Assessing a Length Artificial Intelligence Algorithm to Estimate Length of Children

NCT05079776 | Completed

• 1 other identifier: SBB20R&31696
• Study Type: observational
• Target: 200
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

Growth; Stunting, Nutritional

Keywords

growth; length; artificial intelligence; algorithm

Outcome Measures

Primary Outcomes (1)

• Accuracy of the length AI

  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

  2 days

Secondary Outcomes (3)

• Investigator's assessment on collection of images

  2 days

• Parental acceptability of the length AI

  2 days

• Investigators' (or delegates) acceptability of length AI

  2 days

Study Arms (1)

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

Other: Physical length measurement

Interventions

Physical length measurement OTHER

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

Children aged 0-18 months of age

Eligibility Criteria

• Age: 0 Months - 18 Months
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17)
• Sampling Method: Non-Probability Sample

Study Population

Children between 0-18 months of age with no physical deformities.

You may qualify if:

• 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.

You may not qualify if:

• 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).

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, 229899, Singapore

Location

Related Publications (2)

• 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.

  PMID: 41365661 DERIVED

• Chua 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.

  PMID: 39576977 DERIVED

MeSH Terms

Conditions

Growth Disorders

Condition Hierarchy (Ancestors)

Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Design

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

Study Record Dates

• First Submitted: October 4, 2021
• First Posted: October 15, 2021
• Study Start: November 8, 2021
• Primary Completion: March 8, 2022
• Study Completion: June 20, 2022
• Last Updated: July 27, 2022

Record last verified: 2022-07

Data Sharing

• IPD Sharing: Will not share

Locations

SG (1)