Operational greenhouse-gas emissions of deep learning in digital pathology: a modelling study.

Journal: The Lancet. Digital health
Published Date: Nov 22, 2023

Abstract

BACKGROUND: Deep learning is a promising way to improve health care. Image-processing medical disciplines, such as pathology, are expected to be transformed by deep learning. The first clinically applicable deep-learning diagnostic support tools are already available in cancer pathology, and their number is increasing. However, data on the environmental sustainability of these tools are scarce. We aimed to conduct an environmental-sustainability analysis of a theoretical implementation of deep learning in patient-care pathology.

Authors

  • Alireza Vafaei Sadr
  • Roman Bülow
    Institute of Pathology, University Hospital Aachen, Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany.
  • Saskia von Stillfried
    Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany.
  • Nikolas E J Schmitz
    Institute of Pathology, University Hospital Aachen, Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany.
  • Pourya Pilva
    Institute of Pathology, University Hospital Aachen, Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany.
  • David L Hölscher
    Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany.
  • Peiman Pilehchi Ha
    Healthy Living Spaces Lab, Institute for Occupational, Social and Environmental Medicine, Medical Faculty, Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany.
  • Marcel Schweiker
    Healthy Living Spaces Lab, Institute for Occupational, Social and Environmental Medicine, Medical Faculty, Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany.
  • Peter Boor
    Institute of Pathology, University Hospital Aachen, RWTH Aachen University, Aachen, Germany.

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