Researchers developed a new method using 3D body shapes to predict body composition, offering a more accessible alternative to traditional medical imaging. This approach can accurately track fat mass changes over time.
By Tarun Sai LomteReviewed by Susha Cheriyedath, M.Sc.Oct 25 2024 A novel 3D body shape method promises accessible and accurate body composition predictions, potentially transforming how we monitor health over time and detect risks.
Conventional anthropometrics, such as waist-hip ratio, body mass index , and waist-hip circumferences, are used to infer body composition. Nevertheless, these methods do not differentiate between lean and fat mass and are inadequately accurate/convenient for longitudinal use, often requiring trained personnel and in-person visits. Thus, simple, accessible, inexpensive tools are needed to assess body composition accurately.
Each three columns shows one participant who lost , maintained or gained fat mass, their DXA scans and fitted meshes for Fenland phase 1 and phase 2 . Changes in body shape for the first and third participants are significant. Findings The smartphone validation study participants were younger, leaner, and lighter than those in the Fenland study. The researchers noted that the optimized meshes agreed with the DXA silhouette much better than the initial shape and pose estimates. In the Phase I sample of the Fenland study, correlation coefficients between DXA and predicted parameters were robust for all lean and fat mass variables. Similarly, correlation coefficients were strong for all variables in the Phase II sample.
Body Mass Index Body Shape Chronic Chronic Disease Computed Tomography Magnetic Resonance Imaging Medicine Tomography X-Ray
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