Evidence map›Paper›PMID 41094156›Full record

ArticleScientific reports2025

Automated body weight measurement using computed tomography.

Sebastian Schenkl, Holger Muggenthaler, Rahel Koch, Andreas Heinrich

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Sebastian Schenkl *Institute of Forensic Medicine, Jena University Hospital - Friedrich Schiller University, Am Klinikum 1, 07747, Jena, Germany.
Holger Muggenthaler *Institute of Forensic Medicine, Jena University Hospital - Friedrich Schiller University, Am Klinikum 1, 07747, Jena, Germany.
Rahel KochDepartment of Radiology, Jena University Hospital - Friedrich Schiller University, Am Klinikum 1, 07747, Jena, Germany.
Andreas HeinrichDepartment of Radiology, Jena University Hospital - Friedrich Schiller University, Am Klinikum 1, 07747, Jena, Germany. andreas.heinrich@med.uni-jena.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Computed tomography (CT) images provide detailed information on tissue composition and density. Body weight estimation from CT can be highly relevant in emergency and forensic contexts where conventional weighing is impractical or unavailable-for example, due to unconscious or immobilized patients, lack of appropriate equipment, or the necessity to preserve forensic trace evidence. An automated method for body weight estimation using antemortem and postmortem CT data from clinical scanners was evaluated. Two retrospective cohorts were included: 30 forensic postmortem whole-body CT scans and 66 clinical torso CT scans. Tissue segmentation applied predefined Hounsfield Unit thresholds for fat, muscle, and bone, with voxel volumes multiplied by corresponding tissue densities to calculate weight. In forensic cases, CT-derived weights differed by - 1.26 ± 2.58 kg (median: - 1.15 kg) from measured weights. In clinical cases, torso weight was extrapolated to total body weight using a corrected scaling factor that accounted for body and scan length, resulting in an average difference of 0.53 ± 4.62 kg (median: 0.23 kg). Linear regression demonstrated strong correlations (R

Indexed as

Body WeightTomography, X-Ray ComputedAdultAgedAged, 80 and overFemaleHumansMaleMiddle AgedRetrospective StudiesWhole Body ImagingYoung AdultBody compositionBody weights and measuresComputed tomographyComputer-assisted image processingForensic medicine

Identifiers

PMID41094156
PMCPMC12528416

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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.