Artificial Intelligence Identifying Polyps in Real-world Colonoscopy

NCT03761771 | Completed DMC

• 1 other identifier: AI-1
• Study Type: observational
• Target: 209
• Locations: 1 country
• Sites: 2

Brief Summary

Recently, artificial intelligence (AI) assisted image recognition has made remarkable breakthroughs in various medical fields with the developing of deep learning and conventional neural networks (CNNs). However, all current AI assisted-diagnosis systems (ADSs) were established and validated on endoscopic images or selected videos, while its actual assisted-diagnosis performance in real-world colonoscopy is up to now unknown. Therefore, we validated the performance of an ADS in real-world colonoscopy, which is based on deep learning algorithm and CNNs, trained and tested in multicenter datasets of 20 endoscopy centers.

Conditions

Sensitivity of the ADS in Identifying Polyps in Real-world Colonoscopy; Mean Number of Polyps Per Colonoscopy for Colonoscopists and Colonoscopists + ADS

Outcome Measures

Primary Outcomes (1)

• sensitivity of the ADS in identifying polyps

  Polyps that were only reported by colonoscopists were considered to be missed by the ADS (polyps were reported by the colonoscopists and the ADS did not identify the location of polyps until colonoscopists unfolded and pictured the polyps.)

  1 hour

Secondary Outcomes (1)

• false positves of the ADS per colonoscopy withdrawal

  1 hour

Study Arms (1)

colonoscopy withdrawal with the ADS monitoring

The ADS automatically initiated once the ileocecal valve was pictured by the colonoscopist or the colonoscopist recorded any image of colon during the insertion. When colonoscopists withdrew the colonoscopies and inspect the colons, the video streaming of colonoscopies was real-time switched to the ADS, which made it feasible to identify and classify lesions in real time.

Device: colonoscopy withdrawal with the ADS monitoring

Interventions

colonoscopy withdrawal with the ADS monitoring DEVICE

During the testing of trained ADS, when the system doubts colonic lesions from the input data of the test images, a rectangular frame was displayed in the endoscopic image to surround the lesion. If the system confirmed it as the colonic lesions, a sound of reminder will be played and the types of lesions (non-adenomatous polyps, adenomatous polyps and colorectal cancers) will be classified by the system. We adopted several standards to define the identification and classification of colonic lesions: 1) when the system identified and confirmed any lesion in the images of no polyps or cancers, the results were judged to be false-positive. 2) when the system both confirmed and correctly localized the lesions in images (IoU \> 0.3), the results were judged to be true-positive. 3) when the system did not confirm or correctly localize the lesions, the results were judged as false-negative. 4) when system confirmed no lesions in the normal images, the results were judged to be true-negative.

colonoscopy withdrawal with the ADS monitoring

Eligibility Criteria

• Age: 18 Years - 75 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

consecutive outpatient who recieved colonoscopy

You may qualify if:

• patients receiving screening colonoscopy
• patients receiving surveillance colonoscopy
• patients receiving diagnostic colonoscopy

You may not qualify if:

• patients with declined consent
• patients with poor bowel preparation
• patients with failed cecal intubation
• patients with colonic resection
• patients with inflammatory bowel diseases
• patients with polyposis

Sponsors & Collaborators

• Zhaoshen Li: lead

Study Sites (2)

Changhai Hospital, Second Military Medical University

Shanghai, 200433, China

Location

Changhai Hospital

Shanghai, 200433, China

Location

Related Publications (4)

• Byrne MF, Chapados N, Soudan F, Oertel C, Linares Perez M, Kelly R, Iqbal N, Chandelier F, Rex DK. Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model. Gut. 2019 Jan;68(1):94-100. doi: 10.1136/gutjnl-2017-314547. Epub 2017 Oct 24.

  PMID: 29066576 BACKGROUND

• Wang Z, Meng Q, Wang S, Li Z, Bai Y, Wang D. Deep learning-based endoscopic image recognition for detection of early gastric cancer: a Chinese perspective. Gastrointest Endosc. 2018 Jul;88(1):198-199. doi: 10.1016/j.gie.2018.01.029. No abstract available.

  PMID: 29935613 BACKGROUND

• Urban G, Tripathi P, Alkayali T, Mittal M, Jalali F, Karnes W, Baldi P. Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy. Gastroenterology. 2018 Oct;155(4):1069-1078.e8. doi: 10.1053/j.gastro.2018.06.037. Epub 2018 Jun 18.

  PMID: 29928897 RESULT

• Wang Z, Zhao S, Bai Y. Artificial Intelligence as a Third Eye in Lesion Detection by Endoscopy. Clin Gastroenterol Hepatol. 2018 Sep;16(9):1537. doi: 10.1016/j.cgh.2018.04.032. No abstract available.

  PMID: 30119878 RESULT

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Director of Gastroenterology Dept and Digestive Endoscopy Center

Study Record Dates

• First Submitted: November 30, 2018
• First Posted: December 3, 2018
• Study Start: November 1, 2018
• Primary Completion: December 10, 2018
• Study Completion: December 10, 2018
• Last Updated: December 17, 2018

Record last verified: 2018-12

Locations

CN (2)