An artificial intelligence-driven scoring system to measure histological disease activity in ulcerative colitis.

Journal: United European gastroenterology journal

PMID: 38590110

Abstract

BACKGROUND AND AIMS: Assessment and scoring of histological images in Ulcerative colitis (UC) is prone to inter- and intra-observer variability. This study aimed to investigate whether an artificial intelligence (AI) system developed using image processing and machine learning algorithms could measure histological disease activity based on the Nancy index.

Authors

Laurent Peyrin-Biroulet

Institut National de la Santé et de la Recherche Médicale U954 and Department of Gastroenterology, Nancy University Hospital, University of Lorraine, France. Electronic address: peyrinbiroulet@gmail.com.
Shashi Adsul

Takeda Pharmaceutical Company, Zurich, Switzerland.
Andrea Stancati

Takeda Pharmaceutical Company, Zurich, Switzerland.
Jamshid Dehmeshki

Image Analysis Group, Philadelphia, PA, USA; Kingston University, Kingston-upon-Thames, UK.
Olga Kubassova

Image Analysis Group, London, United Kingdom.

Keywords

Algorithms Artificial Intelligence Colitis, Ulcerative Colon Deep Learning Female Humans Image Interpretation, Computer-Assisted Image Processing, Computer-Assisted Machine Learning Male Observer Variation Reproducibility of Results Severity of Illness Index

External Resources

View on PubMed Access via DOI PubMed (38590110)

An artificial intelligence-driven scoring system to measure histological disease activity in ulcerative colitis.

Abstract

Authors

Keywords

External Resources

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