4 November 2019
The Medical Research Council Unit The Gambia at the London School of Hygiene and Tropical Medicine hosted its first workshop on Artificial Intelligence for Health, at the Unit’s Bray Lecture Theatre from 28th to 29th October 2019.
The two-day workshop was conceived to create awareness on data-driven methods that are applicable to health research, and provide a forum for bioinformaticians, statisticians, scientists and researchers to share insights on their research using artificial intelligence (in particular, machine learning or deep learning) and predictive modelling, as well as identify areas of potential use.
The workshop featured plenary talks and presentations by artificial intelligence and data experts in Africa, who have worked with these technologies in the areas of health care and research, and discussion centred on a range of topics including an introduction to Artificial Intelligence, insights into Machine Learning and Deep Learning, Genomics and more. It also provided an opportunity for the Unit’s researchers to showcase their work using genomics and machine learning to support their research, through the MRCG at LSHTM High Performance Computing Facility.
Professor Umberto D’Alessandro, Director of the MRCG at LSHTM stated, “This workshop comes at an opportune moment for us, as we witness a growing need to integrate cutting-edge technologies, using sophisticated algorithms and high computing capacity for simulations and testing of models. This has been done here in our research facility to determine the source and direction of the flow of infections of the malaria parasite from the eastern rural areas of The Gambia to the western coast. We also adopted machine learning approaches using our High-Performance Computing Facility to trace ancestral connectivity between the various parasite populations, using malaria samples from 15 African countries”.
In his plenary talk, Dr. Bubacarr Bah, Senior Researcher and Head of the Data Science Research Group at the African Institute for Mathematical Sciences (AIMS) in South Africa, said, “The goal is to see if we can leverage these tools provided by artificial intelligence, and establish more collaboration, going forward. The MRCG at LSHTM have shared that they have a system that has been implemented, and they are trying to work with the Government, which has already shown interest in deploying this system into government hospitals. This means we will have a lot of data collected in government hospitals that we can apply AI on”.
The use of AI technologies provides an opportunity to achieve a lot of gains to address the challenges of health care and climate change including speed of diagnoses; predicting, preventing and curing diseases; quality of care, overall patient outcomes; and new drug development.
Other guest speakers included Dr.Habiboulaye Amadou-Boubacar , Lead Data Scientist at Air Liquide in France, who spoke about Artificial Intelligence in Healthcare; and Dr. Neneh Sallah, Research Fellow in Genomics and Global Health at the London School of Hygiene and Tropical Medicine.
The workshop also included focus group discussions among participants, with the aim of identifying potential areas of mutual interest for collaboration, and to help expand the use of cutting-edge digital technologies in our research.
Badou Gaye, Head of IT at the MRCG at LSHTM said, “The workshop was a great success as it showcased the sophisticated work that the Unit has been doing with deep learning neural networks. The interactions from the workshop and the focused group discussions following the plenary talks brought out a lot of opportunities that the Unit is keen to exploit. These include using medical images (radiology and echocardiography) to diagnose diseases such as TB and rheumatic heart diseases using convolutional neural networks (CNN); building applications embedded with machine learning algorithms to automate malaria slide reading; and automation of the analysis of flow cytometry data using Machine Learning”.
The MRCG at LSHTM will also explore exchange programs for capacity building and joint research projects with AIMS, as well as organise annual conferences and meetings with AI collaborators.