Tackling Malaria Diagnosis in sub-Saharan Africa with Fast, Accurate and Scalable Robotic Automation, Computer Vision and Machine Learning (FASt-Mal)

Lead Research Organisation: University College London
Department Name: Computer Science

Abstract

Malaria affect about 300 million people worldwide leading to around one million deaths each year. Up to eighty-five percent of the cases occur in sub-Saharan Africa with about 90% mortality in the under five years-of-age group due to severe malaria syndromes. Control of malaria remains a major public health issue in sub-Saharan Africa developing countries. A quarter of the global malaria cases and a third of malaria-attributable childhood deaths occur in the most populous country of Africa, Nigeria (160M inhabitants) and indicates the importance of the problem. Accurate malaria diagnosis relies on the recognition of clinical parameters and more importantly in the microscopic detection of malarial parasites, parasitised red-blood-cells in peripheral-blood films. Malaria parasite detection and counting by human-operated optical microscopy is the current "gold standard" and despite its major severe drawbacks, other non-microscopic methodologies have not been able to outperform it. Presumptive treatment for malaria (without microscopic confirmation) is wasteful of drugs and ineffective if the diagnosis was wrong, a drain on often precious health resources, fuels antimalarial resistance and have made control and elimination interventions unachievable. We aim to create and test in real-world conditions a fast, accurate and scalable malaria diagnosis system by replacing human-expert optical-microscopy with a robotic automated computer-expert system FASt-MalPrototype that assesses similar digital-optical-microscopy representations of the problem. The system aims to provide access to effective malaria diagnosis, a challenge that is faced by all developing countries where malaria is endemic.

Planned Impact

The global incidence of clinical malaria is estimated at about 300 million cases leading to around one million deaths each year. Up to eighty-five percent of the cases occur in sub-Saharan Africa with about 90% mortality in the under five years-of-age group due to severe malaria syndromes. Control of malaria remains a major public health issue in sub-Saharan Africa developing countries. Our proposal addresses a key problem primarily related to these large group of low- and low-to-middle income countries with large burden Global Health Challenges such a malaria. A quarter of the global malaria cases and a third of malaria-attributable childhood deaths occur in the most populous country of Africa, Nigeria (160M inhabitants) and indicates the importance of the problem. Large all-year-round lethal malaria morbidity and mortality burden has hindered Ibadan wellbeing and economic development within the South-West Nigerian geopolitical region. Key strategies for malaria control has been to reduce mortality by rapid treatment with antimalarial drugs, but this has been stalled by the lack of scalable accurate diagnosis methods. Human-microscopic examination of blood smears remains the "gold standard" for malaria diagnosis and despite its major severe drawbacks, other non-microscopic methodologies have not been able to outperform it. Access to effective malaria diagnosis is a challenge faced by all developing countries where malaria is endemic. Presumptive treatment for malaria (without microscopic confirmation) is wasteful of drugs and ineffective if the diagnosis was wrong, a drain on often precious health resources, fuels antimalarial resistance and have made control and elimination interventions unachievable. This has prompted WHO, CDC and other Global Health organisations to emphasise the urgent need for tools to overcome the deficiencies of human-operated optical-microscopy malaria diagnosis. Our research aims to provide a novel solution for automated fast accurate scalable computational optical-microscopy identification of malaria parasites that will certainly underpin the design and development of future portable accurate and cost-effective malaria-detection devices. Our proposal has a clear path to immediate- and near-future impact outcomes across malaria endemic regions. The proposed timeline is to achieve the immediate-future impact outcome: the design and deployment of a cheaper and optimised robotic bench-top prototype at our primary beneficiary overseas partner at COMUI. Our Nigeria-UK team will carry-out a novel large-scale preclinical assessment of the validity of the automated computational system in real world clinical conditions. The COMUI University College Hospital Ibadan is a centre of academic excellence and attracts both wellbeing and ill people seeking affordable good quality healthcare. Our population footprint has allowed us to execute activities to engage users from different socio-economic backgrounds. Our engagement with primary users is extremely high given to the all-year-round burden of malaria at all ages of the population of the Ibadan metropolis. All inhabitants are at high risk of malaria infection with the most affected being pregnant women, children and the elderly. Fast accurate and scalable malaria-diagnosis methods such as ours will improve health and wealth on large sectors of the population living in extreme poverty across Nigeria and more importantly across the sub-Saharan Africa region (i.e. Togo, Ghana, Cameroon among others).The impact of our research is enormous as rapid and reliable accurate diagnosis of malaria, and therefore its accurate prompt treatment, is a crucial challenge for fulfilling the international development goals for the sub-Saharan African region and other regions of the World affected with malaria.
 
