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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| Format: | Article |
| Language: | English |
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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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| author | Tathiany J. Ferreira Igor C. Salvador Carolina R. Pessanha Renata R. M. da Silva Aline D. Pereira Maria A. Horst Denise P. Carvalho Josely C. Koury Anna P. T. R. Pierucci |
| author_facet | Tathiany J. Ferreira Igor C. Salvador Carolina R. Pessanha Renata R. M. da Silva Aline D. Pereira Maria A. Horst Denise P. Carvalho Josely C. Koury Anna P. T. R. Pierucci |
| author_sort | Tathiany J. Ferreira |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-94a1d7d44a5e413391abf4808e42c7c9 |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-94a1d7d44a5e413391abf4808e42c7c92025-01-19T12:39:47ZengNature Portfolionpj Digital Medicine2398-63522025-01-01811910.1038/s41746-024-01380-6Advances in the estimation of body fat percentage using an artificial intelligence 2D-photo methodTathiany J. Ferreira0Igor C. Salvador1Carolina R. Pessanha2Renata R. M. da Silva3Aline D. Pereira4Maria A. Horst5Denise P. Carvalho6Josely C. Koury7Anna P. T. R. Pierucci8Josué de Castro Institute of Nutrition, Federal University of Rio de JaneiroJosué de Castro Institute of Nutrition, Federal University of Rio de JaneiroJosué de Castro Institute of Nutrition, Federal University of Rio de JaneiroJosué de Castro Institute of Nutrition, Federal University of Rio de JaneiroInstitute of Geography, State University of Rio de JaneiroFaculty of Nutrition, Federal University of GoiasCarlos Chagas Filho Institute of Biophysics, Federal University of Rio de JaneiroInstitute of Nutrition, State University of Rio de JaneiroJosué de Castro Institute of Nutrition, Federal University of Rio de JaneiroAbstract 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.https://doi.org/10.1038/s41746-024-01380-6 |
| spellingShingle | Tathiany J. Ferreira Igor C. Salvador Carolina R. Pessanha Renata R. M. da Silva Aline D. Pereira Maria A. Horst Denise P. Carvalho Josely C. Koury Anna P. T. R. Pierucci Advances in the estimation of body fat percentage using an artificial intelligence 2D-photo method npj Digital Medicine |
| title | Advances in the estimation of body fat percentage using an artificial intelligence 2D-photo method |
| title_full | Advances in the estimation of body fat percentage using an artificial intelligence 2D-photo method |
| title_fullStr | Advances in the estimation of body fat percentage using an artificial intelligence 2D-photo method |
| title_full_unstemmed | Advances in the estimation of body fat percentage using an artificial intelligence 2D-photo method |
| title_short | Advances in the estimation of body fat percentage using an artificial intelligence 2D-photo method |
| title_sort | advances in the estimation of body fat percentage using an artificial intelligence 2d photo method |
| url | https://doi.org/10.1038/s41746-024-01380-6 |
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