Foundation Model-driven Key Anatomy Frame Selection for Blind-sweep Ultrasound Fetal Birth Weight Estimation

2026-07-01

AI Summary

This study addresses the challenge of operator-independent fetal birth weight estimation from unguided (“blind-scan”) ultrasound videos in resource-limited settings. The authors propose a vision–language foundation model-based framework that automatically selects key anatomical frames from videos acquired within 48 hours before delivery and incorporates a redundancy-aware feature compression module to preserve task-relevant anatomical information for end-to-end weight regression. As the first method to estimate fetal weight directly from blind-scan videos, this approach achieves a mean absolute error of 161.3 grams on a prospective cohort of 839 cases, with 90.23% and 100% of estimates falling within 10% and 15% absolute percentage error, respectively—significantly outperforming the conventional Hadlock method and current strong baselines.

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.

Problem

• fetal birth weight estimation
• blind-sweep ultrasound
• operator-independent assessment
• key anatomy frame selection
• near-term birth-weight regression

Innovation

• foundation model
• blind-sweep ultrasound
• fetal birth weight estimation
• anatomy-guided frame selection
• feature compression