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

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

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.

Study Overview

• Status: Recruiting
• Conditions: Growth; Neonates; AI (Artificial Intelligence)

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.

• Study Type: Observational
• Enrollment (Estimated): 60

Contacts and Locations

• Study Contact: Name: Amilia Sng, Senior Digital Health R&I Study Manager, MSc Pharmacology; Phone Number: +6594773284; Email: amilia.sng@danone.com
• Study Contact Backup: Name: Kimberley Tan, Clinical Research Associate, BSc; Phone Number: +6598359119; Email: kimberley.tan@external.danone.com

Study Locations

• Singapore
  • Singapore, Singapore
    • Recruiting
    • KK Women's and Children's Hospital
    • Contact: Bin Huey Quek, MBBS

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child
• Accepts Healthy Volunteers: N/A

• Sampling Method: Non-Probability Sample

Study Population

The study population comprises neonates and infants from birth up to 6 months of age who are admitted to a neonatal intensive care unit (NICU) or special care nursery (SCN). Eligible participants include very preterm, moderate to late preterm, and term infants who meet the study's inclusion criteria and whose parents or legally acceptable representatives provide written informed consent. Participants are enrolled during their NICU/SCN stay and followed prospectively for the duration of their hospitalization, up to a maximum of 10 weeks. The study collects anthropometric measurements and image and video data in conjunction with routine clinical care.

Description

Inclusion Criteria:

1. Infants up from birth up to 6 months of postnatal age (including neonates) who have been admitted to the NICU or SCN at the time of screening
2. Parent(s) should be able to comprehend the content of the study and be willing for their child to undergo video and photo recording, and to allow access to their blood sampling results (haemoglobin) taken as part of standard clinical practice
3. Written consent from parents and/or legally acceptable representative

Exclusion Criteria:

1. Infants who were born with gestational age of less than 28 weeks of gestational age
2. Infants who are intubated (i.e., endotracheal, nasotracheal intubation) at the time of screening
3. The investigator considers for any reason that the participant would not be suitable for the study
4. The participant has an existing medical condition that would prevent standardised measurement of length and/or head circumference e.g. structural abnormality of the lower limbs, orthopaedic conditions, hydrocephalus
5. Employees and/or children/family members or relatives of employees of Danone Global Research & Innovation Center, Danone Asia Pacific Holdings Pte Ltd, or the participating site

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Very preterm infants, moderate to late preterm infants, and term infants; This cohort includes neonates and infants from birth up to 6 months of age, encompassing very preterm (28-31 weeks gestation), moderate to late preterm (32-36 weeks gestation), and term infants (≥37 weeks gestation). Participants are enrolled prospectively during their stay in neonatal or infant care settings. This is an observational study; no investigational product or therapeutic intervention is administered. Study procedures involve the collection of routine clinical anthropometric measurements (length, weight, and head circumference) and the capture of photographs and/or video recordings for the development and evaluation of artificial intelligence-derived algorithms. All data are collected in conjunction with standard clinical care.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
To evaluate the accuracy of the algorithm to estimate length (in cm)	The primary outcome is the proof-of-concept accuracy of an artificial intelligence (AI)-based algorithm for estimating infant length in a neonatal intensive care unit (NICU) or special care nursery (SCN) setting. AI-derived length estimates (in centimeters) obtained from supine images and/or videos are compared with standard clinical length measurements performed by trained investigators using World Health Organization (WHO)-recommended techniques. Accuracy is evaluated using a composite metric that includes bias, mean absolute error, mean absolute percentage error, and the distribution of absolute percentage errors at predefined thresholds.	From enrolment (after informed consent) until discharge from NICU/SCN, up to a maximum of 10 weeks

