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

TL;DR

This study evaluates the generalisability of fetal ultrasound deep learning models across diverse resource settings, demonstrating that transfer learning can adapt models trained in high-resource environments to low-resource African clinics with promising accuracy.

Contribution

It introduces a transfer learning framework that effectively adapts fetal ultrasound classification models from high-resource to low-resource settings, addressing domain-shift challenges in limited-data environments.

Findings

Transfer learning boosts model recall to 0.92 in African centres.

Models trained on high-resource data can be adapted to low-resource settings.

Framework maintains high precision across diverse clinical environments.

Abstract

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 $0.92\pm0.04$ 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.