Artificial Intelligence in Image Recognition of Pouchoscopies in Patients With Restorative Proctocolectomy (PouchVision)

The application of artificial intelligence in pouchoscopy of patients with restorative proctocolectomy might improve the diagnosis of pouchitis and neoplasms. The aim of this pilot study is to develop a convolutional neural network algorithm for pouchoscopy

Study Overview

• Status: Completed
• Conditions: Pouches, Ileoanal
• Intervention / Treatment: Diagnostic test: Artificial intelligence used for image recognition in pouchoscopy

Detailed Description

Restorative proctocolectomy is the standard procedure for treatment of refractory severe colitis in inflammatory bowel disease as well as the standard procedure for carcinoma preventive treatment of patients with inflammatory bowel disease with colonic neoplasia and patients with familial adenomatous polyposis coli (FAP). Pouchoscopy can be used to monitor the success of therapy and to detect complications such as pouchitis or neoplasia. Artificial Intelligence assisted image recognition programs can support the examiner in finding a diagnosis and train physicians in training, objectify endoscopic findings in the context of studies and might make biopsies unnecessary, thus saving costs. The application of Artificial Intelligence in pouchoscopy has not been demonstrated to date. The aim of this study is to develop, an image recognition algorithm that reliably detects the different graduations of pouch inflammation. This requires training and fine-tuning of the image recognition program PiTorch using the largest possible amount of image data, which will be recruited from the image databases of the UMM and the Theresienkrankenhaus Mannheim. A test run for statistical evaluation will be performed on an independent cohort.

• Study Type: Observational
• Enrollment (Actual): 500

Contacts and Locations

Study Locations

• Germany
  • BW
    • Mannheim, BW, Germany, 68165
      • Theresienkrankenhaus und St. Hedwigkliniken GmbH

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No

• Sampling Method: Probability Sample

Study Population

Adult patients with inflammatory bowel disease and status after restorative proctocolectomy with ileoanal pouch might develop pouchitis or neoplasia in the pouch.

Description

Inclusion Criteria:

• All patients aged ≥ 18 years with inflammatory bowel disease and status after restorative proctocolectomy with ileoanal pouch who had received a pouchoscopy

Exclusion Criteria:

• Very poor endoscopic image quality

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Restorative colectomy with ileoanal pouch; Patients with restorative colectomy with ileoanal pouch who receive pouchoscopy for detection of pouchitis or neoplasm	Diagnostic test: Artificial intelligence used for image recognition in pouchoscopy; The aim of this study is to develop an image recognition algorithm that reliably detects the different graduations of pouch inflammation and neoplasms in the pouch

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
AI versus endoscopist	Detection of pouchitis by AI versus assessment by endoscopist in pouchoscopy	Immediately after application of AI algorithm or after assessment of the endoscopic image by the endoscopist
AI versus pathologist	Detection of pouchitis by AI versus pathologist in pouchoscopy	Immediately after application of AI algorithm or after assessment of the microscopic image of the pouch biopsy by the pathologist

Collaborators and Investigators

• Sponsor: Theresienkrankenhaus und St. Hedwig-Klinik GmbH
• Collaborators: Universitätsmedizin Mannheim
• Investigators: Principal Investigator: Daniel Schmitz, PhD, Theresienkrankenhaus Mannheim, University of Heidelberg

Publications and helpful links

General Publications

• van der Sommen F, de Groof J, Struyvenberg M, van der Putten J, Boers T, Fockens K, Schoon EJ, Curvers W, de With P, Mori Y, Byrne M, Bergman JJGHM. Machine learning in GI endoscopy: practical guidance in how to interpret a novel field. Gut. 2020 Nov;69(11):2035-2045. doi: 10.1136/gutjnl-2019-320466. Epub 2020 May 11.

Study record dates

Study Major Dates

• Study Start (Actual): June 1, 2021
• Primary Completion (Actual): June 1, 2023
• Study Completion (Actual): June 1, 2023

Study Registration Dates

• First Submitted: April 21, 2021
• First Submitted That Met QC Criteria: April 26, 2021
• First Posted (Actual): April 29, 2021

Study Record Updates

• Last Update Posted (Actual): August 29, 2023
• Last Update Submitted That Met QC Criteria: August 27, 2023
• Last Verified: August 1, 2023

More Information

Terms related to this study

• Other Study ID Numbers: PouchVision1.0

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No