Anemia, a disease characterized by a lack of healthy red blood cells that impairs tissue oxygen supply, affects about a fifth of the world’s population and is the second leading cause of disability. Individuals in western and central sub-Saharan Africa and South Asia are disproportionately affected by anemia, with children under five years of age and maternal populations experiencing the highest prevalence. In pregnancy, anemia increases the risk of preterm labor, low birth weight and infant and maternal death. In children, anemia may hinder motor and mental development and elevate the risk of infection and cardiac failure.

Blood hemoglobin is an important indicator used to diagnose anemia. In high-income settings, it is measured using hematology tools that automatically provide accurate and reliable measurements. However, in low-income settings, such tools are not accessible due to their high cost. Instead, hemoglobin is measured using simpler devices, which require invasive capillary blood collection. This usually causes discomfort and may not be suitable for pregnant, elderly and pediatric populations. Thus, despite an overall increased ability to measure hemoglobin, several communities still cannot conveniently and accurately screen for anemia at a low cost.

This project team built on prior research to create predictive models that may be suited for the screening of anemia in low-resource settings without internet connectivity. Members were divided into two subteams, one working on model and app development and the other focusing on fostering relationships with domestic and international institutions. The project team collaborated with Johns Hopkins University, the University of Pennsylvania and Bangabandhu Sheikh Mujib Medical University in Bangladesh and presented their findings at conferences locally and in Kyoto, Japan.

Timing

Fall 2022 – Summer 2023 

Team Outputs

Screening for Anemia With Machine Learning (2023 Fortin Foundation Bass Connections Virtual Showcase)

A Predictive and Machine Learning Approach to Non-Invasive Anemia Diagnosis (poster by Dominic Garrity, Adam Wax, Nirmish Shah, Ashley Chompre, Connie Xu, Erin Dollard, Grace Wei, Helen Xu, Isabelle Xiong, Jennifer Teo, Jay Yoon, Kartik Pejavara, Khushmeet Chandi, Kyle Ferguson, Larry Jiang, Olivia Fan, Rayan Malik, Selena Halabi, Sophie Wu, Sunggun Lee and Tingnan Hu, presented at Fortin Foundation Bass Connections Showcase, Duke University, April 19, 2023)

Machine learning algorithm

Protocol paper

Reflection

Kyle Ferguson

This Team in the News

Meet the Members of the 2022-2023 Student Advisory Council

Image: Hemoglobin, by Edumol Molecular Visualizations, licensed under CC BY-NC 2.0