A unified multi-task learning framework for automated assessment of left ventricular structure and its systolic function from echocardiography

The manual assessment of Left Ventricular (LV) structure and its systolic function from echocardiography is fundamental for cardiovascular diagnosis but is often time-consuming and subject to inter-observer variability. While deep learning has advanced the automation of individual echocardiographic tasks, the prevailing approach of developing separate models for functional and structural analysis fails to leverage the intrinsic relationship between these two aspects of cardiac health. We propose a novel, unified Multi-Task Learning (MTL) framework designed to simultaneously perform LV segmentation and anatomical keypoint detection from a single analysis. The model employs a shared EfficientNet encoder that feeds into two parallel, specialized heads, including a U-Net-style decoder for segmentation and a convolutional head for heatmap-based keypoint localization. The framework was trained and validated on three large-scale public datasets: CAMUS, EchoNet-Dynamic, and EchoNet-LVH. Our proposed framework achieved state-of-the-art performance on both tasks. For segmentation, the model reached a Dice Similarity Coefficient (DSC) of up to 0.951 on the CAMUS dataset and 0.931 on EchoNet-Dynamic. For keypoint detection, it achieved a low Mean Absolute Error (MAE) of ~ 1.13 pixels across all structural measurements on the EchoNet-LVH dataset. An ablation study also confirmed that the MTL approach synergistically improved the performance of both tasks compared to single-task models. By unifying segmentation with heatmap-based keypoint detection, this synergistic approach offers an efficient, accurate, and interpretable geometry-based alternative to existing systems that rely on direct regression or complex full-wall segmentation