Related Experiment Video
Updated: Feb 4, 2026
Systematic Scoring Analysis for Intestinal Inflammation in a Murine Dextran Sodium Sulfate-Induced Colitis Model
Published on: February 14, 2021
Fully automated diagnostic system with artificial intelligence using endocytoscopy to identify the presence of
Yasuharu Maeda1, Shin-Ei Kudo1, Yuichi Mori1
1Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan.
Background And Aims:
In the treatment of ulcerative colitis (UC), an incremental benefit of achieving histologic healing beyond that of endoscopic mucosal healing has been suggested; persistent histologic inflammation increases the risk of exacerbation and dysplasia. However, identification of persistent histologic inflammation is extremely difficult using conventional endoscopy. Furthermore, the reproducibility of endoscopic disease activity is poor. We developed and evaluated a computer-aided diagnosis (CAD) system to predict persistent histologic inflammation using endocytoscopy (EC; 520-fold ultra-magnifying endoscope).
Methods:
We evaluated the accuracy of the CAD system using test image sets. First, we retrospectively reviewed the data of 187 patients with UC from whom biopsy samples were obtained after endocytoscopic observation. EC images and biopsy samples of each patient were collected from 6 colorectal segments: cecum, ascending colon, transverse colon, descending colon, sigmoid colon, and rectum. All EC images were tagged with reference to the biopsy sample's histologic activity. For validation samples, 525 validation sets of 525 independent segments were collected from 100 patients, and 12,900 EC images from the remaining 87 patients were used for machine learning to construct CAD. The primary outcome measure was the diagnostic ability of CAD to predict persistent histologic inflammation. Its reproducibility for all test images was also assessed.
Results:
CAD provided diagnostic sensitivity, specificity, and accuracy as follows: 74% (95% confidence interval, 65%-81%), 97% (95% confidence interval, 95%-99%), and 91% (95% confidence interval, 83%-95%), respectively. Its reproducibility was perfect (κ = 1).
Conclusions:
Our CAD system potentially allows fully automated identification of persistent histologic inflammation associated with UC.
Related Concept Videos
01:29Drugs for Treatment of Ulcerative Colitis in IBD
01:27Inflammatory Bowel Disease I: Ulcerative Colitis
Inflammatory bowel disease, or IBD, encompasses a group of disorders characterized by chronic inflammation or ulceration of the gastrointestinal tract.
Risk Factors
The exact cause of IBD remains unclear, although it is believed to be due to a mix of genetic, environmental, microbial, and immune factors. Genetic factors are significant in determining susceptibility to IBD, with family history being a critical risk factor. Individuals with a first-degree relative who has IBD are at...
01:28Peptic Ulcer Disease III: Clinical Manifestations and Diagnostic Studies
Few clinical manifestations differentiate gastric ulcers from duodenal ulcers. Distinctions in the location, timing, and pain relief are crucial for healthcare providers in differentiating between gastric and duodenal ulcers during clinical assessments.
01:38Inflammation
01:27Intelligence
01:29Measures of Intelligence
Validity refers to how well a test measures what it claims to measure. An intelligence test should accurately assess intelligence rather than another characteristic, like anxiety. Criterion validity is one way to evaluate this;...