Artificial Intelligence-Based Opportunities in Liver Pathology-A Systematic Review
2023
Allaume, Pierre | Rabilloud, Noémie | Turlin, Bruno | Bardou-Jacquet, Edouard | Loréal, Olivier | Calderaro, Julien | Khene, Zine-Eddine | Acosta, Oscar | de Crevoisier, Renaud | Rioux-Leclercq, Nathalie | Pécot, Thierry | Kammerer-Jacquet, Solène-Florence | CHU Pontchaillou [Rennes] | Laboratoire Traitement du Signal et de l'Image (LTSI) ; Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM) | Nutrition, Métabolismes et Cancer (NuMeCan) ; Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | Institut Mondor de Recherche Biomédicale (IMRB) ; Institut National de la Santé et de la Recherche Médicale (INSERM)-IFR10-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12) | CRLCC Eugène Marquis (CRLCC) | Institut de recherche en santé, environnement et travail (Irset) ; Université d'Angers (UA)-Université de Rennes (UR)-École des Hautes Études en Santé Publique [EHESP] (EHESP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ) | Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ) | None
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Show more [+] Less [-]English. BACKGROUND: Artificial Intelligence (AI)-based Deep Neural Networks (DNNs) can handle a wide range of applications in image analysis, ranging from automated segmentation to diagnostic and prediction. As such, they have revolutionized healthcare, including in the liver pathology field. OBJECTIVE: The present study aims to provide a systematic review of applications and performances provided by DNN algorithms in liver pathology throughout the Pubmed and Embase databases up to December 2022, for tumoral, metabolic and inflammatory fields. RESULTS: 42 articles were selected and fully reviewed. Each article was evaluated through the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool, highlighting their risks of bias. CONCLUSIONS: DNN-based models are well represented in the field of liver pathology, and their applications are diverse. Most studies, however, presented at least one domain with a high risk of bias according to the QUADAS-2 tool. Hence, DNN models in liver pathology present future opportunities and persistent limitations. To our knowledge, this review is the first one solely focused on DNN-based applications in liver pathology, and to evaluate their bias through the lens of the QUADAS2 tool.
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