Generalisability of fetal ultrasound deep learning models to low-resource imaging settings in five African countries
Open accessAbstract
Most artificial 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 first time different strategies to reduce the domain-shift effect arising from a fetal plane classification model trained on one clinical centre with high-resource settings and transferred to a new centre with low-resource settings. To that end, a classifier trained with 1792 patients from Spain is first evaluated on a new centre in Denmark in optimal conditions with 1008 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 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 [Formula: see text] 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.
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- Authors
- Sendra-Balcells, C., Campello, V. M., Torrents-Barrena, J., Ahmed, Y. A., Elattar, M., Ohene-Botwe, B., Nyangulu, P., Stones, W., Ammar, M., Benamer, L. N., Kisembo, H. N., Sereke, S. G., Wanyonyi, S. Z., Temmerman, M., Gratacós, E., Bonet, E., Eixarch, E., Mikolaj, K., Tolsgaard, M. G., & Lekadir, K.
- Affiliations
- Universitat de Barcelona (ES)
- Suez University (EG)
- Nile University (EG)
- Kamuzu University of Health Sciences (MW)
- University of Boumerdes (DZ)
- Mulago Hospital (UG)
- Makerere University (UG)
- Aga Khan University Hospital Nairobi (KE)
- Aga Khan University Nairobi (KE)
- Hospital Sant Joan de Déu Barcelona (ES)
- Rigshospitalet (DK)
- Corresponding author
- Sendra-Balcells, Carla
- Campello, Víctor M.
- ORCID
- Venue
- Scientific Reports
- Published
- 2023-02-15
- Volume
- 13
- Issue
- 1
- Pages
- 2728-2728
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC9932015
- OpenAlex
- W4320914483
- Cited by
- 57
- References
- 23
- Citations by year
- 2026: 15 · 2025: 26 · 2024: 11 · 2023: 5
- FWCI
- 2.27
- Type
- journal article
- Language
- en
- Update notices
- correction (2023-02-28)
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
- UPCommons institutional repository (Universitat Politècnica de Catalunya) (submittedVersion, cc-by-nc-nd)
- City Research Online (City University London) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Horizon 2020 (825903)
- Cerebra
- Ministerio de Ciencia, Innovación y Universidades (RTI2018-099898-B-I00)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- prenatal ultrasound, domain adaptation, transfer learning, resource-limited settings, model generalization, perinatal mortality, prenatal screening, fetal malformations, sub-Saharan Africa
- MeSH terms
- Deep Learning, Artificial Intelligence, Diagnostic Imaging, Egypt, Female, Humans, Malawi, Pregnancy
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