ArticleJMIR pediatrics and parenting2024
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.
Article in JMIR pediatrics and parenting, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05079776 (Assessing the Use of a Growth Artificial Intelligence Algorithm for Estimating the leNgth of Children in Real-world Setting), which is not on this map. Cited by 3 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Assessing the Use of a Growth Artificial Intelligence Algorithm for Estimating the leNgth of Children in Real-world Setting
Who cites it
3 citing papers in PubMed.
- [Use of artificial intelligence for next-gen anamnesis and communication in orthopedics & trauma surgery : From chatbots to ambient intelligence].Orthopadie (Heidelberg, Germany) · 2026Review
- Artificial intelligence-driven anthropometric assessment for young children: evaluating the accuracy and practicality of a digital image-based length and weight prediction tool.BMJ health & care informatics · 2025Observational
- Inferring Body Measurements from 2D Images: A Comprehensive Review.Journal of imaging · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLength 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.
objectiveThis 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.
methodsThis 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.
resultsA 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).
conclusionsThe 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 registrationClinicalTrials.gov NCT05079776; https://clinicaltrials.gov/ct2/show/NCT05079776.
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