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Completed

NCT Number: NCT06578338

Assessing a Height Artificial Intelligence Algorithm to Estimate Height of Children

An exploratory study to explore the possibility of using computer vision algorithms to estimate a child's height using images taken by a healthcare professional or parents.

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Key information

Conditions

Growth; Stunting, Nutritional Growth Disorders Pathologic Processes Pathological Conditions, Signs and Symptoms

Age range

24 month–6 year

Sex eligibility

All sexes

Study type

Observational

Primary location

KK Women's and Children's Hospital

Singapore

About this study

This is an exploratory, observation, data-collection study that aims to evaluate the performance of a Height Artificial Intelligence (HAI) algorithm in a real world setting. Images will be collected by parents or healthcare professionals, together with physical height measurements. This data will be used to evaluate the accuracy of the algorithm and to explore potential improvements. Data on the acceptance and experience of using the algorithm will be collected for improvements.

Who can participate

Healthy volunteers accepted: Yes

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

Inclusion criteria

  • Children aged above 24 months old and below 6 years old.
  • Parent(s) should have access to the internet and a smartphone or table to complete study questionnaires, take images and upload images.
  • Parent(s) should be able to comprehend the content of the study and complete the study questionnaires in English.
  • Written consent from parents and/or legally acceptable representative

Exclusion criteria

  • Children who are unable to stand upright against a wall
  • Children who are unable to cooperate with standing height measurement

Treatment and study plan

Physical height measurement

Other

Physical height will be measured and images will be collected for AI to estimate the height

Primary outcomes

  1. Accuracy of the height AI (cm)

    Time frame: 2 days

    Accuracy of the Height AI (cm) in a clinic and in a home setting, derived from:

    • The height AI prediction from images collected
    • The physical height measurements of subjects using WHO standard height measurement

Secondary outcomes

  1. Accuracy of the Weight AI (kg)

    Time frame: 2 days

    Accuracy of the Weight AI (kg) in a clinic and in a home setting, derived from:

    • The weight prediction from the standing images collected in this study
    • The WHO standard weight measurement of subjects

Other outcomes

  1. Assessments by the parent on usability of the AI in a home-setting via study questionnaire

    Time frame: 2 days

    • Ease of collecting images (very easy to very difficult)
    • Acceptable range of height & weight difference between AI prediction & WHO measurement (between ≤1cm and >5cm)
    • Frequency of measuring child's height & weight at home (never,< 1/month, at least <1/month, at least 1 per 2 weeks,at least 1/week,> 1/week,daily)
    • Usefulness of using digital tool to measure child's height & weight at home (Very useful to Not very useful)
    • Frequency of using digital tool to measure child's height & weight at home (never,<1/month, at least 1/month, at least 1 per 2 weeks,at least 1/week,>1/week,daily)
    • Likelihood and reason of using a digital tool to measure child's height & weight at home (Very Likely to Very unlikely)
    • Sharing the child's height & weight measured using digital tool with others (Very Likely to Very unlikely)
    • Use of mobile apps to track child's height & weight (free text)
    • Other features/tools useful to measure child's height & weight (free text)

  2. Assessments by the investigator on usability of the AI in a clinic-setting via a questionnaire

    Time frame: 2 days

    • Acceptable range of height and weight difference between the height and weight AI from the WHO measurement (≤ 1cm, ≤ 2cm, ≤ 3cm, ≤ 4cm, ≤5cm, >5cm)
    • Likelihood of using a digital tool to measure a child's height and weight in a clinical setting (Very Likely, Likely, Neutral, Unlikely, Very unlikely)
    • Likelihood of recommending parents to use a digital tool to measure their child's height and weight in a clinical setting (Very Likely, Likely, Neutral, Unlikely, Very unlikely)
    • Other features/tools they find useful to measure the child's height and weight (free text)

  3. Assessments by the investigator on ease of collecting images in a clinic-setting via a questionnaire

    Time frame: 2 days

    Investigator's assessment on the ease of collecting the images [Very Easy, Easy, Normal, Difficult, Very Difficult]

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

A Study to Collect Data to Build Artificial INtelligence Derived Algorithms For Estimating Height and Weight in childRen (INFER)

Acronym: INFER

Important dates

Study start
2024
Primary completion
2025
Study completion
2025
First posted
Aug 29, 2024
Registry last updated
Feb 4, 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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