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
To evaluate the mean absolute error of the algorithm to estimate weight (in kg)	The secondary outcome is the proof-of-concept accuracy of an artificial intelligence (AI)-based algorithm for estimating infant weight in a neonatal intensive care unit (NICU) or special care nursery (SCN) setting. AI-derived weight estimates (in kilograms) generated from supine images and/or videos are compared with standard clinical weight measurements obtained by trained investigators using World Health Organization (WHO)-recommended techniques. Accuracy is assessed using a composite metric that includes bias, mean absolute error, mean absolute percentage error, and the distribution of absolute percentage errors at predefined thresholds.	From enrolment (after informed consent) until discharge from NICU/SCN, up to a maximum of 10 weeks

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
To evaluate the mean absolute error of the algorithm to estimate head circumference (in cm)	The exploratory outcome evaluates the proof-of-concept accuracy of an artificial intelligence (AI)-based algorithm for estimating infant head circumference in a neonatal intensive care unit (NICU) or special care nursery (SCN) setting. AI-derived head circumference estimates obtained from images and/or videos are compared with standard clinical head circumference measurements performed by trained investigators using World Health Organization (WHO)-recommended techniques. Accuracy is assessed descriptively using appropriate error metrics to explore the feasibility and potential clinical utility of image-based AI estimation methods.	From enrolment (after informed consent) until discharge from NICU/SCN, up to a maximum of 10 weeks
Ease of image collection by investigator for each participant (Very Easy, Easy, Normal, Difficult, Very difficult]	Investigator's assessment on the ease of collecting the images	Once per participant, prior to discharge from NICU/SCN (at the conclusion of study participation) [1 day]
Expected level of accuracy by the investigator for length [Less than or equal to 1 cm, less than or equal to 2 cm, less than or equal to 3 cm, less than or equal to 4 cm, less than or equal to 5 cm, more than 5 cm]	The expected level of accuracy for the anthropometric measurements for length	Per participant, at the conclusion of study participation (prior to discharge from NICU/SCN) [1 day]
To assess the mean absolute error of the Iron algorithm to estimate hemoglobin level (mg/dl) using image recognition technology	This exploratory outcome evaluates the proof-of-concept (POC) accuracy of an artificial intelligence (AI)-based algorithm for estimating haemoglobin levels (Iron AI) in a neonatal intensive care unit (NICU) or special care nursery (SCN) setting. AI-derived haemoglobin estimates (g/dL), generated from images and/or videos collected as part of the study, are compared with haemoglobin values obtained from standard venous blood sampling performed as part of routine clinical care. Accuracy is assessed using a composite metric including bias, mean absolute error, mean absolute percentage error, and predefined percentiles of absolute percentage error. This outcome is exploratory and descriptive in nature.	At enrolment or when routine clinical haemoglobin results become available during the NICU/SCN stay (single time point) [ 1day]

Collaborators and Investigators

• Sponsor: Danone Asia Pacific Holdings Pte, Ltd.
• Collaborators: KK Women's and Children's Hospital
• Investigators: Principal Investigator: Bin Huey Quek, MBBS, KK Women's and Children's Hospital, Singapore

Study record dates

Study Major Dates

• Study Start (Actual): March 26, 2026
• Primary Completion (Estimated): October 1, 2026
• Study Completion (Estimated): October 1, 2026

Study Registration Dates

• First Submitted: March 26, 2026
• First Submitted That Met QC Criteria: April 1, 2026
• First Posted (Actual): April 7, 2026

Study Record Updates

• Last Update Posted (Actual): April 13, 2026
• Last Update Submitted That Met QC Criteria: April 8, 2026
• Last Verified: April 1, 2026

More Information

Terms related to this study

• Keywords: Artificial Intelligence; Infants; Digital Health; Observational study; Neonates; Neonatal intensive care unit; Head Circumference; Anthropometric measurements; Proof of concept study; Neonatal Growth; Infant Growth assessment; Infant length measurements; Infant weight measurement; Image based assessment; Photographs and video recordings; Special Care nursery; Growth monitoring
• Other Study ID Numbers: 25REX0090391

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: Individual participant data will not be shared. Data collected in this study will be used solely for the purposes described in the protocol and handled in accordance with applicable data protection regulations and data processing agreements.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No