NCT07514182

Improving Artificial Intelligence-derived Algorithms for Estimating Length and Weight in NEonateS and infanTs up to 6 Months of Age (NEST)

Improving Artificial Intelligence-derived Algorithms for Estimating Length and Weight in NEonateS and infanTs up to 6 Months of Age

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 60 (estimated)
• Sponsor: Danone Asia Pacific Holdings Pte, Ltd. · Industry
• Sex: All
• Age: 0 Days – 6 Months
• Healthy volunteers: —

Summary

The NEST study is a prospective, observational research study designed to collect clinical measurements and image data to develop and evaluate artificial intelligence (AI)-derived algorithms for estimating anthropometric parameters in neonates and young infants. The study focuses on infants from birth up to 6 months of age and aims to assess the accuracy of AI-based estimations of length, weight, and head circumference using photographs and/or video recordings captured during routine clinical care. These AI-derived measurements will be compared against standard clinical measurements obtained by trained healthcare professionals in neonatal and infant care settings.

Detailed description

The NEST study is a prospective, observational cohort study designed to collect paired clinical reference measurements, image data, and associated clinical information to support the development and proof-of-concept evaluation of artificial intelligence (AI)-based algorithms for estimating anthropometric parameters in neonates and young infants. Standard clinical anthropometric measurements-including infant length, weight, and head circumference-are obtained by trained healthcare professionals in accordance with site-standard clinical procedures and established neonatal measurement practices. These measurements serve as the clinical reference standard for comparison with AI-derived estimates. All reference measurements collected as part of routine clinical care during the study period may be recorded. In parallel with clinical measurements, non-invasive image data consisting of two-dimensional photographs and/or video recordings of the infant are captured using digital imaging devices. Image capture occurs under real-world clinical conditions and does not require additional physical contact beyond routine care. Image and video data may be collected at multiple timepoints for a given participant, including repeated assessments during hospitalization or follow-up, where applicable. Image-based measurements are not used for clinical decision-making. AI-derived estimates are compared against standard clinical reference measurements using predefined analytical accuracy and agreement metrics. Secondary and exploratory objectives include the evaluation of AI models for additional anthropometric parameters, such as weight and head circumference, as well as assessment of the feasibility of image capture in neonatal and infant care settings. Investigator- and parent-reported perceptions related to the usability and acceptability of image-based measurement approaches are also evaluated. For participants with laboratory test results obtained as part of routine clinical care, selected laboratory values may be recorded. In a subset of participants, additional image data may be collected to support exploratory research related to AI-based estimation of iron status. No additional laboratory testing is performed as part of the study. Questionnaire-based feedback is collected from investigators and parents or caregivers on the image capture process and to define acceptable ranges of differences between AI-derived estimates and standard clinical measurements. and/or experiences and perceptions related to the use of AI-powered digital tools for monitoring infant growth parameters and health. All study data are coded prior to analysis. Image data and clinical measurements are linked using study-specific participant identifiers. No facial recognition or identity verification is performed. Data are stored, processed, and analyzed in accordance with approved data protection and confidentiality measures and applicable regulatory requirements.

Conditions

• Growth
• Neonates
• AI (Artificial Intelligence)

Timeline

• Start date: 2026-03-26
• Primary completion: 2026-10-01
• Completion: 2026-10-01
• First posted: 2026-04-07
• Last updated: 2026-04-13

Locations

1 site across 1 country: Singapore