Just published: AI Scoring System for Histology Review in Ulcerative Colitis

Just published: AI Scoring System for Histology Review in Ulcerative Colitis

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

IAG, Takeda and scientific collaborators just published validation article for the novel AI powered approach to assess histopathology slides in Ulcerative Colitis

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.

A total of 200 histological images of patients with UC were used in this study. A novel 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 the Nancy index, were manually annotated and measured independently by four histopathologists. Manual and AI-automated measurements of the Nancy index score were conducted and assessed using the intraclass correlation coefficient (ICC).

Validation work of AI vs expert pathologists: Intraclass correlation coefficient statistical analyses were performed to evaluate the AI tool and used as a reference to calculate the accuracy. The average ICC among the histopathologists was 89.3 and the average ICC between histopathologists and the AI tool was 87.2. The AI tool was found to be highly correlated with histopathologists.

The high correlation of performance of the AI method suggests promising potential for inflammatory bowel disease clinical applications. A standardized automated histological AI-driven scoring system can potentially be used in daily inflammatory bowel disease practice to reduce training needs and resource use, eliminate the subjectivity of the pathologists, and assess disease severity for treatment decisions.

‘AI-based methods demonstrate feasibility, reliability and validity when utilized to quantify UC slides. Computer-aided evaluation could be considered as an alternative to conventional expert driven methods especially when it comes to pathology slide reviews in clinical research or practice,’ said Dr Olga Kubassova, IAG’s President.

About Image Analysis Group (IAG)

IAG, Image Analysis Group is a unique partner to life sciences companies. IAG leverages expertise in medical imaging and the power of Dynamika™ – our proprietary cloud-based platform, to de-risk clinical development and deliver lifesaving therapies into the hands of patients much sooner.  IAG provides early drug efficacy assessments, smart patient recruitment and predictive analysis of advanced treatment manifestations, thus lowering investment risk and accelerating study outcomes. IAG bio-partnering takes a broader view on asset development bringing R&D solutions, operational breadth, radiological expertise via risk-sharing financing and partnering models.

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