Echocardiography is a cornerstone for managing heart failure (HF), with Left Ventricular Ejection Fraction (LVEF) being a critical metric for guiding therapy. However, manual LVEF assessment suffers from high inter-observer variability, while existing Deep Learning (DL) models are often computationally intensive and data-hungry "black boxes" that impede clinical trust and adoption. Here, we propose a backpropagation-free multi-task Green Learning (MTGL) framework that performs simultaneous Left Ventricle (LV) segmentation and LVEF classification. Our framework integrates an unsupervised VoxelHop encoder for hierarchical spatio-temporal feature extraction with a multi-level regression decoder and an XG-Boost classifier. On the EchoNet-Dynamic dataset, our MTGL model achieves state-of-the-art classification and segmentation performance, attaining a classification accuracy of 94.3% and a Dice Similarity Coefficient (DSC) of 0.912, significantly outperforming several advanced 3D DL models. Crucially, our model achieves this with over an order of magnitude fewer parameters, demonstrating exceptional computational efficiency. This work demonstrates that the GL paradigm can deliver highly accurate, efficient, and interpretable solutions for complex medical image analysis, paving the way for more sustainable and trustworthy artificial intelligence in clinical practice.

翻译：超声心动图是管理心力衰竭（HF）的基石，其中左心室射血分数（LVEF）是指导治疗的关键指标。然而，手动LVEF评估存在观察者间差异大的问题，而现有的深度学习（DL）模型通常是计算密集、数据需求大的“黑箱”，阻碍了临床信任与采用。本文提出一种无需反向传播的多任务绿色学习（MTGL）框架，可同时执行左心室（LV）分割与LVEF分类。该框架将用于分层时空特征提取的无监督VoxelHop编码器与多级回归解码器及XG-Boost分类器相结合。在EchoNet-Dynamic数据集上，我们的MTGL模型实现了最先进的分类与分割性能，分类准确率达94.3%，Dice相似系数（DSC）达0.912，显著优于多个先进的3D DL模型。关键在于，本模型以超过一个数量级更少的参数达成此性能，展现出卓越的计算效率。这项工作证明GL范式能为复杂医学图像分析提供高精度、高效且可解释的解决方案，为临床实践中更可持续、更可信的人工智能铺平道路。