TY  - JOUR
AU  - Chua, Mei Chien
AU  - Hadimaja, Matthew
AU  - Wong, Jill
AU  - Mukherjee, Sankha Subhra
AU  - Foussat, Agathe
AU  - Chan, Daniel
AU  - Nandal, Umesh
AU  - Yap, Fabian
PY  - 2024
DA  - 2024/11/22
TI  - Exploring the Use of a Length AI Algorithm to Estimate Children’s Length from Smartphone Images in a Real-World Setting: Algorithm Development and Usability Study
JO  - JMIR Pediatr Parent
SP  - e59564
VL  - 7
KW  - computer vision
KW  - length estimation
KW  - artificial intelligence
KW  - smartphone images
KW  - children
KW  - AI
KW  - algorithm
KW  - imaging
KW  - height
KW  - length
KW  - measure
KW  - pediatric
KW  - infant
KW  - neonatal
KW  - newborn
KW  - smartphone
KW  - mHealth
KW  - mobile health
KW  - mobile phone
AB  - 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 
SN  - 2561-6722
UR  - https://pediatrics.jmir.org/2024/1/e59564
UR  - https://doi.org/10.2196/59564
DO  - 10.2196/59564
ID  - info:doi/10.2196/59564
ER  -