A virtual chromoendoscopy artificial intelligence system to detect endoscopic and histologic activity/remission and predict clinical outcomes in ulcerative colitis.
Author
Iacucci, MariettaCannatelli, Rosanna
Parigi, Tommaso L
Nardone, Olga M
Tontini, Gian Eugenio
Labarile, Nunzia
Buda, Andrea
Rimondi, Alessandro
Bazarova, Alina
Bisschops, Raf
Del Amor, Rocio
Meseguer, Pablo
Naranjo, Valery
Ghosh, Subrata
Grisan, Enrico
Publication date
2022-10-13Subject
Microbiology. ImmunologyPractice of medicine
Patients. Primary care. Medical profession. Forensic medicine
Oncology. Pathology.
Metadata
Show full item recordAbstract
Background: Endoscopic and histological remission (ER, HR) are therapeutic targets in ulcerative colitis (UC). Virtual chromoendoscopy (VCE) improves endoscopic assessment and the prediction of histology; however, interobserver variability limits standardized endoscopic assessment. We aimed to develop an artificial intelligence (AI) tool to distinguish ER/activity, and predict histology and risk of flare from white-light endoscopy (WLE) and VCE videos. Methods: 1090 endoscopic videos (67 280 frames) from 283 patients were used to develop a convolutional neural network (CNN). UC endoscopic activity was graded by experts using the Ulcerative Colitis Endoscopic Index of Severity (UCEIS) and Paddington International virtual ChromoendoScopy ScOre (PICaSSO). The CNN was trained to distinguish ER/activity on endoscopy videos, and retrained to predict HR/activity, defined according to multiple indices, and predict outcome; CNN and human agreement was measured. Results: The AI system detected ER (UCEIS ≤ 1) in WLE videos with 72 % sensitivity, 87 % specificity, and an area under the receiver operating characteristic curve (AUROC) of 0.85; for detection of ER in VCE videos (PICaSSO ≤ 3), the sensitivity was 79 %, specificity 95 %, and the AUROC 0.94. The prediction of HR was similar between WLE and VCE videos (accuracies ranging from 80 % to 85 %). The model's stratification of risk of flare was similar to that of physician-assessed endoscopy scores. Conclusions: Our system accurately distinguished ER/activity and predicted HR and clinical outcome from colonoscopy videos. This is the first computer model developed to detect inflammation/healing on VCE using the PICaSSO and the first computer tool to provide endoscopic, histologic, and clinical assessment.Citation
Iacucci M, Cannatelli R, Parigi TL, Nardone OM, Tontini GE, Labarile N, Buda A, Rimondi A, Bazarova A, Bisschops R, Del Amor R, Meseguer P, Naranjo V, Ghosh S, Grisan E; PICaSSO group. A virtual chromoendoscopy artificial intelligence system to detect endoscopic and histologic activity/remission and predict clinical outcomes in ulcerative colitis. Endoscopy. 2023 Apr;55(4):332-341. doi: 10.1055/a-1960-3645. Epub 2022 Oct 13.Type
ArticlePMID
36228649Journal
EndoscopyPublisher
Thieme Gruppeae974a485f413a2113503eed53cd6c53
10.1055/a-1960-3645