Deep learning helps discriminate between autoimmune hepatitis and primary biliary cholangitis

This study demonstrates the significant potential of the autoimmune liver neural estimator model, a transformer-based deep learning system, in accurately distinguishing between autoimmune hepatitis and primary biliary cholangitis using digitized liver biopsy slides without human annotation. The scientific justification for this work lies in addressing the challenge of differentiating these conditions, which often present with overlapping features and can lead to therapeutic mistakes. In addition, there is need for quantitative assessment of information embedded in liver biopsies, which are currently evaluated on qualitative or semi-quantitative methods. The results of this study are crucial for pathologists, researchers, and clinicians, providing a reliable diagnostic tool that reduces interobserver variability and improves diagnostic accuracy of these conditions. Potential methodological limitations, such as the diversity in scanning techniques and slide colorations, were considered, ensuring the robustness and generalizability of the findings.

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Publication Details

Journal
JHEP Reports
Published
2024-08-31
DOI
https://doi.org/10.1016/j.jhepr.2024.101198
Citations
14
Primary Topic
Liver Diseases and Immunity
Type
article
Field-Weighted Citation Impact
5.17
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article

Deep learning helps discriminate between autoimmune hepatitis and primary biliary cholangitis

Federica Malinverno, Bastian Engel, Marco Muselli, Reha Akpınar et al.
14 citations
JHEP Reports
Liver Diseases and Immunity
5.17
article

Deep learning helps discriminate between autoimmune hepatitis and primary biliary cholangitis

Federica Malinverno, Bastian Engel, Marco Muselli, Reha Akpınar, Daniela Cabibi, Mauro Viganò, Francesca Colapietro, Luca Di Tommaso, Vincenza Calvaruso, Richard Taubert, Nicoló Caldonazzi, Luigi Terracciano, Fabio Gibilisco, Valentina Canini, Vincenzo L’Imperio, Marco Carbone, Francesca Bolis, Cristina Rigamonti, Alessio Gerussi, Giorgio Cazzaniga, Ana Lleò, Damiano Verda, Dina Tiniakos, Laura Cristoferi, Pietro Invernizzi, Julien Caldéraro, Zunamys I. Carrero, Jakob Nikolas Kather, Oliver Lester Saldanha, Stefano Ceola, Nicola Zucchini, Fabio Pagni, Eugenia Nofit, Alessandro Valentino
article en
14 citations

Abstract

This study demonstrates the significant potential of the autoimmune liver neural estimator model, a transformer-based deep learning system, in accurately distinguishing between autoimmune hepatitis and primary biliary cholangitis using digitized liver biopsy slides without human annotation. The scientific justification for this work lies in addressing the challenge of differentiating these conditions, which often present with overlapping features and can lead to therapeutic mistakes. In addition, there is need for quantitative assessment of information embedded in liver biopsies, which are currently evaluated on qualitative or semi-quantitative methods. The results of this study are crucial for pathologists, researchers, and clinicians, providing a reliable diagnostic tool that reduces interobserver variability and improves diagnostic accuracy of these conditions. Potential methodological limitations, such as the diversity in scanning techniques and slide colorations, were considered, ensuring the robustness and generalizability of the findings.

JHEP ReportsVol. 7(2)
University of Verona (IT), Università degli Studi del Piemonte Orientale “Amedeo Avogadro” (IT), Humanitas University (IT), Inserm (FR), Université Paris-Est Créteil (FR), National and Kapodistrian University of Athens (GR), Heidelberg University (DE), University Hospital Heidelberg (DE), Royal University of Law and Economics (KH), Medizinische Hochschule Hannover (DE), University of Catania (IT), Azienda Socio Sanitaria Territoriale Grande Ospedale Metropolitano Niguarda (IT), Assistance Publique – Hôpitaux de Paris (FR), Ospedale Papa Giovanni XXIII (IT), Azienda Ospedaliera San Gerardo (IT), National Center for Tumor Diseases (DE), ERN GUARD-Heart (NL), Istituti di Ricovero e Cura a Carattere Scientifico (IT), Hôpital Albert-Chenevier (FR), Institut Mondor de Recherche Biomédicale (FR), University Hospital Carl Gustav Carus (DE), IRCCS Humanitas Research Hospital (IT), ERN RARE-LIVER (DE), University of Milano-Bicocca (IT), Technische Universität Dresden (DE), Newcastle University (GB), RWTH Aachen University (DE), University of Palermo (IT)
Reduced inequalities
Openalex Percentile: Top 3%
Liver Diseases and Immunity
5.17
DOIOpen Access PDFOpenAlex
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