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
Proceedings of The 9th International Conference on Medical Imaging with Deep Learning, PMLR 315:2987-2997, 2026.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v315-bhandari26a, title = {Automated Quality Assessment of Blind Sweep Obstetric Ultrasound for Improved Diagnosis}, author = {Bhandari, Prasiddha and Poudel, Kanchan and Luitel, Nishant and Acharya, Bishram and Ghimire, Angelina and Wellman, Tyler and Koepsell, Kilian and Regmi, Pradeep Raj and Khanal, Bishesh}, booktitle = {Proceedings of The 9th International Conference on Medical Imaging with Deep Learning}, pages = {2987--2997}, year = {2026}, editor = {Huo, Yuankai and Gao, Mingchen and Kuo, Chang-Fu and Jin, Yueming and Deng, Ruining}, volume = {315}, series = {Proceedings of Machine Learning Research}, month = {08--10 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v315/main/assets/bhandari26a/bhandari26a.pdf}, url = {https://proceedings.mlr.press/v315/bhandari26a.html}, 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.} }
Endnote
%0 Conference Paper %T Automated Quality Assessment of Blind Sweep Obstetric Ultrasound for Improved Diagnosis %A Prasiddha Bhandari %A Kanchan Poudel %A Nishant Luitel %A Bishram Acharya %A Angelina Ghimire %A Tyler Wellman %A Kilian Koepsell %A Pradeep Raj Regmi %A Bishesh Khanal %B Proceedings of The 9th International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2026 %E Yuankai Huo %E Mingchen Gao %E Chang-Fu Kuo %E Yueming Jin %E Ruining Deng %F pmlr-v315-bhandari26a %I PMLR %P 2987--2997 %U https://proceedings.mlr.press/v315/bhandari26a.html %V 315 %X 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.
APA
Bhandari, P., Poudel, K., Luitel, N., Acharya, B., Ghimire, A., Wellman, T., Koepsell, K., Regmi, P.R. & Khanal, B.. (2026). Automated Quality Assessment of Blind Sweep Obstetric Ultrasound for Improved Diagnosis. Proceedings of The 9th International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 315:2987-2997 Available from https://proceedings.mlr.press/v315/bhandari26a.html.

Related Material