Clinical trial NCT04693078: Detection of Colonic Polyps Via a Large Scale Artificial Intelligence (AI) System
Official title: Detection of Colonic Polyps Via a Large Scale AI System
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Colonic Polyp
Keywords: Colonoscopy Performance
Lead sponsor: Shaare Zedek Medical Center
Sponsor class: OTHER
Collaborators: name: Google LLC.
First posted: 2021-01-05
Enrollment: 100
Sex: ALL
Minimum age (years): 40
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: SCREENING

Brief summary: Colonoscopy is the gold standard for detection and removal of precancerous lesions, and has been amply shown to reduce mortality. However, the miss rate for polyps during colonoscopies is 22-28%, while 20-24% of the missed lesions are histologically confirmed precancerous adenomas. To address this shortcoming, the investigators propose a new polyp detection system based on deep learning, which can alert the operator in real-time to the presence and location of polyps during a colonoscopy. The investigators dub the system DEEP: (DEEP) DEtection of Elusive Polyps. The DEEP system was trained on 3,611 hours of colonoscopy videos derived from two sources, and was validated on a set comprising 1,393 hours of video, coming from a third, unrelated source. For the validation set, the ground truth labelling was provided by offline gastroenterologist annotators, who were able to watch the video in slow-motion and pause/rewind as required; two or three specialist annotators examined each video.

This is a prospective, non-blinded, non-randomized pilot study of patients undergoing elective screening and surveillance colonoscopies using DEEP.

The aim of the study is to:

Assess the:

1. Number of additional polyps detected by the DEEP system in real time colonoscopy.
2. Safety by prospective assessment of the rate of adverse events during the study period attributed or not to the use of the DEEP system.
3. Stability of the DEEP system by measuring the rate of false positives (False Alarms) per colonoscopies 4 And to examine its feasibility and usefulness of in clinical practice by assessing the colonoscopist user experience while using the DEEP system in a 5 point scale.


Eligibility criteria: Inclusion Criteria:

* Healthy subjects undergoing routine screening or surveillance colonoscopy in an ambulatory non urgent setting.
* Able to understand the study protocol and sign inform consent.

Exclusion Criteria:

* Previous surgery involving the colon or rectum
* Known diagnosis of colorectal cancer
* Known history of inflammatory bowel disease
* Known or suspected diagnosis of familial polyposis syndrome

Primary outcomes:
measure: Number of Additional Polyps Detected by the DEEP System in Real Time Colonoscopy
timeFrame: Through study completion, an average of 12 months
measure: The Rate of Adverse Events During the Study Attributed or Not to the Use of the DEEP System
timeFrame: Until discharge, assessed up to 7 days
Secondary outcomes:
measure: Rate of False Positives (False Alarms) Per Colonoscopy
timeFrame: Through study completion, an average of 12 months
measure: Colonoscopist User Experience While Using the DEEP System in a 5 Point Scale
timeFrame: Through study completion, an average of 12 months
Other outcomes:
Locations:
city: Jerusalem
country: Israel
facility: Digestive Diseases Institute, Shaare Zedek Medical Center
Study officials:
References:
citation: Livovsky DM, Veikherman D, Golany T, Aides A, Dashinsky V, Rabani N, Ben Shimol D, Blau Y, Katzir L, Shimshoni I, Liu Y, Segol O, Goldin E, Corrado G, Lachter J, Matias Y, Rivlin E, Freedman D. Detection of elusive polyps using a large-scale artificial intelligence system (with videos). Gastrointest Endosc. 2021 Dec;94(6):1099-1109.e10. doi: 10.1016/j.gie.2021.06.021. Epub 2021 Jun 30.