Document Type
Article
Department
Obstetrics and Gynaecology (East Africa); Centre of Excellence in Women and Child Health
Abstract
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 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 arisen 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 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 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 usability of AI in countries with less resources and, consequently, in higher need of clinical support.
Publication (Name of Journal)
arXiv
Recommended Citation
Sendra-Balcells, C.,
Campello, V. M.,
Torrents-Barrena, J.,
Ahmed, Y. A.,
Elattar, M.,
Botwe, B. O.,
Nyangulu, P.,
Stones, W.,
Wanyonyi, S.,
Temmerman, M.
(2022). Generalisability of deep learning models in low-resource imaging settings: A fetal ultrasound study in 5 African countries. arXiv, 1-14.
Available at:
https://ecommons.aku.edu/eastafrica_fhs_mc_obstet_gynaecol/396
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.