Evidence map›Paper›PMID 40100280›Full record

ArticleAbdominal radiology (New York)2025

AI-enabled body composition biomarkers at post-mortem CT for enriching autopsy: analysis of a large decedent cohort.

Max V Golden, Matthew H Lee, John W Garrett, Shamsi Daneshvari Berry, Nicollette Appel, Ronald M Summers, Heather J H Edgar, Perry J Pickhardt

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Article in Abdominal radiology (New York), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Max V GoldenDepartment of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave., Madison, Wisconsin, 53792, USA. mgolden3@wisc.edu.
Matthew H LeeDepartment of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave., Madison, Wisconsin, 53792, USA.
John W GarrettDepartment of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave., Madison, Wisconsin, 53792, USA.
Shamsi Daneshvari BerryDepartment of Biomedical Informatics, Western Michigan University, 1000 Oakland Dr., Kalamazoo, Michigan, 49008, USA.
Nicollette AppelDepartment of Anthropology, University of New Mexico, 1 University of New Mexico, Albuquerque, New Mexico, 87131, USA.
Ronald M SummersImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Maryland, Bethesda, USA.
Heather J H EdgarDepartment of Anthropology, University of New Mexico, 1 University of New Mexico, Albuquerque, New Mexico, 87131, USA.
Perry J PickhardtDepartment of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave., Madison, Wisconsin, 53792, USA. ppickhardt2@uwhealth.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo correlate fully-automated PMCT-based body composition measures with causes of death and comorbidities. MATERIALS AND

methodsRetrospective study of New Mexico Decedent Image Database (NMDID) with non-contrast PMCT scans between 2010 and 2017. Automated pipeline of AI-driven algorithms for quantifying skeletal muscle, subcutaneous/visceral fat, and aortic calcification from the abdominal component of PMCT scans was used. Scans with more than minimal decomposition were excluded. Cause of death was categorized as "acute" or "chronic." A predetermined model derived CT-based "biological age."

results6638 decedents (mean age, 50±18 [SD]; 74% male) comprised the final cohort. 80% of deaths were classified as "acute," 10% as "chronic," and 10% "uncertain." Muscle density (HU) and area at the L3 lumbar level were higher in the "acute" versus "chronic" group (26 HU vs. 18 HU, p < 0.001; 192 cm

conclusionFully-automated quantitative PMCT-based tissue biomarkers correlate with the temporal nature of death and chronic co-morbidities, supporting their use for enhancing autopsies.

Indexed as

Artificial IntelligenceBody CompositionTomography, X-Ray ComputedAdultAgedAutopsyBiomarkersCause of DeathFemaleHumansMaleMiddle AgedNew MexicoPostmortem ImagingRetrospective StudiesBiomarkersArtificial intelligenceDeep learningMachine learningPost-Mortem CT

Identifiers

PMID40100280

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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.