Local Assessment and Small Bowel Crohn's Disease Severity Scoring using AI.

ABSTRACT:

Rationale and objectives

We present a machine learning and computer vision approach for a localized, automated, and standardized scoring of Crohn's disease (CD) severity in the small bowel, overcoming the current limitations of manual measurements CT enterography (CTE) imaging and qualitative assessments, while also considering the complex anatomy and distribution of the disease.

Materials and methods

Two radiologists introduced a severity score and evaluated disease severity at 7.5 mm intervals along the curved planar reconstruction of the distal and terminal ileum using 236 CTE scans. A hybrid model, combining deep-learning, 3-D CNN, and Random Forest model, was developed to classify disease severity at each mini-segment. Precision, sensitivity, weighted Cohen's score, and

SUBMITTER: Enchakalody BE 

PROVIDER: S-EPMC12108342 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature