Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response predictionKey points
David Nam,
Julius Chapiro,
Valerie Paradis,
Tobias Paul Seraphin,
Jakob Nikolas Kather
Affiliations
David Nam
Section of Interventional Radiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
Julius Chapiro
Section of Interventional Radiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
Valerie Paradis
INSERM U1149 ''Centre de Recherche Sur L'inflammation'', CRI, Université de Paris, Paris, France; University Paris, AP-HP, Department of Pathology, Hôpital Beaujon, Clichy, France
Tobias Paul Seraphin
Department of Gastroenterology, Hepatology and Infectious Diseases, Medical Faculty of Heinrich Heine University Düsseldorf, University Hospital Düsseldorf, Düsseldorf, Germany; Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
Jakob Nikolas Kather
Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany; Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK; Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany; Corresponding author. Address: Department of Medicine III, RWTH University Hospital, 52074 Aachen, Germany.
Summary: Clinical routine in hepatology involves the diagnosis and treatment of a wide spectrum of metabolic, infectious, autoimmune and neoplastic diseases. Clinicians integrate qualitative and quantitative information from multiple data sources to make a diagnosis, prognosticate the disease course, and recommend a treatment. In the last 5 years, advances in artificial intelligence (AI), particularly in deep learning, have made it possible to extract clinically relevant information from complex and diverse clinical datasets. In particular, histopathology and radiology image data contain diagnostic, prognostic and predictive information which AI can extract. Ultimately, such AI systems could be implemented in clinical routine as decision support tools. However, in the context of hepatology, this requires further large-scale clinical validation and regulatory approval. Herein, we summarise the state of the art in AI in hepatology with a particular focus on histopathology and radiology data. We present a roadmap for the further development of novel biomarkers in hepatology and outline critical obstacles which need to be overcome.
