Advances in the estimation of body fat percentage using an artificial intelligence 2D-photo method
Abstract There is a growing need to evaluate the agreement between the field methods and integrate artificial intelligence (AI) using two-dimensional (2D) photos for enhanced real-world analysis. This study evaluated the agreement between AI-2D photos and the clinical reference method, dual-energy X...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-024-01380-6 |
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| Summary: | Abstract There is a growing need to evaluate the agreement between the field methods and integrate artificial intelligence (AI) using two-dimensional (2D) photos for enhanced real-world analysis. This study evaluated the agreement between AI-2D photos and the clinical reference method, dual-energy X-ray absorptiometry (DXA) to estimate the body fat percentage (BFP). Other methods were also investigated, including skinfolds, A-mode ultrasound, and bioelectrical impedance analysis (BIA). This cross-sectional study was conducted on 1273 adults of both sexes. The Bland–Altman plots, Lin’s Correlation Coefficient of Agreement (CCC), and error analyses were calculated. AI-2D photos demonstrated substantial agreement with DXA presenting the highest agreement (CCC ≥ 0.96) among all the investigated methods. InBody-270 and Omron HBF-514 BIA devices showed moderate agreement (CCC = 0.90 to 0.95) for all participants, age groups >30 years, and body mass index >25 kg/m2. AI-2D photos can be interchangeable with DXA, providing a practical, accessible alternative and an easy-to-use system for BFP estimation. |
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| ISSN: | 2398-6352 |