Evidence map›Paper›PMID 42495262›Full record

ArticleFrontiers in pediatrics2026

uFlowAM: an unsupervised framework for detection and visualization of abnormal intracardiac microflow on early-pregnancy fetal cardiac microflow imaging.

Yan Xia, Yarui Wei, Zhanru Lan, Ning Wu, Yongxin Li, Xueqin Ji

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Article in Frontiers in pediatrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Yan XiaDepartment of Ultrasound Medicine, Peking University First Hospital Ningxia Women and Children's Hospital (Ningxia Hui Autonomous Region Maternal and Child Health Hospital), Yinchuan, Ningxia Hui Autonomous Region, China.
Yarui WeiDepartment of Ultrasound Medicine, Peking University First Hospital Ningxia Women and Children's Hospital (Ningxia Hui Autonomous Region Maternal and Child Health Hospital), Yinchuan, Ningxia Hui Autonomous Region, China.
Zhanru LanDepartment of Ultrasonography, Ningxia Guyuan Maternal and Child Health Hospital, Guyuan, Ningxia Hui Autonomous Region, China.
Ning WuDepartment of Ultrasound Medicine, Peking University First Hospital Ningxia Women and Children's Hospital (Ningxia Hui Autonomous Region Maternal and Child Health Hospital), Yinchuan, Ningxia Hui Autonomous Region, China.
Yongxin LiDepartment of Thoracic Surgery, Beijing Genertec Aerospace Hospital, Beijing, China.
Xueqin JiDepartment of Ultrasound Medicine, Peking University First Hospital Ningxia Women and Children's Hospital (Ningxia Hui Autonomous Region Maternal and Child Health Hospital), Yinchuan, Ningxia Hui Autonomous Region, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Congenital heart disease (CHD) is a clinically important fetal anomaly. Early-pregnancy fetal cardiac microflow imaging (FCMI) can show low-velocity intracardiac flow, but brief shunt-related, regurgitant, and outflow-tract disturbances remain difficult to recognize when image quality, fetal position, and gestational age vary across examinations. Objective: To evaluate uFlowAM for fetus-level detection and visualization of abnormal intracardiac microflow patterns on early-pregnancy FCMI. Methods: This multicenter diagnostic accuracy study analyzed 650 early-pregnancy FCMI examinations from fetuses referred for suspected CHD or CHD risk assessment, including 500 examinations in the internal cohort and 150 in the external cohort. Standard four-chamber, left ventricular outflow tract (LVOT), and right ventricular outflow tract (RVOT) clips were processed by microflow extraction, cardiac-cycle alignment, signal normalization, and 16-frame windowing. uFlowAM used self-supervised training to learn a 256-dimensional representation of control fetal microflow from temporal-order discrimination and masked-frame reconstruction. Model training used no pixel-level or lesion-level labels. Control embeddings were grouped by view and cardiac phase to build a normal microflow template library, and an abnormality index (AbI) was calculated from latent-space Mahalanobis distances. The operating threshold was calibrated in internal validation and then kept fixed for external testing. Clinical utility was assessed in a 9-reader, 240-case multi-reader multi-case (MRMC) study. Results: Using the fixed operating threshold ( Conclusions: In this CHD-enriched referral/risk-assessment cohort, uFlowAM detected abnormal early-pregnancy fetal cardiac microflow patterns and improved reader consistency and efficiency on selected fetal cardiac microflow views. The framework should be considered an assistive second-reader tool for early fetal CHD assessment. It should not be used as a substitute for a complete fetal echocardiographic examination.

Indexed as

anomaly detectioncongenital heart diseaseearly pregnancyfetal cardiac microflow imagingfetal cardiologySlowFlowHDunsupervised learningvisualization

Identifiers

PMID42495262
PMCPMC13391499

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