Rapid Endoscopic Diagnosis of Benign Ulcerative Colorectal Diseases with an Artificial Intelligence Contextual Framework

ABSTRACT

Background & Aims

Benign ulcerative colorectal diseases (UCDs) such as ulcerative colitis (UC), Crohn's disease (CD), ischemic colitis (IC) and intestinal tuberculosis (ITB) share similar phenotypes with different etiologies and treatment strategies. To accurately diagnose closely-related diseases like UCDs, we hypothesize that contextual learning is critical in enhancing the ability of the artificial intelligence models to differentiate the subtle differences in lesions amidst the vastly divergent spatial contexts.

Methods

White light colonoscopy datasets of patients with confirmed UCDs and normal controls were retrospectively collected. We developed a Multi-class Contextual Classification (MCC) model that can differentiate among the mentioned UCDs and normal controls by incorporating the tissue object contexts surrounding the individual lesion region in a scene and spatial information from other endoscopic frames (video-level) into a unified framework. Internal and external datasets were used to validate the model's performance.

Results

Training datasets included 762 patients, and the internal and external testing cohorts included 257 patients and 293 patients, respectively. Our MCC model provided a rapid reference diagnosis on internal test sets with a high averaged AUROC (image level: 0.950 and video-level: 0.973) and balanced accuracy (image-level: 76.1% and video-level: 80.8%), which was superior to junior endoscopists (accuracy: 71.8%, p<0.0001) and similar to experts (accuracy: 79.7%, p=0.732). The MCC model achieved a AUROC of 0.988 and balanced accuracy of 85.8% using external testing datasets.

Conclusions

These results enable this model to fit in the routine endoscopic workflow, and the contextual framework to be adopted for diagnosing other closely-related diseases.