Automated Quality Assessment of Blind Sweep Obstetric Ultrasound for Improved Diagnosis
This work addresses reliability issues in AI-assisted prenatal ultrasound for low-resource settings, though it is incremental by focusing on quality assessment rather than new AI methods.
The study evaluated how deviations in Blind Sweep Obstetric Ultrasound acquisition affect AI model performance for fetal imaging tasks, showing that automated quality assessment and re-acquisition of flagged sweeps improved downstream task performance, with specific gains in classification accuracy.
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.