CAIRE Projects

• Team: Gari Clifford, Nasim Katebi, Sheree Boulet, Cheryl Franklin, Natalie Hernandez
• Grant: Georgia IMPROVE on Maternal Health, funded by NIH National Center for Advancing Translational Sciences (NCATS) as an Administrative Supplement to the Georgia Clinical and Translational Alliance (UL1-TR002378)

The IMPROVE Project is a collaborative effort among Emory University, Morehouse School of Medicine, and Grady Hospital. The initiative focuses on developing an mHealth support system for African American mothers during the postpartum period. Motivated by the need to address gaps in patient-clinician communication, the project integrates innovative mHealth technology to enhance postpartum care.

AI-driven low-cost ultrasound for automated quantification of hypertension, preeclampsia and IUGR in Georgia and Rural Guatemala

• Team: Gari Clifford, Nasim Katebi, Reza Sameni, Rachel Hall-Clifford, Peter Rohloff, Suchitra Chandrasekaran
• Grant: R01- NIH R01HD110480

Developing and validating AI models to assess fetal and maternal health using low-cost devices.

Supporting Safe Births Through Real-Time Monitoring and Diagnostics in Rural Guatemala

• Team: Gari Clifford, Nasim Katebi, Reza Sameni, Rachel Hall-Clifford, Peter Rohloff
• Grant: Google.org 

Leveraging Edge Computing for Real-Time Analysis of Fetal Cardiac Signals and AI-Driven Decision Support.

• Team: Nasim Katebi, Vasiliki Michopoulos, Suchitra Chandrasekaran, Alicia Smith, Gari Clifford
• Grant: K12-Pediatric and Reproductive Environmental Health -  Southeastern Environmental Exposures and Disparities (K12ESO3359) 

To study the effect of experiencing cumulative lifetime trauma and social determinants of health on fetal development and pregnancy complications using comprehensive multimodal data analysis. 

• Team: Nasim Katebi, Gari Clifford, Reza Sameni, Cheryl Franklin, Suchitra Chandrasekaran

This project focuses on developing advanced predictive models for hypertensive disorders of pregnancy using blood pressure patterns and electronic health records. By leveraging machine learning and deep learning techniques, the study aims to identify risk patterns early in pregnancy. 

• Team: Abeed Sarker, Natalie Hernandez, Rasheeta Chandler, Muhammed Idris

Behavioral health issues, such as mood disorders, substance use, and intimate partner violence, are the leading preventable causes of maternal deaths in the U.S., with Black pregnant and postpartum women facing particularly high risks. By developing customized screening tools that consider cultural norms, our project aims to significantly improve health outcomes for this historically marginalized population. Using innovative technology and community input, we will create AI-driven solutions that enhance early detection and intervention, which can enhance maternal behavioral health outcomes and reduce maternal health inequities.

• Team: Abeed Sarker, Lisa Flowers, Oluseye Ajayi

Elevating AVIVA-AI for mobile health innovations with multilingual precision and contextual understanding in LMICs. The existing AVIVA-AI app will be updated to incorporate large language model based answers, offline capability and multi-lingual capability. The app is tailored for pre/postpartum women at risk of cervical cancer.

• Team: Reza Sameni, Nasim Katebi, Gari Clifford

Since 2005, our team has made significant contributions to fetal cardiac monitoring using noninvasive techniques such as fetal electrocardiogram, magnetocardiogram, phonocardiogram, and Doppler ultrasound. These efforts have led to a U.S. patent, integration into an FDA-approved fetal ECG monitor, a series of publications, and open-access datasets and codes. Recent advancements include the implementation of online fetal ECG extraction using real-time source separation algorithms, the use of fetal ECG to estimate and track fetal movements and rotations relative to maternal body coordinates, the extraction of noninvasive fetal ECG from low-rank and time-varying mixtures, and the development of a novel semi-blind source separation algorithm capable of handling nonstationary noise and irregular maternal beats.

• Team: Azra Ismail, Suhani Jalota (Stanford University & Myna Mahila Foundation), Tanvi Divate (Myna Mahila Foundation)
• Grant: Google Award for Inclusion Research

Designing a culturally sensitive LLM-powered chatbot for question-answering support on reproductive health in Hindi. This work is in collaboration with Myna Mahila Foundation, a sexual and reproductive health and women’s empowerment organization in India.

• Team: Azra Ismail, Selen Bozkurt, Nadi Nina Kaonga (OBGYN, Grady Hospital & Emory University), Elizabeth Chahine (OBGYN, Grady Hospital & Emory University)

• Team: Azra Ismail, Neha Madhiwalla (ARMMAN)

Creating a decision support system for semi-skilled telehealth counselors—to support learning on the job, provide more targeted decision support during counseling, and help identify early signs of malnutrition. This work is in collaboration with ARMMAN, a maternal and child health organization in India.