Improved assessment of donor liver steatosis using Banff consensus recommendations and deep learning algorithms.

Journal: Journal of hepatology
PMID: 38036009

Abstract

BACKGROUND & AIMS: The Banff Liver Working Group recently published consensus recommendations for steatosis assessment in donor liver biopsy, but few studies reported their use and no automated deep-learning algorithms based on the proposed criteria have been developed so far. We evaluated Banff recommendations on a large monocentric series of donor liver needle biopsies by comparing pathologists' scores with those generated by convolutional neural networks (CNNs) we specifically developed for automated steatosis assessment.

Authors

  • Alessandro Gambella
    A.O.U. Città della Salute e della Scienza Hospital, Division of Pathology, Corso Bramante 88, Turin, 10126, Italy.
  • Massimo Salvi
  • Luca Molinaro
    A.O.U. Città della Salute e della Scienza Hospital, Division of Pathology, Corso Bramante 88, Turin, 10126, Italy.
  • Damiano Patrono
    General Surgery 2U, Liver Transplant Center, AOU Città Della Salute e Della Scienza di Torino, University of Turin, Turin, Italy.
  • Paola Cassoni
    Pathology Unit, Department of Medical Sciences, University of Turin, Turin, Italy.
  • Mauro Papotti
    University of Turin, Division of Pathology, Department of Oncology, Via Santena 5, Turin, 10126, Italy.
  • Renato Romagnoli
    General Surgery 2U, Liver Transplant Center, AOU Città Della Salute e Della Scienza di Torino, University of Turin, Turin, Italy.
  • Filippo Molinari
    Department of Electronics and Telecommunications, Politecnico di Torino, Italy.

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