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.
Study Type
OBSERVATIONAL
Enrollment
250
Franciscus Gasthuis
Rotterdam, Netherlands
Ginemedica
Wroclaw, Poland
Hospital Universitario Puerta del Mar
Cadiz, Spain
Hospital Universitario de Jerez dela Frontera
Jerez de la Frontera, Spain
Accuracy length AI
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).
Time frame: Date of enrolment, at baseline
Accuracy caregiver length
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.
Time frame: Date of enrolment, at baseline
Accuracy caregiver vs AI length
Accuracy of the caregiver measurements (self preferred methodology in cm) vs Length AI by same parameters as mentioned in the first primary outcome measure.
Time frame: Date of enrolment, at baseline
Accuracy weight AI
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).
Time frame: Date of enrolment, at baseline
Accuracy caregiver weight
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.
Time frame: Date of enrolment, at baseline
Ease of use tool
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.
Time frame: Date of enrolment, at baseline
Acceptability tool
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.
Time frame: Date of enrolment, at baseline
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.