Assessing a Height Artificial Intelligence Algorithm to Estimate Height of Children

NCT ID: NCT06578338

Last Updated: 2025-08-20

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

ACTIVE_NOT_RECRUITING

Total Enrollment

200 participants

Study Classification

OBSERVATIONAL

Study Start Date

2024-09-09

Study Completion Date

2025-11-30

Brief Summary

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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.

Detailed Description

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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.

Conditions

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Growth; Stunting, Nutritional

Study Design

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Observational Model Type

OTHER

Study Time Perspective

PROSPECTIVE

Study Groups

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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

Physical height measurement

Intervention Type OTHER

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

Interventions

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Physical height measurement

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

Intervention Type OTHER

Eligibility Criteria

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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
Minimum Eligible Age

24 Months

Maximum Eligible Age

6 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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KK Women's and Children's Hospital

OTHER_GOV

Sponsor Role collaborator

Danone Asia Pacific Holdings Pte, Ltd.

INDUSTRY

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Fabian Yap, MBBS

Role: PRINCIPAL_INVESTIGATOR

KK Women's and Children's Hospital

Locations

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KK Women's and Children's Hospital

Singapore, , Singapore

Site Status

Countries

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Singapore

References

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

Reference Type BACKGROUND
PMID: 29777245 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 25462637 (View on PubMed)

Other Identifiers

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SBB20R&31696-A

Identifier Type: -

Identifier Source: org_study_id

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