Deutsche Nationalbibliothek
metadata only
Diagnostic performance of AI-powered prostate MRI against biopsy ground truth: quasi-continuous risk scoring versus fixed PI-RADS thresholds / Nadine Bayerl, Joel Appel, Maximilian Schmidt, Alexander Cavallaro, Robert Grimm, Heinrich von Busch, Bernd Wullich, Arndt Hartmann, Michael Schlicht, Michael Uder, Matthias S. May, Stephan Ellmann
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Current status and future directions of AI in prostate cancer detection on MRI: a special report from the ESUR prostate MRI working group authors#1002133
- Integrating Artificial Intelligence into Prostate MR Imaging: Technical Foundations, Clinical Applications, and Workflow Implications.#645363
- High Resolution MRI Study for Prostate Cancer#1139014
- Diagnostic assessment of artificial intelligence reconstruction on accelerated prostate MRI: a retrospective, paired, multi-reader multi-case study#348964
- Improving Prostate Lesion Classification and Development of a PI-RADS 3 Classifier#851553
Record · ID 983115
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive