Higher effect sizes for the detection of accelerated brain volume loss and disability progression in multiple sclerosis using deep-learning.

Journal: Computers in biology and medicine
PMID: 39423705

Abstract

PURPOSE: Clinical validation of "BrainLossNet", a deep learning-based method for fast and robust estimation of brain volume loss (BVL) from longitudinal T1-weighted MRI, for the detection of accelerated BVL in multiple sclerosis (MS) and for the discrimination between MS patients with versus without disability progression.

Authors

  • Roland Opfer
    jung diagnostics GmbH, Hamburg, Germany.
  • Tjalf Ziemssen
    Technische Universität, Dresden, Germany.
  • Julia Krüger
    Jung diagnostics, Hamburg, Germany.
  • Thomas Buddenkotte
    Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom; Department of Radiology, University of Cambridge, Cambridge, United Kingdom; Department for Diagnostic and Interventional Radiology and Nuclear Medicine, University Hospital Hamburg-Eppendorf, Hamburg, Germany; Jung diagnostics GmbH, Hamburg, Germany. Electronic address: t.buddenkotte@uke.de.
  • Lothar Spies
    jung diagnostics GmbH, Hamburg, Germany.
  • Carola Gocke
    Conradia Medical Prevention Hamburg, Hamburg, Germany.
  • Matthias Schwab
    Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, and University of Tuebingen, Tuebingen, Germany.
  • Ralph Buchert
    Department for Diagnostic and Interventional Radiology and Nuclear Medicine, University Hospital Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany. r.buchert@uke.de.

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