Echocardiogram Foundation Model -- Application 1: Estimating Ejection Fraction
Cardiovascular diseases stand as the primary global cause of mortality. Among the various imaging techniques available for visualising the heart and evaluating its function, echocardiograms emerge as the preferred choice due to their safety and low cost. Quantifying cardiac function based on echocardiograms is very laborious, time-consuming and subject to high interoperator variability. In this work, we introduce EchoAI, an echocardiogram foundation model, that is trained using self-supervised learning (SSL) on 1.5 million echocardiograms. We evaluate our approach by fine-tuning EchoAI to estimate the ejection fraction achieving a mean absolute percentage error of 9.40%. This level of accuracy aligns with the performance of expert sonographers.
Code (0)
등록된 구현이 없습니다.
Tasks
modelSelf-Supervised LearningSimilar Papers 제목 키워드 기반
Video-based AI for beat-to-beat assessment of cardiac function
Accurate assessment of cardiac function is crucial for the diagnosis of cardiovascular disease, screening for cardiotoxicity and decisions regarding the clinical management of patients with a critical illness. However, h…
LV SegmentationM(otion)-mode Based Prediction of Ejection Fraction using Echocardiograms
Early detection of cardiac dysfunction through routine screening is vital for diagnosing cardiovascular diseases. An important metric of cardiac function is the left ventricular ejection fraction (EF), where lower EF is …
Contrastive LearningDiagnosticEchoFlow: A Foundation Model for Cardiac Ultrasound Image and Video Generation
Advances in deep learning have significantly enhanced medical image analysis, yet the availability of large-scale medical datasets remains constrained by patient privacy concerns. We present EchoFlow, a novel framework d…
Medical Image AnalysisPrivacy PreservingVideo GenerationLightweight and Interpretable Left Ventricular Ejection Fraction Estimation using Mobile U-Net
Accurate LVEF measurement is important in clinical practice as it identifies patients who may be in need of life-prolonging treatments. This paper presents a deep learning based framework to automatically estimate left v…
D'ARTAGNAN: Counterfactual Video Generation
Causally-enabled machine learning frameworks could help clinicians to identify the best course of treatments by answering counterfactual questions. We explore this path for the case of echocardiograms by looking into the…
AnatomycounterfactualSSIMVideo Generation