ArticleSN computer science2026
Voxel-based Deep Regression for Enhanced Body Composition Estimation from 3D Body Scans.
Article in SN computer science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Predicting anthropometric body composition variables using 3D optical imaging and machine learning.Frontiers in bioinformatics · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate estimation of body composition remains a critical task in personalized healthcare and clinical decision-making. However, existing measurement methods, such as Dual-Energy X-ray Absorptiometry (DXA) and Computed Tomography (CT), are often difficult to access and pose potential risks associated with ionizing exposure. In this study, we present a streamlined pipeline that replaces handcrafted feature engineering with end-to-end learning from voxel maps and demographic features. Our extended experiments demonstrate promising RMSE performance on real-world 3D scan datasets across multiple regional and total body composition values, validating the effectiveness of this voxel-demographic approach over traditional part-based feature descriptors. The proposed methodology represents a paradigm shift from manual feature extraction to automated pattern learning, demonstrating significant improvements in both accuracy and scalability for clinical body composition analysis.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.