What is AWARrD?

AWARrD is the "Alliance for Women's African Research Data" that comprises a multinational team of data scientists, hematologists, epidemiologists, obstetrician gynecologists, experienced clinical investigators and the developers of the most downloaded women's health application in the world, who, individually and collectively, are dedicated to improving the reproductive health of African girls and women. This team, many of whom have worked together in the past, has come together to respond to a funding opportunity provided by the US National Institutes of Health (NIH). The request by the NIH is to build the capacity of African nations to utilize data science to solve health care problems important to the African continent and to do so by partnering with universities, private enterprize and other experts from around the world. More on the NIH "funding opportunity" called DS-I Africa, can be found here.

The scientists, clinicians and other entities that comprise AWARrD have an interest in improving women's reproductive health in general, and, in particular, in improving the diagnosis and treatment of problems that related to the uterus, or the womb. The notion of using "big data" or data science to help solve some of these problems is enticing so it was decided to harness the skills, passions, and available resources of this spectrum of stakeholders and design research projects that utilize existing data to start to address important clinical problems regarding uterine health that face and challenge women in Africa as well as their health care providers.

Project 1

aim: Artificial Intelligence for the Myometrium

Teaching machines to detect leiomyosarcoma

The aim project is designed to test the hypothesis that by exposing magnetic resonance images (MRIs) of knowingly benign and malignant leiomyomas - leiomyosarcomas - to a "deep learning" computerized system, the "machine" may "learn" to diagnose the leiomyosarcomas by detecting subtle differences that may escape the human eye. For the aim project, a database of hundreds of MRIs of benign fibroids will be used to train the machine on recognizing benign tumors, and a set of MRIs of leiomyosarcoma will be found to show the system the malignant version. Computer scientists with expertise in "deep learning" at the University of Assiut and University of Chicago will develop this project and hopefully will help us find a way to diagnose these rare but dangerous tumors. Indeed, preliminary evidence suggests that this may be a useful and effective approach.

Project 2

DatAUB

Can smartphones identify girls & women with HMB, ID & IDA?

The AWARrD team has joined with Flo Health and a spectrum of internationally known and respected specialists in abnormal menstrual bleeding (AUB) to create a research plan designed to test the ability of the Flo app to identify iron deficiency (ID) and iron deficiency anemia (IDA) in African women. The name of the project is "DatAUB", which stands for Data on Abnormal Uterine Bleeding and, if approved for initiation, will start with the anonymized data already present from more than 2 million girls and women. Then, in a pair of studies called Val-IDA-te, we will compare the results of women using the application with their iron levels to test the hypothesis that the system can predict those who have ID or IDA. We anticipate that there will be a correlation between the volume of bleeding and the presence of ID and IDA and that these data may ultimately be used on a broader scale to help health care systems and health care providers target women for testing and therapy for their AUB, and ID/IDA.

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