Development and validation of an AI-enabled mHealth technology for in-home pregnancy management

Abstract

The solution of perinatal morbidity and mortality problem lies in the development of an information technology for large-scale eHealth-enabled management of pregnancy, making use of personal mobile web monitors, cloud computing and artificial intelligence. The solution leads to halving the indices of perinatal morbidity and mortality, as well as the costs of pregnancy care at the same time. Lowering the costs of care in turn ensures the investment attractiveness of the eHealthcare services, and makes them affordable to low budget care providers. The advances of the technology are substantiated by the results already obtained. The concept of large-scale eHealth pregnancy management, making use of mobile/ wearable personal monitors, artificial intelligence and cloud computing is first proposed.

Artificial Intelligence in Pregnancy: A Scoping Review

“…Real time data gathering has been reported in (n = 3) studies. Firstly, [21] used Doppler ultrasound for remote surveillance (home monitoring) of the fetal state and well-being, combining the Doppler's sonographic assessment of fetoplacental blood flow and fetal heart rate. Secondly, in [71], the prediction system (part of the e-health system) employed data collected from medical equipment (Doppler Ultrasonography device).…”

“…Secondly, AI can provide healthcare professionals and administrators with the most optimal way to allocate health resources [20]. Thirdly, patients can benefit from using an AI based home monitoring device to provide pregnancy support [21], but also to detect possible future complications during pregnancy before they occur, such as pre-eclampsia [22].…”

Review on Pregnancy Progress Monitoring

“…However, handling sensitive health data requires robust security measures to protect patient privacy and adheres to data protection principles. The research paper [8]introduces an innovative AI-enabled ehealth service for in-home pregnancy management, utilizing mobile monitors and cloud computing. Methodology includes a Doppler ultrasonographic core and AI data processing for early complication detection.…”

An mHealth approach to remote fetal monitoring

“…Early diagnosis and prevention of antenatal diseases are on the agenda now. Large-scale fetal monitoring could help if it were more pervasive [2,3]. However, there is the lack of ehealth technologies enabling low-cost remote services for broad-scale management of pregnancy.…”