Antenatal surveillance of placental function using a wearable near infrared spectroscopy device with machine learning data interpretation

BackgroundPlacental 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 non-invasive, continuous monitoring of tissue oxygenation and metabolism. ObjectivesTo develop a wearable NIRS system for placental monitoring (FetalSenseM v1 - 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 designIn 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 sub-cohort. Outcomes were classified using the In Utero near-miss criteria for stillbirth. Machine learning (ML) analysis evaluated 11 classifiers using nested stratified 5 x 4 cross-validation (5 outer folds for performance estimation and 4 inner folds for hyperparameter tuning). ResultsSeventy monitoring sessions from 58 participants were completed across gestational ages (25+2-41+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 x 4 nested cross-validation using the top 50 features. Feature importance analysis identified oxCCO-derived and haemodynamic-metabolic coupling features as dominant predictors, whereas static PltO2 was non-discriminatory. ConclusionWe 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. Condensation pageO_ST_ABSTweetable statementC_ST_ABSA wearable placental near-infrared spectroscopy device enabled real-time monitoring of placental oxygenation and metabolism; machine learning of dynamic signals predicted risk of adverse pregnancy outcomes with 78% balanced accuracy. At a GlanceA. Why was the study conducted? O_LICurrent antenatal surveillance for assessment of fetal wellbeing is suboptimal. C_LIO_LIWe evaluated a wearable near-infrared spectroscopy device for real-time placental monitoring and outcome prediction C_LI B. What are the key findings? O_LIMachine learning applied to dynamic haemodynamic and metabolic optical signals of placental function identified pregnancies at risk with 78% balance accuracy C_LIO_LIPlacental oxygenation was higher in severe fetal growth restriction (FGR) and lower in participants with gestational diabetes (GDM) C_LI C. What does this study add to what is already known O_LIThis is the first wearable near-infrared spectroscopy system to simultaneously monitor real-time changes in placental oxygenation and metabolism in vivo. This is also the first application of machine learning analysis to placental NIRS signals. C_LIO_LIDynamic features of placental metabolism and oxygenation levels may provide clinically meaningful placental biomarkers. C_LI