Assessing Small Bowel Stricturing and Morphology in Crohn’s Disease Using Semi-automated Image Analysis

Abstract

Background Evaluating structural damage using imaging is essential for the evaluation of small intestinal Crohn’s disease (CD), but it is limited by potential interobserver variation. We compared the agreement of enterography-based bowel damage measurements collected by experienced radiologists and a semi-automated image analysis system. Methods Patients with small bowel CD undergoing a CT-enterography (CTE) between 2011 and 2017 in a tertiary care setting were retrospectively reviewed. CT-enterography studies were reviewed by 2 experienced radiologists and separately underwent automated computer image analysis using bowel measurement software. Measurements included maximum bowel wall thickness (BWT-max), maximum bowel dilation (DIL-max), minimum lumen diameter (LUM-min), and the presence of a stricture. Measurement correlation coefficients and paired t tests were used to compare individual operator measurements. Multivariate regression was used to model identification of strictures using semi-automated measures. Results In 138 studies, the correlation between radiologists and semi-automated measures were similar for BWT-max (r = 0.724, 0.702), DIL-max (r = 0.812, 0.748), and LUM-min (r = 0.428, 0.381), respectively. Mean absolute measurement difference between semi-automated and radiologist measures were no different from the mean difference between paired radiologists for BWT-max (1.26 mm vs 1.12 mm, P = 0.857), DIL-max (2.78 mm vs 2.67 mm, P = 0.557), and LUM-min (0.54 mm vs 0.41 mm, P = 0.596). Finally, models of radiologist-defined intestinal strictures using automatically acquired measurements had an accuracy of 87.6%. Conclusion Structural bowel damage measurements collected by semi-automated approaches are comparable to those of experienced radiologists. Radiomic measures of CD will become an important new data source powering clinical decision-making, patient-phenotyping, and assisting radiologists in reporting objective measures of disease status.

Big data in IBD: big progress for clinical practice

“…Similarly, assessment of CT/MRI images in IBD is extremely subjective; therefore, computer-aided scores could potentially overcome interobserver variation. A semiautomated image analysis software showed a performance similar to those of experienced radiologists for the assessment of CD structural bowel damage in abdominal CT-enterography data 89. Also, machine learning methods and algorithms have been applied to predict the grading of severity of CD in abdominal MRI data 90 91.…”

“…A semiautomated image analysis software showed a performance similar to those of experienced radiologists for the assessment of CD structural bowel damage in abdominal CT-enterography data. 89 Also, machine learning methods and algorithms have been applied to predict the grading of severity of CD in abdominal MRI data. 90 91 Additionally, machine learning algorithms could assist with the time-consuming assessment of wireless capsule endoscopy data.…”

Clinical applications of artificial intelligence and machine learning‐based methods in inflammatory bowel disease

“…The application of computer‐based automation to imaging interpretation would have similar benefits in reducing variation among radiologists' interpretations. Computer vision image analysis performed similarly to experienced radiologists in the assessment of CT enterography evidence of structural bowel damage in Crohn's disease 17 . Such AI image interpretation systems offer not only the potential for replicating expert assessment but also can provide detailed iterative measurements which may improve the personalization of therapeutic decision making and prognosis (Fig.…”

“…In this conceptual example, regions of diseased bowel can be predicted using extracted measures of bowel wall thickness, lumen diameter, and total bowel dilation. Of more value is the opportunity to better quantify intestinal disease using direct area and volume measurements, which are expected to aid personalization of care in IBD 17 ,. Outer wall; , Lumen; , Thickness; , Disease.…”

Emerging use of artificial intelligence in inflammatory bowel disease

“…Similarly, the inherent subjectivity of endoscopic and radiographic assessment has led to a great interest in automating image interpretation. ML assisted analysis of computed tomography and magnetic resonance imaging has been shown to effectively identify structural bowel damage, such as stricturing disease in CD[ 13 - 15 ]. Image analyzing programs have also been adapted to improve the efficiency of previously time consuming manual review of video capsule endoscopy images[ 16 ].…”