Diagnostic performance of a fully automated AI algorithm for lesion detection and PI-RADS classification in patients with suspected prostate cancer
The AI algorithm proved to be a reliable and robust tool for lesion detection and classification. Its cancer detection rates and PI-RADS category distribution align with the results of recent meta-analyses, indicating precise risk stratification.
Authors
- Jakob Weiß
- Heinrich von Busch (ORCID: https://orcid.org/0000-0003-0469-1622)
- August Sigle (ORCID: https://orcid.org/0000-0001-6857-8149)
- Matthias Benndorf (ORCID: https://orcid.org/0000-0002-0407-1266)
- Benedict Oerther (ORCID: https://orcid.org/0000-0003-4267-3601)
- Robert Grimm (ORCID: https://orcid.org/0000-0002-1233-8215)
- Andrea Nedelcu (ORCID: https://orcid.org/0009-0000-6812-5334)
- Hannes Engel (ORCID: https://orcid.org/0000-0003-1985-325X)
- Tobias Krauß (ORCID: https://orcid.org/0000-0002-5745-2054)
- Christopher L. Schlett (ORCID: https://orcid.org/0000-0002-1576-1481)
Institutions
- University of Freiburg (DE)
- University Medical Center Freiburg (DE)
- Klinikum Lippe (DE)
- Klinikum Bielefeld (DE)
- Siemens Healthineers (Germany) (DE)
Publication Details
- Journal
- La radiologia medica
- Published
- 2025-04-16
- DOI
- https://doi.org/10.1007/s11547-025-02003-0
- Citations
- 2
- Primary Topic
- Prostate Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 3.04