Assessing a Height Artificial Intelligence Algorithm to Estimate Height of Children

NCT06578338 | Completed

• 1 other identifier: SBB20R&31696-A
• Study Type: observational
• Target: 250
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Growth; Stunting, Nutritional

Keywords

Growth; Height; Artificial Intelligence; Algorithm

Outcome Measures

Primary Outcomes (1)

• Accuracy of the height AI (cm)

  Accuracy of the Height AI (cm) in a clinic and in a home setting, derived from: 1. The height AI prediction from images collected 2. The physical height measurements of subjects using WHO standard height measurement

  2 days

Secondary Outcomes (1)

• Accuracy of the Weight AI (kg)

  2 days

Other Outcomes (3)

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

  2 days

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

  2 days

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

  2 days

Study Arms (1)

Children aged above 24 months old and below 6 years of age

Children aged above 24 months old and below 6 years of age with no structural abnormalities of the lower limbs or orthopaedic conditions

Other: Physical height measurement

Interventions

Physical height measurement OTHER

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

Children aged above 24 months old and below 6 years of age

Eligibility Criteria

• Age: 24 Months - 6 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17)
• Sampling Method: Non-Probability Sample

Study Population

Children aged above 24 months old and below 6 years of age with no physical deformities

You may qualify if:

• 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

You may not qualify if:

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

Sponsors & Collaborators

• Danone Asia Pacific Holdings Pte, Ltd.: lead
• KK Women's and Children's Hospital: collaborator

Study Sites (1)

KK Women's and Children's Hospital

Singapore, Singapore

Location

Related Publications (2)

• Yap F, Lee YS, Aw MMH. Growth Assessment and Monitoring during Childhood. Ann Acad Med Singap. 2018 Apr;47(4):149-155.

  PMID: 29777245 BACKGROUND

• Schmidhuber J. Deep learning in neural networks: an overview. Neural Netw. 2015 Jan;61:85-117. doi: 10.1016/j.neunet.2014.09.003. Epub 2014 Oct 13.

  PMID: 25462637 BACKGROUND

MeSH Terms

Conditions

Growth Disorders

Condition Hierarchy (Ancestors)

Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• Fabian Yap, MBBS

  KK Women's and Children's Hospital

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: PROSPECTIVE
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: August 5, 2024
• First Posted: August 29, 2024
• Study Start: September 9, 2024
• Primary Completion: March 4, 2025
• Study Completion: March 4, 2025
• Last Updated: February 4, 2026

Record last verified: 2026-02

Data Sharing

• IPD Sharing: Will not share

Locations

SG (1)