New advances in body composition assessment with ShapedNet: A single image deep regression approach
Year: 2024
Type: article
Abstract: We introduce a novel technique called ShapedNet to enhance body composition assessment. This method employs a deep neural network capable of estimating Body Fat Percentage (BFP), performing individual identification, and enabling localization using a single photograph. The accuracy of ShapedNet is validated through comprehensive comparisons against the gold standard method, Dual-Energy X-ray Absorptiometry (DXA), utilizing 1273 healthy adults spanning various ages, sexes, and BFP levels. The results demonstrate that ShapedNet outperforms in 19.5% state of the art computer vision-based approaches for body fat estimation, achieving a Mean Absolute Percentage Error (MAPE) of 4.91% and Mean Absolute Error (MAE) of 1.42. The study evaluates both gender-based and Gender-neutral approaches, with the latter showcasing superior performance. The method estimates BFP with 95% confidence within an error margin of 4.01% to 5.81%. This research advances multi-task learning and body composition assessment theory through ShapedNet.
Source: Pattern Recognition Letters
Authors: Navar Medeiros M. Nascimento, Pedro Cavalcante de Sousa, Pedro Yuri Rodrigues Nunes, Suane Pires Pinheiro da Silva, Luiz Lannes Loureiro, Victor Zaban Bittencourt, Valden Luis Matos Capistrano, Pedro Pedrosa Rebouças Filho
Language: English
Subfield: Physiology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $2,610
OpenAlex ID: W4405130883