A new Deep Learning-Assisted Probe Navigation System for Transthoracic Echocardiography

Transthoracic Echocardiography (TTE) is a primary imaging modality for the evaluation of cardiac anatomy and function. Standardized TTE protocols rely on specific probe maneuvers to acquire predefined anatomical views; however, image acquisition quality is heavily operator-dependent, and particularly novice users often fail to obtain diagnostically adequate images, since optimal probe positioning is inherently a very challenging task. To address this challenge, we propose a novel deep learning-based framework to support less experienced operators in acquiring correct echocardiographic apical views. The proposed approach integrates two core components: a new navigation map that models the relationships between optimal and suboptimal views, linking common acquisition errors to the corresponding corrective probe motion (e.g., tilting, rotation, translation) and a classification system combining a Variational Autoencoder, for semi-supervised latent feature extraction, with a lightweight Multi-Layer Perceptron, for view classification. The framework was first evaluated on two public datasets (CAMUS and HMC-QU) for binary classification (apical 2 and 4 chamber views), achieving average accuracies up to 0.97 while maintaining good generalization across heterogeneous acquisition conditions. The analysis was then extended to a more challenging 11-class classification task, including four standard apical views (2, 3, 4, 5 chambers) and seven suboptimal variants, using a newly collected in-house dataset (PROUD). In this setting, the proposed VAE+MLP achieved an average accuracy of 0.85, representing a promising step toward AI-assisted TTE guidance systems for novice operators.