The potential use of deep learning in performing autocorrection of setup errors in patients receiving radiotherapy.

Journal: Radiography (London, England : 1995)
PMID: 39892051

Abstract

INTRODUCTION: Modern radiotherapy practice relies on multiple approaches for verification of patient positioning. All of these techniques require experienced radiotherapists who understand the anatomical landmarks and the limitations of the used verification techniques. We explore the feasibility of using Artificial intelligence in assisted patient positions using acquired port images (PFIs) and digital reconstructed radiographs (DRRs).

Authors

  • A Muhammed
    Clinical Oncology Department, Sohag University Hospital, Egypt. Electronic address: Amr.muhammed@med.sohag.edu.eg.
  • M Hassan
    Clinical Oncology Department, Sohag University Hospital, Egypt.
  • W Soliman
    Clinical Oncology Department, Sohag University Hospital, Egypt.
  • A Ibrahim
    The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands; Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands; Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, Hospital Center Universitaire De Liege, Liege, Belgium; Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), University Hospital RWTH Aachen University, Aachen, Germany. Electronic address: a.ibrahim@maastrichtuniversity.nl.
  • S H Abdelaal
    Clinical Oncology Department, Sohag University Hospital, Egypt.

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