Congenital heart disease (CHD) is the leading cause of neonatal mortality worldwide, making early and accurate diagnosis crucial. In resource-constrained regions, standardized ultrasound screening remains difficult due to the shortage of specialized clinicians. Digital Twin (DT) technology, which constructs virtual AI diagnostic models, enables personalized assessments of heart structure and function, offering intelligent diagnostic support in primary or remote healthcare settings. However, the real-time updates required by DT systems place increased demands on the inference speed and computational efficiency of AI models. Existing methods often suffer from parameter redundancy and inference delays, making them inadequate for meeting the low-latency, large-scale needs of clinical applications. To overcome these challenges, we propose the Liquid-Sequencer, a lightweight model for the diagnosis of fetal CHD. The model first employs a convolutional network with DPSE (Depthwise Separable and Squeeze-and-Excitation) modules to efficiently extract spatial features by utilizing depthwise separable convolution and channel attention. These feature maps are then processed by a bidirectional liquid sequence module, where orthogonally scanned Liquid Neural Networks (LNNs) capture global context with linear complexity, offering a more efficient alternative to self-attention mechanisms. This integration of spatial and sequential learning is well-suited to the dynamic nature of fetal cardiac ultrasound and the demands of DT systems. Experimental results on 12 datasets demonstrate outstanding performance with only 0.30M parameters. Additionally, t-SNE visualizations reveal highly discriminative feature representations and clear inter-class separations, underscoring the model's potential as an advanced diagnostic tool.
Qiao et al. (Thu,) studied this question.