Spatiotemporal analysis of small bowel capsule endoscopy videos for outcomes prediction in Crohn’s disease

Raizy Kellerman, Amit Bleiweiss, Shimrit Samuel, Reuma Margalit-Yehuda, Estelle Aflalo, Oranit Barzilay, Shomron Ben-Horin, Rami Eliakim, Eyal Zimlichman, Shelly Soffer, Eyal Klang, Uri Kopylov

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Background: Deep learning techniques can accurately detect and grade inflammatory findings on images from capsule endoscopy (CE) in Crohn’s disease (CD). However, the predictive utility of deep learning of CE in CD for disease outcomes has not been examined. Objectives: We aimed to develop a deep learning model that can predict the need for biological therapy based on complete CE videos of newly-diagnosed CD patients. Design: This was a retrospective cohort study. The study cohort included treatment-naïve CD patients that have performed CE (SB3, Medtronic) within 6 months of diagnosis. Complete small bowel videos were extracted using the RAPID Reader software. Methods: CE videos were scored using the Lewis score (LS). Clinical, endoscopic, and laboratory data were extracted from electronic medical records. Machine learning analysis was performed using the TimeSformer computer vision algorithm developed to capture spatiotemporal characteristics for video analysis. Results: The patient cohort included 101 patients. The median duration of follow-up was 902 (354–1626) days. Biological therapy was initiated by 37 (36.6%) out of 101 patients. TimeSformer algorithm achieved training and testing accuracy of 82% and 81%, respectively, with an Area under the ROC Curve (AUC) of 0.86 to predict the need for biological therapy. In comparison, the AUC for LS was 0.70 and for fecal calprotectin 0.74. Conclusion: Spatiotemporal analysis of complete CE videos of newly-diagnosed CD patients achieved accurate prediction of the need for biological therapy. The accuracy was superior to that of the human reader index or fecal calprotectin. Following future validation studies, this approach will allow for fast and accurate personalization of treatment decisions in CD.

Original languageEnglish
JournalTherapeutic Advances in Gastroenterology
Volume16
DOIs
StatePublished - 1 Jan 2023

Keywords

  • artificial intelligence
  • capsule endoscopy
  • Crohn’s disease

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