Growth; Stunting, NutritionalINFERNCT06578338
Assessing a Height Artificial Intelligence Algorithm to Estimate Height of Children
An observational study of Growth; Stunting, Nutritional, run by Danone Asia Pacific Holdings Pte, Ltd. at 1 site in Singapore.
Trial 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. Read more about this study
- Who is it for?
- Ages 24 months to 6 yearsHealthy volunteers can take part
- Conditions the study is for
- Growth; Stunting, Nutritional
- What is being tested?
- Physical height measurement
- How many people
- 250 participants
- Where
- 1 siteSingapore
- Study dates
- 9 Sep 2024to 4 Mar 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 24 months to 6 years
- All sexes
- Healthy volunteers accepted
- Child
Study population: Children aged above 24 months old and below 6 years of age with no physical deformities
Who may be able to join
- 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
Who cannot join
- Children who are unable to stand upright against a wall
- Children who are unable to cooperate with standing height measurement
Trial locations and contacts
1 site in 1 country.
About this study
Official title: A Study to Collect Data to Build Artificial INtelligence Derived Algorithms For Estimating Height and Weight in childRen (INFER)
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.
Read the detailed description
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
- Growth; Stunting, Nutritional
- Being tested
- Physical height measurement
- Keywords
- Growth, Height, Artificial Intelligence, Algorithm
- Medical subject headings
- Growth Disorders
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Physical height measurementOther
Physical height will be measured and images will be collected for AI to estimate the height
| Study group | Role | What they receive |
|---|---|---|
| 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 Receives: Other: Physical height measurement |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of the height AI (cm)
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
Measured: 2 days
Secondary outcomes (1)
Accuracy of the Weight AI (kg)
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
Measured: 2 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted29 Aug 2024
- Study start9 Sep 2024
- Primary completion4 Mar 2025
- Study completion4 Mar 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for Growth; Stunting, NutritionalStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Prospective
- Enrolment
- 250 participants (actual)
Sponsor and investigators
- Lead sponsor
- Danone Asia Pacific Holdings Pte, Ltd.
- Sponsor type
- Industry (pharma, biotech, devices)
- Collaborators
- KK Women's and Children's Hospital
- Responsible party
- Sponsor
- Investigators
- Fabian Yap, MBBS, Principal investigator, KK Women's and Children's Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06578338
- Sponsor's protocol ID
- SBB20R&31696-A
- Acronym
- INFER
- Status last verified
- Feb 2026
- First submitted
- 5 Aug 2024
- Last updated
- 4 Feb 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Yap F, Lee YS, Aw MMH. Growth Assessment and Monitoring during Childhood. Ann Acad Med Singap. 2018 Apr;47(4):149-155.
PubMed 29777245Schmidhuber 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.
PubMed 25462637
Study at a glance
From the registry record, last updated by the sponsor on 4 Feb 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Study to Collect Data to Build Artificial INtelligence Derived Algorithms For Estimating Height and Weight in childRen (INFER)
- ClinicalTrials.gov ID
- NCT06578338
- Acronym / sponsor ID
- INFER / SBB20R&31696-A
- Conditions
- Growth; Stunting, Nutritional
- Keywords
- Growth, Height, Artificial Intelligence, Algorithm
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 250 participants (actual)
- Lead sponsor
- Danone Asia Pacific Holdings Pte, Ltd.Industry (pharma, biotech, devices)
- Collaborators
- KK Women's and Children's Hospital
- Study start
- 9 Sep 2024 (actual)
- Primary completion
- 4 Mar 2025 (actual)
- Study completion
- 4 Mar 2025 (actual)
- First posted
- 29 Aug 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
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.
Study design
- Observational model
- Other
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Children aged above 24 months old and below 6 years of age | – | Other: Physical height measurementChildren aged above 24 months old and below 6 years of age with no structural abnormalities of the lower limbs or orthopaedic conditions |
Who may qualify
- Ages
- 24 months to 6 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child
- Study population
- Children aged above 24 months old and below 6 years of age with no physical deformities
Key inclusion criteria (4 listed)
- 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
Key exclusion criteria (2 listed)
- Children who are unable to stand upright against a wall
- Children who are unable to cooperate with standing height measurement
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Physical height measurementPhysical height will be measured and images will be collected for AI to estimate the height | Other | Children aged above 24 months old and below 6 years of age |
Primary outcome
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: 1. The height AI prediction from images collected 2. The physical height measurements of subjects using WHO standard height measurement
1 secondary outcome
Accuracy of the Weight AI (kg)
Time frame: 2 days
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 4 Feb 2026.
- Background: PubMed 29777245Yap F, Lee YS, Aw MMH. Growth Assessment and Monitoring during Childhood. Ann Acad Med Singap. 2018 Apr;47(4):149-155.
- Background: PubMed 25462637Schmidhuber 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.
- Individual participant dataSharing planned: No
Related trials for Growth; Stunting, Nutritional
RecruitingObservational study
Evaluation of Children With Endocrine and Metabolic-Related Conditions
Adrenal Insufficiency, Growth Disorder
Recruiting
Hens Hatching Hope
Malnutrition, Child
Recruiting
Effectiveness of Small-Quantity vs Medium-Quantity Lipid-Based Nutrient Supplements for Prevention of Undernutrition in Young Children
Undernutrition
RecruitingObservational study
Stunting and Targeting Reduction of Impaired Growth in Kids Effectively
Malnutrition (Calorie)
RecruitingObservational study
Placental Biology in Health and Disease
Pre-eclampsia, Gestational Diabetes Mellitus (GDM)
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Fabian Yap, MBBS principal investigator
KK Women's and Children's Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Growth; Stunting, Nutritional, run by Danone Asia Pacific Holdings Pte, Ltd. at 1 site in Singapore.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 4 Feb 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Singapore. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of the height AI (cm) (time frame: 2 days). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06578338, which also lists 2 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 4 Feb 2026.