Antenatal surveillance of placental function using a wearable near infrared spectroscopy device with machine learning data interpretation
Journal:
medRxiv
Published Date:
Mar 13, 2026
Abstract
Background Placental dysfunction remains a leading cause of stillbirth and neonatal morbidity, yet current monitoring tools provide only indirect and intermittent measures of fetoplacental wellbeing. Near infrared spectroscopy (NIRS) offers noninvasive, continuous monitoring of tissue oxygenation and metabolism. Objectives To develop a wearable NIRS system for placental monitoring (FetalSenseM v1 or FSM v1), investigate optical markers of placental oxygenation and metabolism in a population at high risk of adverse pregnancy outcomes such as stillbirth, and to apply machine learning analysis to develop a model for pregnancy outcome prediction. Study design In this prospective observational study, women with high-risk singleton pregnancies underwent antenatal placental NIRS monitoring for over 40 minutes. FSM v1 incorporates dual source detector separations and multiwavelength light sources to derive absolute placental oxygen saturation (PltO2) and relative cytochrome c oxidase (oxCCO) changes. FSM was placed on the abdominal wall following an ultrasound scan locating the placental position. Monte Carlo simulations were performed to estimate placental sensitivity, and a minimum placental sensitivity (MPS) threshold (>5%) defined a physiologically refined subcohort. Outcomes were classified using the In Utero nearmiss criteria for stillbirth. Machine learning (ML) analysis evaluated 11 classifiers using nested stratified 5 to 4 cross validation (5 outer folds for performance estimation and 4 inner folds for hyperparameter tuning). Results Seventy monitoring sessions from 58 participants were completed across gestational ages (25+2 to 41+1 weeks gestation); 33 recordings from 30 participants met MPS criteria. In the full cohort, mean PltO2 was 49.8% and was not related to gestational age or poor outcome based on near miss stillbirth criteria. In the MPS sub-cohort, higher PltO2 was observed in severe fetal growth restriction (FGR) and lower PltO2 in gestational diabetes (both p=0.04). Hemodynamic-metabolic coupling (HbD:oxCCO semblance) was increased in severe FGR (p=0.0002). The best performing ML model (SVM) achieved a balanced accuracy of 78%, a recall (sensitivity) of 72% and a specificity of 84% under 5 to 4 nested cross-validation using the top 50 features. Feature importance analysis identified oxCCO-derived and haemodynamicmetabolic coupling features as dominant predictors, whereas static PltO& was nondiscriminatory. Conclusion We describe the first wearable NIRS device to provide simultaneous non-invasive placental haemodynamic and metabolic monitoring. While static oxygenation indices lacked predictive value, ML analysis applied to dynamic NIRS features yielded accurate pregnancy outcome prediction, with metabolic signals emerging as key drivers. These findings support further development of wearable placental NIRS integrated with advanced analytics for antenatal surveillance.
