Automated assessment of brain MRIs in multiple sclerosis patients significantly reduces reading time.

Journal: Neuroradiology
PMID: 39514032

Abstract

INTRODUCTION: Assessment of multiple sclerosis (MS) lesions on magnetic resonance imaging (MRI) is tedious, time-consuming, and error-prone. We evaluate whether assessment of new, expanding, and contrast-enhancing MS lesions can be done more time-efficiently by radiologists with assistance of artificial intelligence (AI).

Authors

  • Victoria Sieber
    Department of Neuroradiology, University Hospital Basel, Basel, Switzerland.
  • Thilo Rusche
    Department of Clinical Radiology, Neuroradiology, University Hospital Muenster, Albert-Schweitzer-Campus 1, Gebäude A1, 48149, Muenster, Germany.
  • Shan Yang
    Clinic of Radiology and Nuclear Medicine, University Hospital Basel, Petersgraben 4, 4031, Basel, Switzerland.
  • Bram Stieltjes
    University of Basel, University Hospital Basel, Radiology and Nuclear Medicine Clinic, Basel, Switzerland.
  • Urs Fischer
    Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland.
  • Stefano Trebeschi
    Department of Radiology, the Netherlands Cancer Institute, Amsterdam, The Netherlands.
  • Philippe Cattin
    Center for Medical Image Analysis and Navigation, University of Basel, Gewerbestrasse 14, 4123, Allschwil, Switzerland.
  • Dan Linh Nguyen-Kim
    Department of Radiology, Neuroradiology and Nuclear Medicine, Stadtspital Zürich, Zürich, Switzerland.
  • Marios-Nikos Psychogios
    Department of Neuroradiology, Clinic for Radiology & Nuclear Medicine, University Hospital Basel, Basel, Switzerland. mn.psyc@medmail.ch.
  • Johanna M Lieb
    Department of Neuroradiology, University Hospital Basel, Basel, Switzerland.
  • Peter B Sporns
    Department of Neuroradiology, University Hospital Basel, Basel, Switzerland. peter.sporns@hotmail.de.

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