ICT4MOMs project aims at implementing a novel remote ICT service towards the monitoring and prediction of maternal and fetal conditions throughout pregnancy. The envisioned application is based on the integration of wearable sensors and devices connected by an ad-hoc smartphone app in communication with an ob-gyn clinical center. Advanced signal and image processing software tools will be developed for extracting information from the recorded signals, namely: fetal heart rate, uterine contractions, continuous glucose sensors and portable US probe. Once validated by the clinical partners, the collected dataset will be used for a multivariate analysis based on soft computing classifiers and machine learning techniques. Based on the growing literature providing evidence on the fact that mother-fetus system should be considered as a whole, in this proposal pregnancy is conceptualized as a continuously evolving system which needs to be investigated by means of time-varying approaches. The crucial expected outcome is the integration of the established clinical knowledge with the results of computational analysis. Such multilevel integration is expected to provide reliable and translatable clinical guidelines towards a novel pregnancy management encompassing a more inclusive monitoring framework designed on a patient-specific level. The project was recently funded by the Italian Government—Progetti di Interesse Nazionale (PRIN) under the grant number 2017RR5EW3 for the duration of three years (2019–2021).
ICT4MOMs: An ICT Integrated Approach to Monitor and Manage Pregnancy Development
Magenes G.Conceptualization
2020-01-01
Abstract
ICT4MOMs project aims at implementing a novel remote ICT service towards the monitoring and prediction of maternal and fetal conditions throughout pregnancy. The envisioned application is based on the integration of wearable sensors and devices connected by an ad-hoc smartphone app in communication with an ob-gyn clinical center. Advanced signal and image processing software tools will be developed for extracting information from the recorded signals, namely: fetal heart rate, uterine contractions, continuous glucose sensors and portable US probe. Once validated by the clinical partners, the collected dataset will be used for a multivariate analysis based on soft computing classifiers and machine learning techniques. Based on the growing literature providing evidence on the fact that mother-fetus system should be considered as a whole, in this proposal pregnancy is conceptualized as a continuously evolving system which needs to be investigated by means of time-varying approaches. The crucial expected outcome is the integration of the established clinical knowledge with the results of computational analysis. Such multilevel integration is expected to provide reliable and translatable clinical guidelines towards a novel pregnancy management encompassing a more inclusive monitoring framework designed on a patient-specific level. The project was recently funded by the Italian Government—Progetti di Interesse Nazionale (PRIN) under the grant number 2017RR5EW3 for the duration of three years (2019–2021).I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
