Artificial intelligence-driven anthropometric assessment for young children: evaluating the accuracy and practicality of a digital image-based length and weight prediction tool

Authors

Matthew Hadimaja
Sankha Mukherjee (ORCID: https://orcid.org/0000-0001-9094-0538)
Mei Chien Chua (ORCID: https://orcid.org/0000-0002-7471-751X)
Daniel Chan (ORCID: https://orcid.org/0000-0001-8191-5330)
Jill Wong (ORCID: https://orcid.org/0000-0002-7368-8593)
Fabian Yap (ORCID: https://orcid.org/0000-0003-1083-7958)

Institutions

Danone (France) (FR)
KK Women's and Children's Hospital (SG)
Duke-NUS Medical School (SG)

Publication Details

Journal: BMJ Health & Care Informatics
Published: 2025-12-01
DOI: https://doi.org/10.1136/bmjhci-2025-101540
Primary Topic: Body Composition Measurement Techniques
Type: article
Field-Weighted Citation Impact: 0.00

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article

Artificial intelligence-driven anthropometric assessment for young children: evaluating the accuracy and practicality of a digital image-based length and weight prediction tool

Matthew Hadimaja, Sankha Mukherjee, Mei Chien Chua, Daniel Chan et al.

Dec 1, 2025

BMJ Health & Care Informatics

Body Composition Measurement Techniques

article

Artificial intelligence-driven anthropometric assessment for young children: evaluating the accuracy and practicality of a digital image-based length and weight prediction tool

Matthew Hadimaja, Sankha Mukherjee, Mei Chien Chua, Daniel Chan, Jill Wong, Fabian Yap

December 1, 2025article en

Abstract

No abstract available for this paper.

BMJ Health & Care InformaticsVol. 32(1)

Danone (France) (FR), KK Women's and Children's Hospital (SG), Duke-NUS Medical School (SG)

Openalex Percentile: Top 51%

Body Composition Measurement Techniques

DOI Open Access PDF OpenAlex

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