Description We have discovered key aspect of robotic-microscopy and machine learning related to solving the challenge of automated diagnosis of malaria.
We have now developed two microscopy strategies. One based on the capture-fastmal scanning microscope and a novel one based on fourier ptycography which is likely to have a tremendous impact in deploying the system in sub-saharan Africa. The fourier ptycography microscope is fast in acquiring data, provides high-resolution of a wide area of the specimens and more importantly it has no-moving parts. The ML framework of the digital pathology system is underway with the expected difficulties of a challenging real-world problem. We have developed new method for Deep Learning Extended Depth of Field Fusion and object detectors for the diagnosis of a specimen. We are in pace in digitising this large and unique image and clinical dataset. We have now designed, implemented and validated a DeepLearning approach named Deep Malaria Convolutional Neural Network (DeepMCNN) that has PPV and NPV suitable for clinical use in the sub-Saharan malaria region. We are also in the process to deploy a smart AI driven navigational tool for the microscope.
Exploitation Route The findings are part of the development of an automated malaria diagnosis system that will be deployed across our health provision centres in Ibadan, Nigeria, an urban metropolis of 3-million inhabitants with large malaria burden. From there we attempt to deploy across the region. This will have a massive impact in malaria diagnosis capacity, capability and reliability. We also have plans to deploy a novel ptychographic microscope that could speed up diagnosis time. The DeepMCNN is being deployed in the clinical site in sub-Saharan West Africa as well as a simpler version based on mobile smartphone cameras and GPU.
Sectors Aerospace, Defence and Marine,Communities and Social Services/Policy,Digital/Communication/Information Technologies (including Software),Electronics,Financial Services, and Management Consultancy,Healthcare,Government, Democracy and Justice,Manufacturing, including Industrial Biotechology,Pharmaceuticals and Medical Biotechnology,Other

URL https://african-cschd.org
 
Description Our work and findings related to this award are having an positive immediate effect on all our patient cohorts at the College of Medicine of University of Ibadan. The standardisation and procedural adjustments deployed as an immediate consequence of this research have increased the capability and capacity as well as the quality of malaria diagnosis across the users of our develop technology on the urban metropolis of Ibadan Nigeria where 3-million inhabitants live under intense malaria burden all-year-round. Data from this diagnostic pipeline is allowing us to validate data-driven malaria prevalence prediction system which we are in the process to submit for peer-review. This is an ideal integration of the aim of the project (digital enabled malaria diagnosis) with rapid clinical benefit to patient with the use of such information for public and global health monitoring. We have created an African Computational Sciences for Health and Development where expertise from this and other projects with our African partner are showcased and facilitate the access to the region. We have now established contact with NGO focused on training of Machine Learning and Artificial Intelligence in the African Region.
First Year Of Impact 2017
Sector Communities and Social Services/Policy,Creative Economy,Digital/Communication/Information Technologies (including Software),Education,Healthcare,Manufacturing, including Industrial Biotechology,Pharmaceuticals and Medical Biotechnology,Other
Impact Types Cultural,Societal,Economic,Policy & public services

 
Title Case-Control Severe Malaria Study Clinical Database 
Description Database of large case-control study focused on severity biomarker discovery SQL database with clinical, laboratory and experimental data 
Type Of Material Database/Collection of data 
Provided To Others? No  
Impact This large study is producing new findings of molecules that correlate with childhood severe malaria 
 
Title FASt-Mal Malaria Parasite Object Label Expert Database 
Description Expert microscopist object-level labels on digitised malaria thick blood films 
Type Of Material Database/Collection of data 
Year Produced 2020 
Provided To Others? Yes  
Impact This database that will be available upon our publication due 2020 will be available to train deep learning object detectors for malaria artificial intelligence assisted devices. 
URL https://african-cschd.org/index.php/75-FASt-Mal
 
Description Kabarak University Digital Pathology 
Organisation Kabarak University
Country Kenya 
Sector Academic/University 
PI Contribution We have exchanged protocols to deploy a pan-African Digital Innovation network in Digital Pathology
Collaborator Contribution We have exchanged protocols to deploy a pan-African Digital Innovation network in Digital Pathology
Impact Work still in progress to identify what pathologies will be relevant for the digital pathology framework to be used.
Start Year 2019
 
Description University of Dodoma Labelling App 
Organisation University of Dodoma
Country Tanzania, United Republic of 
Sector Academic/University 
PI Contribution We have deployed some of fastmal algorithms and protocols with a team in East Africa University of Dodoma
Collaborator Contribution They have contributed with samples and annotations of malaria specimens as well as the deep learning platform
Impact we are still working on the labels and the east African specimens
Start Year 2019