Development and Evaluation of an AI-Driven Telemedicine System for Prenatal Healthcare
This work addresses prenatal healthcare access for underserved populations in low- and middle-income countries, though it appears incremental as it builds on existing telemedicine and AI-assisted imaging approaches.
The researchers tackled limited access to obstetric ultrasound in low-resource settings by developing an AI-driven telemedicine system that assists midwives in acquiring fetal images, showing promising results in identifying standard fetal planes from non-expert sweeps and good usability in field evaluations.
Access to obstetric ultrasound is often limited in low-resource settings, particularly in rural areas of low- and middle-income countries. This work proposes a human-in-the-loop artificial intelligence (AI) system designed to assist midwives in acquiring diagnostically relevant fetal images using blind sweep protocols. The system incorporates a classification model along with a web-based platform for asynchronous specialist reviews. By identifying key frames in blind sweep studies, the AI system allows specialists to concentrate on interpretation rather than having to review entire videos. To evaluate its performance, blind sweep videos captured by a small group of soft-trained midwives using a low-cost Point-of-Care Ultrasound (POCUS) device were analyzed. The system demonstrated promising results in identifying standard fetal planes from sweeps made by non-experts. A field evaluation indicated good usability and a low cognitive workload, suggesting that it has the potential to expand access to prenatal imaging in underserved regions.