#HealthTechAfrica | Knowledge 4 All Foundation Ltd.

November 20, 2020

AI4D

AI4D blog series: Building a Data Pipeline for a Real World Machine Learning Application

We set out with a novel idea; to develop an application that would (i) collect an individual’s Blood Pressure (BP) and activity data, and (ii) make future BP predictions for the individual with this data.

Key requirements for this study therefore were;

The ability to get the BP data from an individual.
The ability to get a corresponding record of their activities for the BP readings.
The identification of a suitable Machine Learning (ML) Algorithm for predicting future BP.

Dr. Moses Thiga, Kabarak University, School of Science, Engineering and Technology

Ms. Daisy Kiptoo, Kabarak University, School of Science, Engineering and Technology

Dr. Pamela Kimeto, Kabarak University, School of Medicine and Health Sciences

Pre-test the idea – Pre testing the idea was a critical first step in our process before we could proceed to collect the actual data. The data collection process would require the procurement of suitable smart watches and the development of a mobile application, both of which are time consuming and costly activities. At this point we learnt our first lessons; (i) there was no precedence to what we were attempting and subsequently (ii) there were no publicly available BP data sets available for use in pre-testing our ideas.

Simulate the test data – The implication therefore was that we had to simulate data based on the variables identified for our study. The variables utilized were the Systolic and Diastolic BP Reading, Activity and a timestamp. This was done using a spreadsheet and the data saved as a comma separate values (csv) file. The csv is a common file format for storing data in ML.

Identify a suitable ML model – The data simulated and that in the final study was going to be time series data. The need to predict both the Systolic and Diastolic BP using previous readings, activity and timestamps meant that we were was handling a multivariate time series data. We therefore tested and settled on an LSTM model for multivariate time series forecasting based on a guide by Dr Jason Browniee (https://machinelearningmastery.com/how-to-develop-lstm-models-for-time-series-forecasting/)

Develop the data collection infrastructure – There being no pre-existing data for the development implied that we had to collect our data. The unique nature of our study, collecting BP and activity data from individuals called for an innovative approach to the process.

BP data collection – for this aspect of the study we established that the best way to achieve this would be the use of smart watches with BP data collection and transmission capabilities. In addition to the BP data collection, another key consideration for the device selection was affordability. This was occasioned both by the circumstances of the study, limited resources available and more importantly, the context of use of a probable final solution; the watch would have to be affordable to allow for wide adoption of the solution.

The watch identified was the F1 Wristband Heart and Heart Rate Monitor.

Activity data collection – for this aspect of the study a mobile application was identified as the method of choice. The application was developed to be able to receive BP readings from the smart watch and to also collect activity data from the user.

Test the data collection – The smart watch – mobile app data collection was tested and a number of key observations were made.

Smart watch challenges – In as much as the watch identified is affordable it does not work well for dark skinned persons. This is a major challenge given the fact that a majority of people in Kenya, the location of the study and eventual system use, are dark skinned. As a result we are examining other options that may work in a universal sense.
Mobile app connectivity challenges – The app initially would not connect to the smart watch but this was resolved and the data collection is now possible.

Next Steps

Pilot the data collection – We are now working on piloting the solution with at least 10 people over a period of 2 – 3 weeks. This will give us an idea on how the final study will be carried out with respect to:

How the respondents use the solution,
The kind of data we will be able to actually get from the respondents
The suitability of the data for the machine learning exercise.

Develop and Deploy the LSTM Model – We shall then develop the LSTM model and deploy it on the mobile device to examine the practicality of our proposed approach to BP prediction.

Reposted within the project “Network of Excellence in Artificial Intelligence for Development (AI4D) in sub-Saharan Africa” #UnitedNations #artificialintelligence #SDG #UNESCO #videolectures #AI4DNetwork #AI4Dev #AI4D

August 20, 2020

AI4D

AI4D blog series: Creating a ground truth dataset for malaria diagnosis in Tanzania

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*].

Martha Shaka

Frederick Apina

Said H Said

Halidi Maneno

Nyamos Waigama

Imani Sulutya

Said Mmaka

Emilian Ngatunga

Simon Chaula

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 [3].

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.

Taking images of stained blood smear

A sample of the annotated image( green box represent a plasmodium)

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.

Links

[1] 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.

[2]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.

[3]Philip J. Rosenthal*, “How Do We Best Diagnose Malaria in Africa?”: https://doi.org/10.4269/ajtmh.2012.11-0619

[12] UNICEF 2018 Report. The urgent need to end newborn deaths. The reality of Malaria Summary https://www.unicef.org/health/files/health_africamalaria.pdf

[13]WHO malaria 2018 report. Retrieved on 1st March 2019 from https://apps.who.int/iris/bitstream/handle/10665/275867/9789241565653-eng.pdf?ua=1