Sarcopenia, Obesity, and Sarcopenic Obesity: Retrospective Audit of Electronic Health Record Documentation versus Automated CT Analysis in 17 646 Patients
Automated opportunistic CT analysis identified sarcopenia and sarcopenic obesity in 28.5% and 5.7% of 17 646 patients, respectively, contrasting with the 0.05% underreported in the electronic health record, which highlights the potential of artificial intelligence tools to mitigate sarcopenia underdiagnosis.
Authors
- Juan Manuel Zambrano Chaves (ORCID: https://orcid.org/0000-0002-7274-8072)
- Robert Downey Boutin (ORCID: https://orcid.org/0000-0001-7887-9658)
- Leon Lenchik (ORCID: https://orcid.org/0000-0001-6450-2470)
- Jason Hom (ORCID: https://orcid.org/0000-0002-8404-5796)
- Akshay Sanjay Chaudhari (ORCID: https://orcid.org/0000-0002-3667-6796)
Institutions
- Wake Forest University (US)
- Stanford University (US)
Publication Details
- Journal
- Radiology
- Published
- 2025-04-01
- DOI
- https://doi.org/10.1148/radiol.243525
- Citations
- 4
- Primary Topic
- Nutrition and Health in Aging
- Type
- article
- Field-Weighted Citation Impact
- 4.66
Funders
- National Institutes of Health
- National Institute on Aging