Automated Quality Assessment of Blind Sweep Obstetric Ultrasound for Improved Diagnosis

Prasiddha Bhandari, Kanchan Poudel, Nishant Luitel, Bishram Acharya, Angelina Ghimire, Tyler Wellman, Kilian Koepsell, Pradeep Raj Regmi, Bishesh Khanal

Keywords: Blind sweep ultrasound, quality assessment, obstetric imaging, AI robustness, prenatal care

TL;DR: BSOU enables minimally trained operators to collect sweep videos for AI, but performance drops with protocol errors. We show how acquisition mistakes impact key tasks and how automated quality checks detect issues and improve results.

Abstract: Blind Sweep Obstetric Ultrasound (BSOU) enables scalable fetal imaging in low-resource settings by allowing minimally trained operators to acquire standardized sweep videos for automated Artificial Intelligence(AI) interpretation. However, the reliability of such AI systems depends critically on the quality of the acquired sweeps, and little is known about how deviations from the intended protocol affect downstream predictions. In this work, we present a systematic evaluation of BSOU quality and its impact on three key AI tasks: sweep-tag classification, fetal presentation classification, and placenta-location classification. We simulate plausible acquisition deviations, including reversed sweep direction, probe inversion, and incomplete sweeps, to quantify model robustness, and we develop automated quality-assessment models capable of detecting these perturbations. To approximate real-world deployment, we simulate a feedback loop in which flagged sweeps are “re-acquired”, showing that such correction improves downstream task performance. Our findings highlight the sensitivity of BSOU-based AI models to acquisition variability and demonstrate that automated quality assessment can play a central role in building reliable, scalable AI assisted prenatal ultrasound workflows, particularly in low-resource environments.

Primary Subject Area: Integration of Imaging and Clinical Data

Secondary Subject Area: Application: Radiology

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Submission Number: 273