Discover the Preemie sensor and our portfolio of applications
The Preemie System delivers improved health outcomes for premature infants through AI-driven personalized nutrition and real-time data analytics. It is the first end-to-end neonatal feeding management system that analyses the mother’s milk, calculates the required milk fortification based on standard nutritional guidelines and infant clinical data, including disease risk scores for necrotizing enterocolitis (NEC), Bronchopulmonary dysplasia (BPD) and neonatal sepsis.
Preemie Sensor is an spectrometer calibrated with human milk and designed to analyse mother’s milk for its nutritional composition.
The NurvetteTM is a demountable patented cuvette to designed to be used with the Preemie Sensor for the analysis of human milk.
Milk Analysis App (MAA) is a mobile application that runs on an Android tablet and operates Preemie Sensor.
NutiNTrack is a secure, browser-based application (compliant with GDPR and HIPAA) that generates personalised fortification plans for mother breast milk by integrating analysed milk composition (from the Preemie Sensor), individual infant data, predictive early-warning models (sourced from the Preemie Analytics platform), and consensus-based nutritional guidelines such as the guideline published by European Society of Pediatric Gastroenterology, Hepatology, and Nutrition (ESPGHAN).
A secure, federated learning network that allows NICUs to collaboratively build powerful AI models for predicting preterm infant diseases like BPD, NEC, and Sepsis. Preemie Analytics (PA) creates a growing repository of collective intelligence that provides clinicians with critical, data-driven insights for risk stratification and personalised nutritional management. These insights integrate with NutriNTrack software to enable personalised, data-driven fortification of mother’s milk.
The SpecData™ is a mobile application (iOS and Android) designed to operate the Preemie Sensor™. It enables the collection of data that is used later in developing calibration models.