Chen Changzhao, Liu Yiman, Liang Tongtong, Lin Shibin, Han Xiaoxiang, Liu Xiaohong, Yang Jing, Zhang Yuqi, Yan Xueping
Department of Ultrasound Medicine, Hainan Women and Children'S Medical Center, Haikou, China.
Department of Pediatric Cardiology, Shanghai Children'S Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Med Biol Eng Comput. 2025 Apr 2. doi: 10.1007/s11517-025-03347-5.
Fetal congenital heart disease (FCHD) represents a serious and prevalent congenital malformation. However, there exist notable regional disparities in the detection rates of fetal heart abnormalities. To enhance the diagnostic capabilities of ultrasound physicians in primary hospitals regarding fetal heart structures, the adoption of artificial intelligence technology to assist in acquiring high-quality, standard fetal echocardiographic images is of paramount importance. Currently, primary hospitals face challenges in recognizing standard views in fetal echocardiography, particularly under resource-constrained conditions. Efficient and accurate identification of fetal heart structures has become an urgent issue to address. Despite existing research efforts dedicated to the recognition of standard views in fetal echocardiography, current methods still suffer from limitations in computational complexity, feature extraction capabilities, and long-distance feature capturing, hindering their widespread application in ultrasound diagnosis at primary hospitals. Specifically, the literature lacks an efficient and robust model that can effectively balance high accuracy in standard view recognition with low computational complexity and fast inference times. The need for a model that can accurately capture long-distance features while maintaining efficiency is particularly acute in the context of primary hospitals, where resources are limited and the demand for accurate fetal heart assessments is high. To address these issues, the present study proposes an efficient network based on a state-space model trained with evidence for standard view recognition in fetal echocardiography. This method integrates a visual state space (VSS) model, which boasts powerful feature extraction capabilities and effective long-distance feature capturing, while significantly reducing computational complexity and facilitating efficient model inference. In the collected dataset, the proposed model achieved an accuracy of 99.32% and an F1-score of 99.29% in identifying eight standard views of fetal echocardiography. Furthermore, the model exhibited the lowest floating point operations per second (FLOPs), parameters, and inference time, while achieving the highest frames per second (FPS). This achievement not only provides a solid technical foundation for intelligent diagnosis of FCHD but also serves as an auxiliary tool for junior or novice sonographers at primary hospitals in acquiring basic views of fetal heart structures.
胎儿先天性心脏病（FCHD）是一种严重且常见的先天性畸形。然而，胎儿心脏异常的检出率存在显著的地区差异。为了提高基层医院超声医生对胎儿心脏结构的诊断能力，采用人工智能技术辅助获取高质量、标准化的胎儿超声心动图图像至关重要。目前，基层医院在识别胎儿超声心动图的标准视图方面面临挑战，特别是在资源有限的情况下。高效、准确地识别胎儿心脏结构已成为亟待解决的问题。尽管已有研究致力于胎儿超声心动图标准视图的识别，但目前的方法在计算复杂度、特征提取能力和长距离特征捕捉方面仍存在局限性，阻碍了它们在基层医院超声诊断中的广泛应用。具体而言，文献中缺乏一种高效且强大的模型，该模型能够有效地在标准视图识别的高精度与低计算复杂度和快速推理时间之间取得平衡。在基层医院资源有限且对准确胎儿心脏评估需求高的背景下，尤其迫切需要一种能够在保持效率的同时准确捕捉长距离特征的模型。为了解决这些问题，本研究提出了一种基于状态空间模型的高效网络，该模型通过证据训练用于胎儿超声心动图标准视图的识别。该方法集成了视觉状态空间（VSS）模型，该模型具有强大的特征提取能力和有效的长距离特征捕捉能力，同时显著降低了计算复杂度并促进了高效的模型推理。在所收集的数据集中，所提出的模型在识别胎儿超声心动图的八个标准视图时，准确率达到99.32%，F1分数达到99.29%。此外，该模型每秒浮点运算次数（FLOPs）、参数和推理时间最低，同时每秒帧数（FPS）最高。这一成果不仅为FCHD的智能诊断提供了坚实的技术基础，也为基层医院的初级或新手超声检查人员获取胎儿心脏结构的基本视图提供了辅助工具。
