3D deep learning-based muscle volume quantification from thoracic CT as a surrogate for DXA-Derived appendicular muscle mass in older adults
The results show that the automated analysis of CT thoracic scans is a useful method for determining muscle volume and agrees well with the DXA analysis. Furthermore, the predictive value of CT muscle volume is significantly enhanced in combination with anthropometric parameters, particularly body weight. Further prospective studies are required to validate the findings and refine CT-based sarcopenia diagnostics.
Authors
- Michael Ingrisch (ORCID: https://orcid.org/0000-0003-0268-9078)
- Olivia Tausendfreund
- Ralf Schmidmaier (ORCID: https://orcid.org/0000-0003-3541-3588)
- Sabine Schluessel (ORCID: https://orcid.org/0000-0002-2485-8859)
- Sophia Stoecklein (ORCID: https://orcid.org/0000-0003-0325-4674)
- Beat Müeller (ORCID: https://orcid.org/0000-0002-1986-2511)
- Michael Drey (ORCID: https://orcid.org/0000-0001-6709-9672)
- Michaela Rippl (ORCID: https://orcid.org/0009-0000-1583-1577)
- Linda Deißler
- Sebastian Martini (ORCID: https://orcid.org/0000-0003-3663-9239)
Institutions
- Zuse Institute Berlin (DE)
- LMU Klinikum (DE)
- Munich Center for Machine Learning
- Ludwig-Maximilians-Universität München (DE)
Publication Details
- Journal
- Aging Clinical and Experimental Research
- Published
- 2025-10-21
- DOI
- https://doi.org/10.1007/s40520-025-03206-1
- Citations
- 2
- Primary Topic
- Nutrition and Health in Aging
- Type
- article
- Field-Weighted Citation Impact
- 2.33