Assessment of a fully-automated diagnostic AI software in prostate MRI: Clinical evaluation and histopathological correlation.

Journal: European journal of radiology
PMID: 39520837

Abstract

OBJECTIVE: This study aims to evaluate the diagnostic performance of a commercial, fully-automated, artificial intelligence (AI) driven software tool in identifying and grading prostate lesions in prostate MRI, using histopathological findings as the reference standard, while contextualizing its performance within the framework of PI-RADS v2.1 criteria.

Authors

  • Nadine Bayerl
    Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute of Radiology, University Hospital Erlangen, Maximiliansplatz 3, 91054 Erlangen, Germany. Electronic address: nadine.bayerl@fau.de.
  • Lisa C Adams
    School of Medicine and Health, Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, TUM University Hospital, Technical University of Munich, Munich, Germany.
  • Alexander Cavallaro
    Department of Radiology, University Hospital Erlangen, Maximiliansplatz 1, 91054, Erlangen, Germany. alexander.cavallaro@uk-erlangen.de.
  • Tobias Bäuerle
    Department of Radiology, Universitätsklinikum Erlangen, 91054 Erlangen, Germany. Electronic address: tobias.baeuerle@uk-erlangen.de.
  • Michael Schlicht
    Sozialstiftung Bamberg, Clinic of Internal Medicine III, Hanst-Schütz Str. 3, 96050 Bamberg, Germany.
  • Bernd Wullich
    Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.
  • Arndt Hartmann
    Institute of Pathology, University Hospital of Friedrich-Alexander-University Erlangen-Nürnberg, Germany.
  • Michael Uder
    Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
  • Stephan Ellmann
    Department of Radiology, Universitätsklinikum Erlangen, 91054 Erlangen, Germany. Electronic address: stephan.ellmann@uk-erlangen.de.

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