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<p>Congenital heart disease (CHD) is the most common major congenital anomaly and a leading cause of perinatal mortality and long-term neurodevelopmental disability globally, among which severe ductal dependent CHD (D-CHD) requires urgent clinical intervention due to the high risk of catastrophic cardiovascular collapse if undiagnosed. Despite the advancement of fetal echocardiography-based analysis models, there remains a lack of comprehensive and accurate evaluation approaches for D-CHD, especially for the precise differentiation of critical subtypes including coarctation of the aorta (CoA), interrupted aortic arch (IAA), and transposition of the great arteries (TGA). To address this gap, we developed the D-CHD Precision Screening System (TLEUDS) using 9,142 fetal aortic arch echocardiographic long-axis view images and 58 ultrasound clips from three medical institutions. Aiming to solve the challenges of uneven quality consistency and long-tail distribution in real-world ultrasound imaging data, TLEUDS adopts a cascaded Dual-Transfer learning framework for quality and knowledge enhancement, which implements a closed-loop screening process from initial disease detection (via the TEN-S module, a transfer version of EfficientNetV2-Screening) to detailed subtype-specific malformation screening (via the TY-PS module, a transfer version of YOLOv11-Precise Screening). Experimental results show that TLEUDS achieved a sensitivity of 0.988, 0.941, 0.966, and 0.900 for D-CHD overall, CoA, IAA, and TGA respectively, with corresponding specificities of 0.988, 0.987, 1.000, and 0.956, demonstrating state-of-the-art (SOTA) performance compared with general models. TLEUDS is expected to serve as a potential automated screening tool for tiered healthcare and computer-aided diagnosis of fetal D-CHD. Furthermore, the proposed quality and knowledge-enhanced cascaded Dual-Transfer learning framework fully leverages the “image quality distribution” and “structural prior knowledge” of fetal echocardiography, holding great potential for extension to other ultrasound image analysis domains.</p>

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TLEUDS: a cascade Dual-Transfer learning system with quality- and knowledge-enhanced for precise fetal CHD screening

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月18日

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  • Yuxuan Jiang,
  • Jiajie Tang,
  • Fanfan Zhu,
  • Yuzhou Zeng,
  • Junbo Wu,
  • Wanqi Wang,
  • Yuan Liu,
  • Hongying Wang,
  • Long Lu

摘要

Congenital heart disease (CHD) is the most common major congenital anomaly and a leading cause of perinatal mortality and long-term neurodevelopmental disability globally, among which severe ductal dependent CHD (D-CHD) requires urgent clinical intervention due to the high risk of catastrophic cardiovascular collapse if undiagnosed. Despite the advancement of fetal echocardiography-based analysis models, there remains a lack of comprehensive and accurate evaluation approaches for D-CHD, especially for the precise differentiation of critical subtypes including coarctation of the aorta (CoA), interrupted aortic arch (IAA), and transposition of the great arteries (TGA). To address this gap, we developed the D-CHD Precision Screening System (TLEUDS) using 9,142 fetal aortic arch echocardiographic long-axis view images and 58 ultrasound clips from three medical institutions. Aiming to solve the challenges of uneven quality consistency and long-tail distribution in real-world ultrasound imaging data, TLEUDS adopts a cascaded Dual-Transfer learning framework for quality and knowledge enhancement, which implements a closed-loop screening process from initial disease detection (via the TEN-S module, a transfer version of EfficientNetV2-Screening) to detailed subtype-specific malformation screening (via the TY-PS module, a transfer version of YOLOv11-Precise Screening). Experimental results show that TLEUDS achieved a sensitivity of 0.988, 0.941, 0.966, and 0.900 for D-CHD overall, CoA, IAA, and TGA respectively, with corresponding specificities of 0.988, 0.987, 1.000, and 0.956, demonstrating state-of-the-art (SOTA) performance compared with general models. TLEUDS is expected to serve as a potential automated screening tool for tiered healthcare and computer-aided diagnosis of fetal D-CHD. Furthermore, the proposed quality and knowledge-enhanced cascaded Dual-Transfer learning framework fully leverages the “image quality distribution” and “structural prior knowledge” of fetal echocardiography, holding great potential for extension to other ultrasound image analysis domains.