Generalisability of deep learning models in low-resource imaging settings: A fetal ultrasound study in 5 African countries

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 $0.92 \pm 0.04$ 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

The Malawi National Tuberculosis Programme: an equity analysis

<p>Abstract</p> <p>Background</p> <p>Until 2005, the Malawi National Tuberculosis Control Programme had been implemented as a vertical programme. Working within the Sector Wide Approach (SWAp) provides a new environment and new opportunities for monitoring the equity performance of the programme. This paper synthesizes what is known on equity and TB in Malawi and highlights areas for further action and advocacy.</p> <p>Methods</p> <p>A synthesis of a wide range of published and unpublished reports and studies using a variety of methodological approaches was undertaken and complemented by additional analysis of routine data on access to TB services. The analysis and recommendations were developed, through consultation with key stakeholders in Malawi and a review of the international literature.</p> <p>Results</p> <p>The lack of a prevalence survey severely limits the epidemiological knowledge base on TB and vulnerability. TB cases have increased rapidly from 5,334 in 1985 to 28,000 in 2006. This increase has been attributed to HIV/AIDS; 77% of TB patients are HIV positive. The age/gender breakdown of TB notification cases mirrors the HIV epidemic with higher rates amongst younger women and older men. The WHO estimates that only 48% of TB cases are detected in Malawi. The complexity of TB diagnosis requires repeated visits, long queues, and delays in sending results. This reduces poor women and men's ability to access and adhere to services. The costs of seeking TB care are high for poor women and men – up to 240% of monthly income as compared to 126% of monthly income for the non-poor. The TB Control Programme has attempted to increase access to TB services for vulnerable groups through community outreach activities, decentralising DOT and linking with HIV services.</p> <p>Conclusion</p> <p>The Programme of Work which is being delivered through the SWAp is a good opportunity to enhance equity and pro-poor health services. The major challenge is to increase case detection, especially amongst the poor, where we assume most 'missing cases' are to be found. In addition, the Programme needs a prevalence survey which will enable thorough equity monitoring and the development of responsive interventions to promote service access amongst 'missing' women, men, boys and girls.</p

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

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 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 the usability of AI in countries with fewer resources and, consequently, in higher need of clinical support