So why have we decided to collect malaria datasets to assist in developing a solution in its diagnosis? First, Malaria remains one of the significant threats to public health and economic development in Africa. Globally, it is estimated that 216 million cases of malaria occurred in 2017, with Africa bearing the brunt of this burden [5*]. In Tanzania, malaria is the leading cause of morbidity and mortality, especially in children under 5 years and pregnant women. Malaria kills one child every 30 seconds, about 3000 children every day [4*]. Malaria is also the leading cause of outpatients, inpatients, and admissions of children less than five years of age at health facilities [5*].
Second, the most common methods to test for malaria are microscopy and Rapid Diagnostic Tests (RDT) [1, 2]. RDTs are widely used, but their chief drawback is that they cannot count the number of parasites. The gold standard for the diagnosis of malaria is, therefore, microscopy. Evaluation of Giemsa-stained thick blood smears, when performed by expert microscopists, provides an accurate diagnosis of malaria .
Nonetheless, there are challenges to this method, it consumes a lot of time to perform one diagnosis, requires experienced technologists who are very few in developing countries, and manually looking at the sample via a microscope is a tedious and eye-straining process. We learned that although a microscopic diagnostic is a golden standard for malaria diagnosis, it is still not used in most of the private and public health centers. We realized that some of the lab technologists in health care are not competent in preparing staining reagents used in the diagnosis process. We had to create our own reagents and supply to them for the purpose of this research.
Artificial intelligence is transforming how health care is delivered across the world. This has been evident in pathology detection, surgery assistance and early detection of diseases such as breast cancer. However, these technologies often require significant amounts of quality data and in many developing countries, there is a shortage of this.
To address this deficiency, my team, composed of 6 computer scientists and 3 lab technologists, collected and annotated 10,000 images of a stained blood smear and developed an open-source annotation tool for the creation of a malaria dataset. We strongly believe the availability of more datasets and the annotation tool (for automating the labeling of the parasites in an image of stained blood smear) will improve the existing algorithms in malaria diagnosis and create a new benchmark.
In the collection of this dataset, we first sought and were granted ethical clearance from the University of Dodoma and Benjamin Mkapa Hospital’s research center. We have collected 50 blood smear samples for patients confirmed with malaria and 50 samples for negative confirmed cases. Each sample was stained by the lab technologist and 100 images were taken using iPhone 6S attached to a microscope. This led to having a total of 5000 images for the positive confirmed patients and 5000 imaged for the negative confirmed patient.
Through this work, we have had several opportunities including attending academic conferences and forming connections with other researchers such as Dr. Tom Neumark, a postdoctoral social anthropologist at the University of Oslo. Through our work, we also met Prof Delmiro Fernandes-Reyes, a professor of biomedical engineering. In a joint venture with Prof Delmiro Fernandes-Reyes, we submitted a proposal for the DIDA Stage 1 African Digital Pathology Artificial Intelligence Innovation Network (AfroDiPAI) at the end of November 2019.
We are also disseminating the results of our research. We have submitted an abstract (on the ongoing project) to two workshops (Practical Machine Learning in Developing Countries and Artificial Intelligence for Affordable Health) for the 2020 ICLR conference in Ethiopia, and it has been accepted to be presented as a poster. We were also delighted to get very constructive feedback from reviewers of the conference and look forward to incorporating them as we continue with the projects and final publication.
The next stage will be to start using our data and train deep learning models in the development of the open-source annotation tool. At the same time, together with the AI4D team, we are looking for the best approach to follow when releasing our open-source dataset in the medical field.
But our overall aim is to develop a final product of our mobile application that will assist lab technologist in Tanzania and beyond in the onerous work of diagnosis malaria. We have already met many of these technologists who are not only excited and eagerly awaiting this tool, but generously helped us as we have gone about developing it.
 B.B. Andrade, A. Reis-Filho, A.M. Barros, S.M. Souza-Neto, L.L. Nogueira, K.F. Fukutani, E.P. Camargo, L.M.A. Camargo, A. Barral, A. Duarte, and M. Barral-Netto. Towards a precise test for malaria diagnosis in the Brazilian Amazon: comparison among field microscopy, a rapid diagnostic test, nested PCR, and a computational expert system based on artificial neural networks. Malaria Journal, 9:117, 2010.
Maysa Mohamed Kamel, Samar Sayed Attia, Gomaa Desoky Emam, and Naglaa Abd El Khalek Al Sherbiny, “The Validity of Rapid Malaria Test and Microscopy in Detecting Malaria in a Preelimination Region of Egypt,” Scientifica, vol. 2016, Article ID 4048032, 5 pages, 2016. https://doi.org/10.1155/2016/4048032.
Philip J. Rosenthal*, “How Do We Best Diagnose Malaria in Africa?”: https://doi.org/10.4269/ajtmh.2012.11-0619
 UNICEF 2018 Report. The urgent need to end newborn deaths. The reality of Malaria Summary https://www.unicef.org/health/files/health_africamalaria.pdf
WHO malaria 2018 report. Retrieved on 1st March 2019 from https://apps.who.int/iris/bitstream/handle/10665/275867/9789241565653-eng.pdf?ua=1