Generalisability of fetal ultrasound deep learning models to low‑resource imaging settings in fve African countries
Date
2023
Journal Title
Journal ISSN
Volume Title
Publisher
Scientific reports
Abstract
Most artifcial intelligence (AI) research and innovations 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 the 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 low resources, i.e. with limited access to high-end ultrasound equipment and ultrasound
data. This work investigates for the frst time diferent strategies to reduce the domain-shift efect
arising from a fetal plane classifcation model trained on one clinical centre with high-resource settings
and transferred to a new centre with low-resource settings. To that end, a classifer trained with 1792
patients from Spain is frst evaluated on a new centre in Denmark in optimal conditions with 1008
patients and is later optimised to reach the same performance in fve African centres (Egypt, Algeria,
Uganda, Ghana and Malawi) with 25 patients each. The results show that a transfer learning approach
for domain adaptation 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 the usability of AI
in countries with fewer resources and, consequently, in higher need of clinical support.
Description
Research Article
Keywords
fetal ultrasound, artificial intelligence (AI), African