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Exploring the Use of a Length AI Algorithm to Estimate Children's Length from Smartphone Images in a Real-World
Mei Chien Chua1,2,3,4, Matthew Hadimaja5, Jill Wong5
1Department of Neonatology, KK Women's and Children's Hospital, Singapore, Singapore.
Insights
A new AI tool uses smartphone images to measure children's length, offering a convenient alternative to traditional methods. This innovation shows promise for accurate growth monitoring at home and in clinics.
Area of Science:
- Pediatric growth monitoring
- Artificial Intelligence in healthcare
- Medical imaging analysis
Background:
- Accurate length measurement is crucial for monitoring growth in children under 18 months.
- Traditional length measurement is challenging due to equipment, training, and child cooperation requirements.
Purpose of the Study:
- To develop a Length Artificial Intelligence (LAI) algorithm for convenient recumbent length estimation from smartphone images.
- To evaluate the LAI algorithm's performance and suitability for personal and clinical use.
Main Methods:
- A proof-of-concept study involving healthy children aged 0-18 months.
- Comparison of LAI algorithm's image-based length estimations against standardized length-board measurements.
- Evaluation of prediction performance on individual images and participant-averaged bases, alongside user experience questionnaires.
Main Results:
- The LAI algorithm achieved a high prediction success rate (99.4%) across analyzed photos.
- Mean absolute errors were 2.47 cm for individual images and 1.77 cm for participant-averaged predictions.
- High user satisfaction reported by parents and investigators regarding photo capture ease.
Conclusions:
- The LAI algorithm provides an accessible method for estimating children's length using smartphone images, eliminating the need for specialized equipment or personnel.
- The algorithm's performance and ease of use suggest potential for parental/caregiver use and clinical applications, approaching general clinic accuracy.
- The study serves as a proof of concept for the LAI algorithm's utility in personal and clinical settings.
Background:
Length measurement in young children younger than 18 months is important for monitoring growth and development. Accurate length measurement requires proper equipment, standardized methods, and trained personnel. In addition, length measurement requires young children's cooperation, making it particularly challenging during infancy and toddlerhood.
Objective:
This study aimed to develop a length artificial intelligence (LAI) algorithm to aid users in determining recumbent length conveniently from smartphone images and explore its performance and suitability for personal and clinical use.
Methods:
This proof-of-concept study in healthy children (aged 0-18 months) was performed at KK Women's and Children's Hospital, Singapore, from November 2021 to March 2022. Smartphone images were taken by parents and investigators. Standardized length-board measurements were taken by trained investigators. Performance was evaluated by comparing the tool's image-based length estimations with length-board measurements (bias [mean error, mean difference between measured and predicted length]; absolute error [magnitude of error]). Prediction performance was evaluated on an individual-image basis and participant-averaged basis. User experience was collected through questionnaires.
Results:
A total of 215 participants (median age 4.4, IQR 1.9-9.7 months) were included. The tool produced a length prediction for 99.4% (2211/2224) of photos analyzed. The mean absolute error was 2.47 cm for individual image predictions and 1.77 cm for participant-averaged predictions. Investigators and parents reported no difficulties in capturing the required photos for most participants (182/215, 84.7% participants and 144/200, 72% participants, respectively).
Conclusions:
The LAI algorithm is an accessible and novel way of estimating children's length from smartphone images without the need for specialized equipment or trained personnel. The LAI algorithm's current performance and ease of use suggest its potential for use by parents or caregivers with an accuracy approaching what is typically achieved in general clinics or community health settings. The results show that the algorithm is acceptable for use in a personal setting, serving as a proof of concept for use in clinical settings.
Trial Registration:
ClinicalTrials.gov NCT05079776; https://clinicaltrials.gov/ct2/show/NCT05079776.
