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Assessing a Length Artificial Intelligence Algorithm to Estimate Length of Children

Assessing the Use of a Growth Artificial Intelligence Algorithm for Estimating the leNgth of Children in Real-world Setting

Status
Completed
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT05079776
Acronym
GAIN
Enrollment
200
Registered
2021-10-15
Start date
2021-11-08
Completion date
2022-06-20
Last updated
2022-07-27

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Growth; Stunting, Nutritional

Keywords

growth, length, artificial intelligence, algorithm

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.

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.

Interventions

OTHERPhysical length measurement

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

Sponsors

KK Women's and Children's Hospital
CollaboratorOTHER_GOV
Danone Asia Pacific Holdings Pte, Ltd.
Lead SponsorINDUSTRY

Study design

Observational model
COHORT
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
0 Months to 18 Months
Healthy volunteers
Yes

Inclusion criteria

1. Children aged 0 to 18 months old. 2. Parent(s) should have access to the internet and a smartphone or tablet to complete study questionnaires, take images and upload images. 3. Parent(s) should be able to comprehend the content of the study and to complete the study questionnaires in English. 4. Written consent from parent.

Exclusion criteria

1. Parent(s) incapable of completing the study questionnaires and uploading of the images using smart phone or tablet with internet. 2. 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).

Design outcomes

Primary

MeasureTime frameDescription
Accuracy of the length AI2 daysAccuracy 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

Secondary

MeasureTime frameDescription
Investigator's assessment on collection of images2 daysInvestigator's assessment on the ease of collecting the images \[Very Easy, Easy, Normal, Difficult, Very Difficult\]
Parental acceptability of the length AI2 daysParental acceptability of length AI assessed via the study questionnaire \[Very useful, useful, neutral, not useful, very not useful\]
Investigators' (or delegates) acceptability of length AI2 daysInvestigators' (or delegates) likelihood of using the length AI assessed via the study questionnaire \[Very Likely, Likely, Neutral, Unlikely, Very Unlikely\]

Countries

Singapore

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 5, 2026