Core Concepts
AI systems show high potential in detecting mucosal healing in ulcerative colitis, improving diagnostic accuracy and reproducibility.
Abstract
Introduction
AI systems show potential in detecting mucosal healing in ulcerative colitis.
High sensitivity and specificity in replicating expert opinions.
Evaluating AI Detection
AI systems can provide objective and real-time diagnosis of mucosal healing.
Deep learning algorithms based on convolutional neural networks enhance diagnostic accuracy.
Research Findings
AI systems achieved satisfactory performance in evaluating mucosal healing in ulcerative colitis.
Different performance metrics for fixed images and videos.
Expert Insights
AI can standardize the assessment of mucosal healing in ulcerative colitis.
Consensus needed for training AI models to improve quality and reproducibility.
Conclusion
AI has the potential to enhance the assessment of mucosal healing in ulcerative colitis.
Stats
AI algorithms achieved a sensitivity of 0.91 and specificity of 0.89 when evaluating fixed images.
AI algorithms achieved 0.86 sensitivity and 0.91 specificity when evaluating videos.
Diagnostic odds ratio (DOR) of 92.42 for fixed images and 70.86 for videos.
Summary receiver operating characteristic curve (SROC) of 0.957 for fixed images and 0.941 for videos.
Area under the curve (AUC) of 0.957 for fixed images and 0.941 for videos.
Quotes
"AI software is expected to potentially solve the longstanding issue of low-to-moderate interobserver agreement when human endoscopists are required to indicate mucosal healing or different grades of inflammation in ulcerative colitis." - Alessandro Rimondi
"In my opinion, artificial intelligence tends to better perform when it is required to evaluate a dichotomic outcome (such as polyp detection, which is a yes or no task) than when it is required to replicate more difficult tasks (such as polyp characterization or judging a degree of inflammation), which have a continuous range of expression." - Alessandro Rimondi