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NCT Number: NCT07514182

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

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.

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Key information

Age range

0 day–6 month

Sex eligibility

All sexes

Study type

Observational

Primary location

KK Women's and Children's Hospital

Singapore

Location status: Recruiting

Location contact

Bin Huey Quek, MBBS

CONTACT

About this study

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.

Who can participate

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • 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
  • 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
  • Written consent from parents and/or legally acceptable representative

Exclusion criteria

  • Infants who were born with gestational age of less than 28 weeks of gestational age
  • Infants who are intubated (i.e., endotracheal, nasotracheal intubation) at the time of screening
  • The investigator considers for any reason that the participant would not be suitable for the study
  • 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
  • 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

Treatment and study plan

Primary outcomes

  1. To evaluate the accuracy of the algorithm to estimate length (in cm)

    Time frame: From enrolment (after informed consent) until discharge from NICU/SCN, up to a maximum of 10 weeks

    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.

Secondary outcomes

  1. To evaluate the mean absolute error of the algorithm to estimate weight (in kg)

    Time frame: From enrolment (after informed consent) until discharge from NICU/SCN, up to a maximum of 10 weeks

    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.

Other outcomes

  1. To evaluate the mean absolute error of the algorithm to estimate head circumference (in cm)

    Time frame: From enrolment (after informed consent) until discharge from NICU/SCN, up to a maximum of 10 weeks

    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.

  2. Ease of image collection by investigator for each participant (Very Easy, Easy, Normal, Difficult, Very difficult]

    Time frame: Once per participant, prior to discharge from NICU/SCN (at the conclusion of study participation) [1 day]

    Investigator's assessment on the ease of collecting the images

  3. 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]

    Time frame: Per participant, at the conclusion of study participation (prior to discharge from NICU/SCN) [1 day]

    The expected level of accuracy for the anthropometric measurements for length

  4. To assess the mean absolute error of the Iron algorithm to estimate hemoglobin level (mg/dl) using image recognition technology

    Time frame: At enrolment or when routine clinical haemoglobin results become available during the NICU/SCN stay (single time point) [ 1day]

    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.

Study contacts

Contact information is provided by the study sponsor or research team.

Amilia Sng, Senior Digital Health R&I Study Manager, MSc Pharmacology

CONTACT

[email protected]

+6594773284

Kimberley Tan, Clinical Research Associate, BSc

CONTACT

[email protected]

+6598359119

Sponsors and collaborators

Lead sponsor

Danone Asia Pacific Holdings Pte, Ltd.

Industry

Collaborators

  • KK Women's and Children's Hospital

Registry information

Official study title

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

Acronym: NEST

Important dates

Study start
2026
Primary completion
2026
Study completion
2026
First posted
Apr 7, 2026
Registry last updated
Apr 13, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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