New advances in body composition assessment with ShapedNet: A single image deep regression approach

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. • Accurate body fat estimation: Innovative deep learning for precise body fat estimation from photos. • Validation with gold standard: Validate using DXA reference. • Expanding multi-task learning: Identifying, localizing, and regressing. Beneficial across computer vision, medical imaging, and biometrics. • Novel Method Development: Innovative CNN-based method for body fat estimation, identification, and visualizations.