Length, Weight
Conditions
Keywords
artificial intelligence, length, weight, algorithm
Brief summary
This is a data collection and machine learning accuracy testing project that aims to a) collect training data to enhance, by machine learning, an artificial intelligence (AI) algorithm for measuring length in infants and young children and b) test the accuracy of the AI algorithm by comparing the AI predicted length with the gold standard measured length. Images and videos will be collected by care givers and healthcare professionals, together with physical length measurements. These data will be used to train the AI algorithm and to explore potential improvements. Other data to be collected is user experience feedback.
Interventions
None listed
Sponsors
Nutricia Research
Study design
Observational model
OTHER
Time perspective
OTHER
Eligibility
Sex/Gender
ALL
Age
0 Months to 24 Months
Healthy volunteers
Yes
Inclusion criteria
1. Infants/young children aged 0 to 24 months old at enrolment; 2. Caregiver(s) have access to the internet and a smartphone or tablet to complete questionnaires, take images and videos, and upload these; 3. Written informed consent from one or both caregivers (according to local laws) or legally acceptable representative(s) aged ≥ 18 years at enrolment.
Exclusion criteria
1. Children unable to undergo length or weight measurements when applying standardized techniques recommended by the WHO (e.g. infants/children with structural abnormalities of the lower limbs or orthopaedic conditions (e.g. club foot, hip dysplasia)). 2. Research staff's uncertainty about the caregiver's ability or willingness to complete the study according to the instructions
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Accuracy length AI | Date of enrolment, at baseline | Accuracy of the Length AI vs length gold standard (WHO methodology with length board in cm) assessed using several different parameters: the bias (cm), agreement and reliability measures, mean absolute error (cm), mean absolute percentage error (%), percentiles of the absolute error (cm), and root mean square error (cm). |
| Accuracy caregiver length | Date of enrolment, at baseline | Accuracy of the caregiver measured length (own preferred methodology in cm) vs gold standard measured length (WHO methodology with length board in cm), assessed by same parameters as mentioned in the first primary outcome measure. |
| Accuracy caregiver vs AI length | Date of enrolment, at baseline | Accuracy of the caregiver measurements (self preferred methodology in cm) vs Length AI by same parameters as mentioned in the first primary outcome measure. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Accuracy weight AI | Date of enrolment, at baseline | Accuracy of the Weight AI vs weight gold standard (WHO methodology with digital scale and tared weighing in kg) assessed using several different parameters: the bias (kg), agreement and reliability measures, mean absolute error (kg), mean absolute percentage error (%), percentiles of the absolute error (kg), and root mean square error (kg). |
| Accuracy caregiver weight | Date of enrolment, at baseline | Accuracy of the caregiver measured weight (self preferred methodology in kg) vs weight gold standard (WHO methodology with digital scale and tared weighing in kg), assessed using several different parameters as mentioned in the first secondary outcome measure. |
| Ease of use tool | Date of enrolment, at baseline | The ease of taking images and videos with the tool "GAINS app" on personal mobile device of the caregiver, assessed via a custom made user experience questionnaire. |
| Acceptability tool | Date of enrolment, at baseline | The expectation and acceptability of the tool "GAINS app" on a personal mobile device of the caregiver, assessed via a custom made user experience questionnaire. |
Countries
Netherlands, Poland, Spain
Outcome results
None listed