Low-Cost Monitoring Technologies for Detecting Pregnancy Complications and Facilitating Timely Interventions in Low-Resource Settings
In this project, we focus on building AI models for real-time processing of fetal cardiac activity and detecting pregnancy complications. We are collaborating with Emory Co-Design Lab and a Guatemalan NGO, Wuqu' Kawoq, to support community healthcare workers and improve outcomes in pregnancy and early childhood.
Related Publications
- Ramos, E., Palax, I.P., Cuxil, E.S., Iquic, E.S., Ajqui, A.C., Miller, A.C., Chandrasekeran, S., Hall-Clifford, R., Sameni, R., Katebi, N. and Clifford, G.D., 2024. Mobil Monitoring Doppler Ultrasound (MoMDUS) study: protocol for a prospective, observational study investigating the use of artificial intelligence and low-cost Doppler ultrasound for the automated quantification of hypertension, pre-eclampsia and fetal growth restriction in rural Guatemala. BMJ open, 14(9), p.e090503. Link
- Katebi, N., Sameni, R., Rohloff, P. and Clifford, G.D., 2023. Hierarchical attentive network for gestational age estimation in low-resource settings. IEEE journal of biomedical and health informatics, 27(5), pp.2501-2511. Link
- Katebi, N., Sameni, R. and Clifford, G.D., 2020. Deep Sequence Learning for Accurate Gestational Age Estimation from a $25 Doppler Device. arXiv preprint arXiv:2012.00553. Link
Investigating the Impact of Social Determinants of Health and Trauma Exposure on Pregnancy Complications
By analyzing multi-modal data collected during pregnancy, including mental health, environment, and social determinants of health, we are building AI models to analyze fetal developmental trajectories and detect adverse events of pregnancy.
Related Publications
- Katebi, N., Sameni, R., Rohloff, P. and Clifford, G.D., 2023. Hierarchical attentive network for gestational age estimation in low-resource settings. IEEE journal of biomedical and health informatics, 27(5), pp.2501-2511. Link
Developing mHealth Solutions for Monitoring of Blood Pressure and Mental Health to Avoid Preventable Delays in Healthcare During the Postpartum Period
This project focuses on developing mHealth solutions that monitor blood pressure and mental health during the postpartum period. By leveraging wearable technology and AI-driven models, the aim is to provide early detection of health issues and prevent delays in healthcare intervention. These efforts target improved outcomes for new mothers, particularly in low-resource settings.