Growth; Stunting, NutritionalGAINNCT05079776
Assessing a Length Artificial Intelligence Algorithm to Estimate Length 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 length using images taken by a healthcare professional or parents. Read more about this study
- Who is it for?
- Ages 0 to 18 monthsHealthy volunteers can take part
- Conditions the study is for
- Growth; Stunting, Nutritional
- What is being tested?
- Physical length measurement
- How many people
- 200 participants
- Where
- 1 siteSingapore
- Study dates
- 8 Nov 2021to 20 Jun 2022
- 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 0 to 18 months
- All sexes
- Healthy volunteers accepted
- Child
Study population: Children between 0-18 months of age with no physical deformities.
Who may be able to join
- Children aged 0 to 18 months old.
- Parent(s) should have access to the internet and a smartphone or tablet to complete study questionnaires, take images and upload images.
- Parent(s) should be able to comprehend the content of the study and to complete the study questionnaires in English.
- Written consent from parent.
Who cannot join
- Parent(s) incapable of completing the study questionnaires and uploading of the images using smart phone or tablet with internet.
- Children unable to undergo length measurement (e.g. children with structural abnormalities of the lower limbs or orthopedic conditions such as club foot, hip dysplasia, etc).
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Assessing the Use of a Growth Artificial Intelligence Algorithm for Estimating the leNgth of Children in Real-world Setting
An exploratory study to explore the possibility of using computer vision algorithms to estimate a child's length using images taken by a healthcare professional or parents.
Read the detailed description
This is an exploratory, observational, pilot study that aims to evaluate the performance of a Length Artificial Intelligence (LAI) algorithm in a real world setting. Images will be collected by parents or healthcare professionals, together with physical length 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 the using the algorithm will be collected for improvements.
- Conditions
- Growth; Stunting, Nutritional
- Being tested
- Physical length measurement
- Keywords
- growth, length, 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 length measurementOther
Physical length will be measured and images will be collected for AI to estimate the length
| Study group | Role | What they receive |
|---|---|---|
| Children aged 0-18 months of age | Children aged 0-18 months of age with no structural abnormalities of the lower limbs or orthopedic conditions Receives: Other: Physical length measurement |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of the length AI
Accuracy of the length AI in a clinic and in a home setting, derived from:
- The length AI prediction from images collected
- The physical length measurement of subjects
Measured: 2 days
Secondary outcomes (3)
Investigator's assessment on collection of images
Investigator's assessment on the ease of collecting the images [Very Easy, Easy, Normal, Difficult, Very Difficult]
Measured: 2 days
Parental acceptability of the length AI
Parental acceptability of length AI assessed via the study questionnaire [Very useful, useful, neutral, not useful, very not useful]
Measured: 2 days
Investigators' (or delegates) acceptability of length AI
Investigators' (or delegates) likelihood of using the length AI assessed via the study questionnaire [Very Likely, Likely, Neutral, Unlikely, Very Unlikely]
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 posted15 Oct 2021
- Study start8 Nov 2021
- Primary completion8 Mar 2022
- Study completion20 Jun 2022
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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 200 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
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05079776
- Sponsor's protocol ID
- SBB20R&31696
- Acronym
- GAIN
- Status last verified
- Jul 2022
- First submitted
- 4 Oct 2021
- Last updated
- 27 Jul 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Chan D, Chua MC, Hadimaja M, Mukherjee S, Wong J, Yap F. Artificial intelligence-driven anthropometric assessment for young children: evaluating the accuracy and practicality of a digital image-based length and weight prediction tool. BMJ Health Care Inform. 2025 Dec 9;32(1):e101540. doi: 10.1136/bmjhci-2025-101540.
PubMed 41365661Chua MC, Hadimaja M, Wong J, Mukherjee SS, Foussat A, Chan D, Nandal U, Yap F. Exploring the Use of a Length AI Algorithm to Estimate Children's Length from Smartphone Images in a Real-World Setting: Algorithm Development and Usability Study. JMIR Pediatr Parent. 2024 Nov 22;7:e59564. doi: 10.2196/59564.
PubMed 39576977
Study at a glance
From the registry record, last updated by the sponsor on 27 Jul 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Assessing the Use of a Growth Artificial Intelligence Algorithm for Estimating the leNgth of Children in Real-world Setting
- ClinicalTrials.gov ID
- NCT05079776
- Acronym / sponsor ID
- GAIN / SBB20R&31696
- Conditions
- Growth; Stunting, Nutritional
- Keywords
- growth, length, artificial intelligence, algorithm
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 200 participants (actual)
- Lead sponsor
- Danone Asia Pacific Holdings Pte, Ltd.Industry (pharma, biotech, devices)
- Collaborators
- KK Women's and Children's Hospital
- Study start
- 8 Nov 2021 (actual)
- Primary completion
- 8 Mar 2022 (actual)
- Study completion
- 20 Jun 2022 (actual)
- First posted
- 15 Oct 2021 (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 length using images taken by a healthcare professional or parents.
Detailed description
This is an exploratory, observational, pilot study that aims to evaluate the performance of a Length Artificial Intelligence (LAI) algorithm in a real world setting. Images will be collected by parents or healthcare professionals, together with physical length 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 the using the algorithm will be collected for improvements.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Children aged 0-18 months of age | – | Other: Physical length measurementChildren aged 0-18 months of age with no structural abnormalities of the lower limbs or orthopedic conditions |
Who may qualify
- Ages
- 0 to 18 months
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child
- Study population
- Children between 0-18 months of age with no physical deformities.
Key inclusion criteria (4 listed)
- Children aged 0 to 18 months old.
- Parent(s) should have access to the internet and a smartphone or tablet to complete study questionnaires, take images and upload images.
- Parent(s) should be able to comprehend the content of the study and to complete the study questionnaires in English.
- Written consent from parent.
Key exclusion criteria (2 listed)
- Parent(s) incapable of completing the study questionnaires and uploading of the images using smart phone or tablet with internet.
- Children unable to undergo length measurement (e.g. children with structural abnormalities of the lower limbs or orthopedic conditions such as club foot, hip dysplasia, etc).
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 length measurementPhysical length will be measured and images will be collected for AI to estimate the length | Other | Children aged 0-18 months of age |
Primary outcome
Accuracy of the length AI
Time frame: 2 days
Accuracy of the length AI in a clinic and in a home setting, derived from: 1. The length AI prediction from images collected 2. The physical length measurement of subjects
3 secondary outcomes
Investigator's assessment on collection of images
Time frame: 2 days
Parental acceptability of the length AI
Time frame: 2 days
Investigators' (or delegates) acceptability of length AI
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 27 Jul 2022.
- Derived from this study: PubMed 41365661Chan D, Chua MC, Hadimaja M, Mukherjee S, Wong J, Yap F. Artificial intelligence-driven anthropometric assessment for young children: evaluating the accuracy and practicality of a digital image-based length and weight prediction tool. BMJ Health Care Inform. 2025 Dec 9;32(1):e101540. doi: 10.1136/bmjhci-2025-101540.
- Derived from this study: PubMed 39576977Chua MC, Hadimaja M, Wong J, Mukherjee SS, Foussat A, Chan D, Nandal U, Yap F. Exploring the Use of a Length AI Algorithm to Estimate Children's Length from Smartphone Images in a Real-World Setting: Algorithm Development and Usability Study. JMIR Pediatr Parent. 2024 Nov 22;7:e59564. doi: 10.2196/59564.
- Individual participant dataSharing planned: No
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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.
The record lists no contact. Use the official record to reach the sponsor.
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 27 Jul 2022. 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 length AI (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 NCT05079776, 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 27 Jul 2022.