Clinical trial NCT06578338: Assessing a Height Artificial Intelligence Algorithm to Estimate Height of Children
Official title: A Study to Collect Data to Build Artificial INtelligence Derived Algorithms For Estimating Height and Weight in childRen (INFER)
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Growth; Stunting, Nutritional
Keywords: Growth
Height
Artificial Intelligence
Algorithm
Lead sponsor: Danone Asia Pacific Holdings Pte, Ltd.
Sponsor class: INDUSTRY
Collaborators: name: KK Women's and Children's Hospital
First posted: 2024-08-29
Enrollment: 200
Sex: ALL
Minimum age (years): 24
Maximum age (years): 6
Standard ages: CHILD
Accepts healthy volunteers: yes
Observational model: OTHER
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Children aged above 24 months old and below 6 years of age with no physical deformities

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.

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.

Eligibility criteria: 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

Primary outcomes:
measure: Accuracy of the height AI (cm)
timeFrame: 2 days
Secondary outcomes:
measure: Accuracy of the Weight AI (kg)
timeFrame: 2 days
Other outcomes:
measure: Assessments by the parent on usability of the AI in a home-setting via study questionnaire
timeFrame: 2 days
measure: Assessments by the investigator on usability of the AI in a clinic-setting via a questionnaire
timeFrame: 2 days
measure: Assessments by the investigator on ease of collecting images in a clinic-setting via a questionnaire
timeFrame: 2 days
Locations:
city: Singapore
country: Singapore
facility: KK Women's and Children's Hospital
Study officials:
affiliation: KK Women's and Children's Hospital
name: Fabian Yap, MBBS
role: PRINCIPAL_INVESTIGATOR
References:
citation: Yap F, Lee YS, Aw MMH. Growth Assessment and Monitoring during Childhood. Ann Acad Med Singap. 2018 Apr;47(4):149-155.
citation: 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.