Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction

Publikation: Beitrag in FachzeitschriftÜbersichtsartikel (Review)BeigetragenBegutachtung

Beitragende

  • David Nam - , Yale University (Autor:in)
  • Julius Chapiro - , Yale University (Autor:in)
  • Valerie Paradis - , Université Paris Cité, Hopital Beaujon (Autor:in)
  • Tobias Paul Seraphin - , Universitätsklinikum Düsseldorf, Rheinisch-Westfälische Technische Hochschule Aachen (Autor:in)
  • Jakob Nikolas Kather - , Rheinisch-Westfälische Technische Hochschule Aachen, University of Leeds, Universität Heidelberg (Autor:in)

Abstract

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.

Details

OriginalspracheEnglisch
Aufsatznummer100443
FachzeitschriftJHEP Reports
Jahrgang4
Ausgabenummer4
PublikationsstatusVeröffentlicht - Apr. 2022
Peer-Review-StatusJa
Extern publiziertJa

Schlagworte

Schlagwörter

  • Artificial intelligence, deep learning, diagnostic support system, imaging, machine learning, multimodal data integration

Bibliotheksschlagworte