Automated Quality Assessment of Blind Sweep Obstetric Ultrasound for Improved Diagnosis

TL;DR

This paper evaluates how variations in blind sweep obstetric ultrasound affect AI interpretation accuracy and develops automated quality assessment tools to improve reliability in low-resource settings.

Contribution

It systematically analyzes the impact of acquisition deviations on AI tasks and introduces automated quality models for real-time correction.

Findings

Model robustness decreases with acquisition deviations.

Automated quality assessment detects perturbations effectively.

Feedback loop improves downstream AI task performance.

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.