Real-time quality feedback on Doppler data for community midwives using edge-AI
Year: 2025
Type: article
Abstract: score was 93.3% on 'Good' quality segments, demonstrating the model's ability to generalize across different datasets. By implementing the algorithm within an Android decision-support application in an mHealth framework, we have enabled real-time feedback during signal acquisition, improving data quality at the source. This scalable, edge mHealth solution offers significant potential to enhance maternal and fetal health monitoring in the Global South, contributing to global health efforts through the integration of mobile technology, AI, and healthcare.
Source: Machine Learning Health
Authors: Mohsen Motie-Shirazi, Sepideh Nikookar, Mohammad Ahmad, Alireza Rafiei, Reza Sameni, Peter Rohloff, Gari D Clifford, Nasim Katebi
Institutions: Emory University, Georgia Institute of Technology
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,125
Awards: G196810821, G9000046174
OpenAlex ID: W4415938183