# Determining Fetal Orientations From Blind Sweep Ultrasound Video

arXiv: https://arxiv.org/abs/2504.06836 · HTML: https://mlsanity.com/paper/2504.06836/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 23 references that are in the map.
- First posted on arXiv 9 Apr 2025.

## Builds on (most cited first, 3 of 3)

- [SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound](https://mlsanity.com/paper/1612.05601/) (2016)
- [Semi-supervised Learning of Fetal Anatomy from Ultrasound](https://mlsanity.com/paper/1908.11624/) (2019)
- [Testing of Deep Learning Model in Real World Clinical Setting: A Case Study in Obstetric Ultrasound](https://mlsanity.com/paper/2404.00032/) (2024)

## Built on by (most cited first, 2 of 2)

- [Weight Space Correlation Analysis: Quantifying Feature Utilization in Deep Learning Models](https://mlsanity.com/paper/2512.13144/) (2025)
- [Automated Quality Assessment of Blind Sweep Obstetric Ultrasound for Improved Diagnosis](https://mlsanity.com/paper/2603.25886/) (2026)

## Abstract (arXiv)

Cognitive demands of fetal ultrasound examinations pose unique challenges among clinicians. With the goal of providing an assistive tool, we developed an automated pipeline for predicting fetal orientation from ultrasound videos acquired following a simple blind sweep protocol. Leveraging on a pre-trained head detection and segmentation model, this is achieved by first determining the fetal presentation (cephalic or breech) with a template matching approach, followed by the fetal lie (facing left or right) by analyzing the spatial distribution of segmented brain anatomies. Evaluation on a dataset of third-trimester ultrasound scans demonstrated the promising accuracy of our pipeline. This work distinguishes itself by introducing automated fetal lie prediction and by proposing an assistive paradigm that augments sonographer expertise rather than replacing it. Future research will focus on enhancing acquisition efficiency, and exploring real-time clinical integration to improve workflow and support for obstetric clinicians.