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Related Experiment Video

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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Interactive Explainable Deep Learning Model Informs Prostate Cancer Diagnosis at MRI.

Charlie A Hamm1, Georg L Baumgärtner1, Felix Biessmann1

  • 1From the Department of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Campus Virchow Klinikum, Augustenburgerplatz 1, 13353 Berlin, Germany (C.A.H., G.L.B., N.L.B., A.H., L.J.S., K.F., F.D., M.R., A.D.J.B., B.H., M.H., S.H., T.P.); Berlin Institute of Health (BIH), Berlin, Germany (C.A.H., N.L.B., L.J.S., T.P.); Faculty VI-Informatics and Media, Berliner Hochschule für Technik (BHT), Einstein Center Digital Future, Berlin, Germany (G.L.B., F.B.); Bayer AG, Medical Affairs and Pharmacovigilance, Integrated Evidence Generation & Business Innovation, Berlin, Germany (A.H.); Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany (S.S.); and Department of Urology, Otto-von-Guericke-University Magdeburg, Germany and PROURO, Berlin, Germany (H.C.).

Radiology
|April 11, 2023
PubMed
Summary
This summary is machine-generated.

An explainable AI model accurately detects prostate cancer (PCa) using MRI, providing transparent justifications. This AI improves nonexpert confidence and reduces reading time for PI-RADS 3 lesions.

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Oncology

Background:

  • Accurate diagnosis of clinically significant prostate cancer (PCa) via MRI is crucial for effective treatment.
  • Current AI models for PCa detection lack transparency, hindering clinical adoption.
  • Explainable AI (XAI) offers a path to transparent and reliable diagnostic tools.

Purpose of the Study:

  • To develop an explainable AI (XAI) model for diagnosing clinically significant PCa using biparametric MRI.
  • To utilize Prostate Imaging Reporting and Data System (PI-RADS) features for AI classification justification.
  • To enhance the clinical translation of AI in radiology through transparency.

Main Methods:

  • Retrospective analysis of biparametric MRI and histopathology data from 1224 patients.
  • Training a deep learning model for lesion detection, PCa classification (Gleason score ≥ 7), and PI-RADS feature-based explanation.
  • Performance evaluation using cross-validation, ROC curves, and external datasets (PROSTATEx).

Main Results:

  • The XAI model achieved high performance in detecting clinically significant PCa (AUC 0.89 internal, 0.87 external) with 93% sensitivity.
  • XAI provided accurate visual and textual explanations (80% accuracy), confirmed by experts.
  • XAI-assisted readings enhanced nonexpert confidence in PI-RADS 3 lesions and reduced reading time by 58 seconds.

Conclusions:

  • The developed XAI model reliably detects and classifies clinically significant prostate cancer.
  • XAI enhances diagnostic transparency by using established PI-RADS features for justification.
  • This technology improves radiologist confidence and efficiency, facilitating clinical integration.