Integrating machine learning for advanced analysis of bioelectrical impedance parameters in children with nephrotic syndrome
Applying ML-supported evaluation of BIA affirmed diagnostics. However, low specificity limits clinical applications. A larger population of patients and inclusion of additional biomarkers may be needed to develop a more acceptable model.
Authors
- Steven Brantlov (ORCID: https://orcid.org/0000-0001-6324-9215)
- Christian Lodberg Hvas (ORCID: https://orcid.org/0000-0001-7973-7184)
- Leigh C. Ward (ORCID: https://orcid.org/0000-0003-2378-279X)
- Josephine Reinert Quist
- René Frydensbjerg Andersen (ORCID: https://orcid.org/0000-0001-8045-7147)
- Lars Jødal
Institutions
- The University of Queensland (AU)
- Aarhus University (DK)
- Aalborg University Hospital (DK)
- Aarhus University Hospital (DK)
- Aalborg University (DK)
Publication Details
- Journal
- Frontiers in Pediatrics
- Published
- 2026-02-16
- DOI
- https://doi.org/10.3389/fped.2026.1714324
- Primary Topic
- Body Composition Measurement Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00