Diagnostic validation of a deep learning nodule detection algorithm in low-dose chest CT: determination of optimized dose thresholds in a virtual screening scenario.

Journal: European radiology
Published Date: Jan 21, 2022

Abstract

OBJECTIVES: This study was conducted to evaluate the effect of dose reduction on the performance of a deep learning (DL)-based computer-aided diagnosis (CAD) system regarding pulmonary nodule detection in a virtual screening scenario.

Authors

  • Alan A Peters
    From the Departments of Diagnostic, Interventional, and Pediatric Radiology.
  • Adrian T Huber
    Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Bern University Hospital, University of Bern, Inselspital Bern, 3010, Switzerland.
  • Verena C Obmann
    Department of Diagnostic, Interventional, and Pediatric Radiology, Inselspital Bern, University of Bern, Bern, Switzerland.
  • Johannes T Heverhagen
    From the Departments of Diagnostic, Interventional, and Pediatric Radiology.
  • Andreas Christe
  • Lukas Ebner

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