English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Background Histological remission is increasingly regarded as an important and deep therapeutic target for ulcerative colitis (UC). Assessment and scoring of histological images is a tedious procedure, that can be imprecise and prone to inter- and intra-observer variability. Therefore, a need exists for an automated method that is accurate, reproducible and reliable. 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. Methods A total of 200 histological images of patients with UC from a database at University Hospital, Vandoeuvre-lès-Nancy, France were used for this study. The novel AI system was used to fully characterise histological images and automatically measure Nancy index. The in-house AI algorithm was developed using state-of-the-art image processing and machine learning algorithms based on deep learning and feature extraction. The cell regions of each image, followed by Nancy index, were manually annotated and measured independently by 3 histopathologists. Manual and AI-automated measurements of Nancy index score were done and assessed using the intraclass correlation coefficient (ICC). Results The 200-image dataset was divided into 2 groups (80% was used for training and 20% for testing). ICC statistical analyses were performed to evaluate AI tool and used as a reference to calculate the accuracy (Table 1). The average ICC amongst the histopathologists was 89.33 and average ICC between histopathologists and AI tool was 87.20. Despite the small number of image data, the AI tool was found to be highly correlated with histopathologists. Conclusion The high correlation of performance of the AI method suggested promising potential for IBD clinical applications. A standardised and validated histological AI-driven scoring system can potentially be used in daily IBD practice to eliminate the subjectivity of the pathologists and assess the disease severity for treatment decision.

DOP58 An artificial intelligence–driven scoring system to measure histological disease activity in Ulcerative Colitis