonymisation protocols, including stripping of DICOM tags 0010,0010 through 0010,0040 and any burned-in annotation text. A de-identification manifest confirming the specific method applied is required alongside each batch delivery. Clinical labels must include a cardiologist-verified LVEF value measured by the biplane Simpson's method of discs, New York Heart Association (NYHA) functional class where available, and a binary heart-failure diagnosis flag. Segmentation masks delineating the left ventricular endocardial border at end-diastole and end-systole in the A4C view are strongly preferred for the full dataset and mandatory for at least 30% of studies. Additional chamber measurements — left ventricular end-diastolic volume (LVEDV), end-systolic volume (LVESV), left atrial volume index, and diastolic function grade per ASE 2016 guidelines — are welcomed as supplementary labels to broaden the model's downstream applicability. Patient demographic metadata including age decade, biological sex, body mass index category, and primary diagnosis (heart failure with reduced ejection fraction HFrEF, heart failure with preserved ejection fraction HFpEF, or no heart failure) should be retained in anonymised DICOM tags or an accompanying JSON sidecar. Studies from multiple scanner vendors — GE Vivid, Philips EPIQ, Siemens Acuson, and Canon Aplio — are explicitly sought to ensure device-agnostic model generalisation. Quality control: studies with greater than 20% of frames degraded by ultrasound dropout, rib shadow, or patient motion artefact should be excluded or flagged with a quality score below threshold. A sonographer-assigned image quality rating (1 poor, 2 adequate, 3 good) is requested for each cine loop.

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