Foundation Model-driven Key Anatomy Frame Selection for Blind-sweep Ultrasound Fetal Birth Weight Estimation
2026-07-01 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors developed a new way to estimate a baby's weight before birth using videos from blind-sweep ultrasound scans taken shortly before delivery. Their method picks out important video frames using a special model that understands anatomy and language, even though the scans are done without specific angles. They also made a system to reduce repeated information in the video frames while keeping important details for weight prediction. Tested on data from 839 patients, their approach was more accurate than traditional methods. This work helps reduce the need for expert operators, especially in places with fewer medical resources.
fetal birth weightblind-sweep ultrasoundfoundation modelkeyframe selectionvision-language modelfeature compressionHadlock estimationtemporal redundancyMAE (mean absolute error)
Authors
Le Ou, Xiliang Zhu, Huanwen Liang, Wenxiong Pan, Yuhao Huang, Yuxiang Deng, Xuan Sheng, Hong Yin, Juhua Xiao, Xin Zhou, Dong Ni
Abstract
Accurate fetal birth weight (FBW) estimation shortly before delivery is clinically valuable yet challenging due to its reliance on operator expertise, particularly in low-resource settings. To reduce this reliance, we study near-term birth-weight regression from blind-sweep ultrasound (US) videos acquired within 48 hours prior to delivery, with post-delivery weighing as ground truth. Accordingly, we propose a foundation model-driven key anatomy frame selection framework that enables accurate FBW regression despite the absence of plane constraints in blind sweeps. Our highlights are as follows: (1) We believe this is the first work to estimate FBW using blind-sweep US videos, enabling operator-independent assessment. (2) An Anatomy-Guided Frame Selection module equipped with a vision-language foundation model is proposed for keyframe collection in unconstrained sweeps. (3) A Redundancy-Aware Feature Compression module is designed to compress frame features while preserving task-relevant information, alleviating temporal redundancy. Extensively validated on prospectively collected data from 839 patients, our method achieves an MAE of 161.3 g, with 90.23% and 100% of cases falling within 10% and 15% absolute percentage error, outperforming typical Hadlock estimation and strong competitors. Codes are available at https://github.com/ouleoule/BlindSweep-EBW.