1. Stokes K, Busola O, Cappuccio F, Pecchia L., (accepted March 2022) Use of technology to prevent, detect, manage, and control hypertension in sub-Saharan Africa: a systematic review, BMJ Open

2. Piaggio, D., Castaldo, R., Cinelli, M., Cinelli, S., Maccaro, A., & Pecchia, L. (2021). A framework for designing medical devices resilient to low-resource settings. Globalization and health, 17(1), 1-13.
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4. Piaggio, D., Andellini, M., Taher, M., & Pecchia, L. (2021, June). A vest for treating jaundice in low-resource settings. In 2021 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4. 0&IoT) (pp. 122-127). IEEE.
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6. Maccaro, A., Piaggio, D., Pagliara, S., & Pecchia, L. (2021). The role of ethics in science: a systematic literature review from the first wave of COVID-19. Health and technology, 11(5), 1063-1071.
7. Piaggio, D., Medenou, D., Houessouvo, R. C., & Pecchia, L. (2019, May). Donation of medical devices in low-income countries: preliminary results from field studies. In International conference on medical and biological engineering (pp. 423-427). Springer, Cham.
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10. Maccaro, A., Piaggio, D., Dodaro, C. A., & Pecchia, L. (2020). Mental illness in some Sub Saharan African communities: the perspective of Bioethics and transcultural nursing. Medicina e Morale, 69(4), 493-502.
11. Stokes, K., Castaldo, R., Franzese, M., Salvatore, M., Fico, G., Pokvic, L. G., ... & Pecchia, L. (2021). A machine learning model for supporting symptom-based referral and diagnosis of bronchitis and pneumonia in limited resource settings. Biocybernetics and Biomedical Engineering, 41(4), 1288-1302.
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13. Jossou, T. R., ET-Tahir, A., Medenou, D., Bybi, A., Fagbemi, L., Sbihi, M., & Piaggio, D. (2021). Methods to distinguish labour and pregnancy contractions: a systematic literature review. Health and Technology, 11(4), 745-757.
14. AHOUANDJINOU, M. H., MEDENOU, D., PECCHIA, L., HOUESSOUVO, R. C., & JOSSOU, T. R. (2020). Modeling an Integrated Network for Remote Patient Monitoring, Based on the Internet of Things for a More Preventive and Predictive Health System in West Africa. Global Clinical Engineering Journal, 3(2), 19-31.
15. Jossou, T. R., Et-Tahir, A., Tahori, Z., El Ouadi, A., Medenou, D., Bybi, A., ... & Piaggio, D. (2021). Electrodes in external electrohysterography: a systematic literature review. Biophysical Reviews, 13(3), 405-415.

Key enabling technologies to address the non-communicable disease burden in Africa

78% of global deaths due to cardiovascular diseases (CVDs) occur in low- to middle-income countries (LMICs), and the Pan-African Society of Cardiology (PASCAR) identifies high blood pressure (hypertension) as the highest priority area for reducing heart disease and stroke in Africa. We have recently performed a systematic literature review of the use of key enabling technologies (KETs), such as mobile phones, Internet of Things devices (IoT), and Artificial Intelligence (AI) for hypertension healthcare in sub-Saharan Africa (currently under review with an Open Access journal). We found very few publications relative to the wealth of research in high-income regions. However, initial results indicate promising use of KETs for screening for hypertension and for reducing and controlling blood pressure, although key barriers are clearly still evident. We plan to contribute to research in this area, evaluating the feasibility of leveraging AI for the design of novel blood pressure monitoring methods and screening tools, suitable for use in low resource settings with minimal training or expertise.

AI for pneumonia detection via symptoms

Sub-Saharan Africa experiences the highest global mortality from pneumonia. We are developing an AI system to detect pneumonia from easily measured symptoms and signs. We have designed a predictive Machine Learning model for distinguishing pneumonia from bronchitis based on patient symptoms and signs, using data collected by our collaborators in Bosnia and Herzegovina, characterized by the World Bank as a low to middle income country (LMIC). The model was designed to be interpretable and simple enough to be suitable for incorporation into a screening tool such an application for use on a mobile phone. Such tools can be used to alleviate pressure on healthcare services in low-resource settings. We are planning to acquire data from other LMICs, with a focus on African countries experiencing high disease burden, for further validation and improvement of this model. There is the potential to expand to detection of other respiratory diseases, such as COVID-19, as data becomes available.

Read about the model in our recent publication:

Stokes K, Castaldo R, Franzese M, et al. A machine learning model for supporting symptom-based referral and diagnosis of bronchitis and pneumonia in limited resource settings. Biocybernetics and Biomedical Engineering 2021;41(4):1288-302. doi: https://doi.org/10.1016/j.bbe.2021.09.002

COVID and LMICs

The one described above are all Global Challenges. Our activities are directly/indirectly the result of our experiences in previous EPSRC GCRF and IAA funded projects. We were considering these topics for limited resources settings (i.e., telemedicine, AI, minimum requirements, regulatory framework, heath technology assessment…) and to many extends a pandemic crisis create a situation that is, de facto, a limited resource one (not enough beds, not enough devices, not enough experiences staff…)

Here our latest publication on similitude between COVID-19 and LMICs, in regard to the limits of current international regulatory frameworks for PPE. The same probably applies to medical devices. Here is the paperLink opens in a new window.