# Generalisability of fetal ultrasound deep learning models to low-resource imaging settings in five African countries

arXiv: https://arxiv.org/abs/2209.09610 · HTML: https://mlsanity.com/paper/2209.09610/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 20 Sep 2022.

## Builds on (most cited first, 7 of 7)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises](https://mlsanity.com/paper/2008.09104/) (2020)
- [SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound](https://mlsanity.com/paper/1612.05601/) (2016)
- [A Survey on Deep Learning for Neuroimaging-based Brain Disorder Analysis](https://mlsanity.com/paper/2005.04573/) (2020)
- [Standard Plane Detection in 3D Fetal Ultrasound Using an Iterative Transformation Network](https://mlsanity.com/paper/1806.07486/) (2018)
- [FetalNet: Multi-task Deep Learning Framework for Fetal Ultrasound Biometric Measurements](https://mlsanity.com/paper/2107.06943/) (2021)

## Built on by (most cited first, 15 of 15)

- [Explainable fetal ultrasound quality assessment with progressive concept bottleneck models](https://mlsanity.com/paper/2211.10630/) (2022)
- [Generative Diffusion Model Bootstraps Zero-shot Classification of Fetal Ultrasound Images In Underrepresented African Populations](https://mlsanity.com/paper/2407.20072/) (2024)
- [Segmenting Fetal Head with Efficient Fine-tuning Strategies in Low-resource Settings: an empirical study with U-Net](https://mlsanity.com/paper/2407.20086/) (2024)
- [Diffusion Model-based Data Augmentation Method for Fetal Head Ultrasound Segmentation](https://mlsanity.com/paper/2506.23664/) (2025)
- [Advancing Fetal Ultrasound Image Quality Assessment in Low-Resource Settings](https://mlsanity.com/paper/2507.22802/) (2025)
- [Challenging DINOv3 Foundation Model under Low Inter-Class Variability: A Case Study on Fetal Brain Ultrasound](https://mlsanity.com/paper/2511.01915/) (2025)
- [ALL-PET: A Low-resource and Low-shot PET Foundation Model in Projection Domain](https://mlsanity.com/paper/2509.09130/) (2025)
- [Influence of Classification Task and Distribution Shift Type on OOD Detection in Fetal Ultrasound](https://mlsanity.com/paper/2509.18326/) (2025)
- [Weight Space Correlation Analysis: Quantifying Feature Utilization in Deep Learning Models](https://mlsanity.com/paper/2512.13144/) (2025)
- [FETAL-GAUGE: A Benchmark for Assessing Vision-Language Models in Fetal Ultrasound](https://mlsanity.com/paper/2512.22278/) (2025)
- [Uncertainty-Calibrated Explainable Artificial Intelligence for Fetal Ultrasound Plane Classification: A Systematic Review](https://mlsanity.com/paper/2601.00990/) (2026)
- [Benchmarking Ultrasound Foundation Models for Fetal Plane Classification](https://mlsanity.com/paper/2605.27796/) (2026)
- [FedCC: A Low-Resource Federated Adaptation of Foundation Models for Robust Corpus Callosum localization in Fetal Ultrasound Images](https://mlsanity.com/paper/2607.18283/) (2026)
- [A Foundational EDM2-Based Generative Model for High-Resolution Synthetic Fetal Ultrasound Imaging from Open Datasets](https://mlsanity.com/paper/2608.05471/) (2026)
- [Towards Whole-Study Screening for Congenital Heart Disease in Fetal Ultrasound Using Multiple Instance Learning](https://mlsanity.com/paper/2609.31376/) (2026)

## Abstract (arXiv)

Most artificial intelligence (AI) research have concentrated in high-income countries, where imaging data, IT infrastructures and clinical expertise are plentiful. However, slower progress has been made in limited-resource environments where medical imaging is needed. For example, in Sub-Saharan Africa the rate of perinatal mortality is very high due to limited access to antenatal screening. In these countries, AI models could be implemented to help clinicians acquire fetal ultrasound planes for diagnosis of fetal abnormalities. So far, deep learning models have been proposed to identify standard fetal planes, but there is no evidence of their ability to generalise in centres with limited access to high-end ultrasound equipment and data. This work investigates different strategies to reduce the domain-shift effect for a fetal plane classification model trained on a high-resource clinical centre and transferred to a new low-resource centre. To that end, a classifier trained with 1,792 patients from Spain is first evaluated on a new centre in Denmark in optimal conditions with 1,008 patients and is later optimised to reach the same performance in five African centres (Egypt, Algeria, Uganda, Ghana and Malawi) with 25 patients each. The results show that a transfer learning approach can be a solution to integrate small-size African samples with existing large-scale databases in developed countries. In particular, the model can be re-aligned and optimised to boost the performance on African populations by increasing the recall to 0.92±0.04 and at the same time maintaining a high precision across centres. This framework shows promise for building new AI models generalisable across clinical centres with limited data acquired in challenging and heterogeneous conditions and calls for further research to develop new solutions for usability of AI in countries with less resources.