# Automated Quality Assessment of Blind Sweep Obstetric Ultrasound for Improved Diagnosis

arXiv: https://arxiv.org/abs/2603.25886 · HTML: https://mlsanity.com/paper/2603.25886/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 26 Mar 2026.

## Builds on (most cited first, 6 of 6)

- [MViTv2: Improved Multiscale Vision Transformers for Classification and Detection](https://mlsanity.com/paper/2112.01526/) (2021)
- [Fetal Pose Estimation in Volumetric MRI using a 3D Convolution Neural Network](https://mlsanity.com/paper/1907.04500/) (2019)
- [Enhanced detection of fetal pose in 3D MRI by Deep Reinforcement Learning with physical structure priors on anatomy](https://mlsanity.com/paper/2007.08146/) (2020)
- [QC-Automator: Deep Learning-based Automated Quality Control for Diffusion MR Images](https://mlsanity.com/paper/1911.06816/) (2019)
- [Uncertainty Estimation in Deep 2D Echocardiography Segmentation](https://mlsanity.com/paper/2005.09349/) (2020)
- [Determining Fetal Orientations From Blind Sweep Ultrasound Video](https://mlsanity.com/paper/2504.06836/) (2025)

## Abstract (arXiv)

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.