Deep learning analysis of capillary refill dynamics in ischemic colitis: differentiating reversible vs. gangrenous mucosa.

Ischemic colitis remains a diagnostic challenge, as endoscopic grading of mucosal ischemia is subjective and insensitive for transmural infarction. While the majority of non-gangrenous cases are resolved conservatively, gangrenous mucosa necessitates immediate resection. The current reliance on visual signals, such as color and bleeding, results in misclassification and variability. We propose a novel deep learning approach to quantify micro-capillary refill dynamics from colonoscopy video. The technology produces objective perfusion measures and real-time viability classification by examining frame-by-frame color recovery following temporary mucosal blanching. This approach makes use of recent developments in AI-assisted colonoscopy, annotated datasets, and endoscopic hardware. By incorporating capillary refill analysis, operational decision-making might be improved, interobserver variability could be decreased, and unnecessary colectomy and delayed gangrene treatment could be avoided. Pilot studies are warranted to validate this concept and establish its role in guiding therapy for ischemic colitis.