Clinical Efficacy Evaluation of a Computer-aided Colonoscopy as Compared With the Standard Colonoscopy.

Summary

Colonoscopy is clinically used as the gold standard for detection of colorectal cancer (CRC) and removal of adenomatous polyps of the colon and rectum. Evidence has shown that CRC could be prevented by colonoscopic removal of adenomatous polyps. Despite the success of colonoscopy in reducing cancer-related deaths, there exists a disappointing level of adenomas missed at colonoscopy. In recent years, emerging artificial intelligence (AI) and computer-aided detection (CADe) technology has been shown to improve ADR. Based on a meta-analysis, ADR was demonstrated to be significantly higher in the CADe groups than in the standard colonoscopy groups, representing a relative risk of 25.2%.

In this study, performance of colonoscopy with or without aid of CADe will be compared in terms of quality indicators. The adenoma detection rate (ADR), which is the proportion of average-risk patients undergoing screening colonoscopy in whom an adenoma is found, is regarded as a robust measure of colonoscopy performance quality that correlates with subsequent cancer risk. Thus, ADR is taken as the primary outcome of this study. The target population includes individuals who are undergoing screening, diagnostic, or surveillance colonoscopy.

Conditions

• Colon Adenoma
• Colorectal Cancer

Interventions

DEVICE

"aetherAI" Computer-aided Polyp Detection (CADe) Systems for Colonoscopy

The investigational medical device is intended to automatically detect potential polyps via colonoscopy in real-time during colonoscopy examinations. The subject device contains an artificial intelligence/machine learning (AI/ML) advanced algorithm to aid the endoscopists in detection of colonic mucosal lesions and the detected polyps will be highlighted to the endoscopists during the real-time colonoscopy procedures.

PROCEDURE

Standard colonoscopy

Standard colonoscopy procedure.

Sponsors & Collaborators

• Changhua Christian Hospital

  collaborator OTHER

• Shin Kong Hospital

  collaborator UNKNOWN

• Fu Jen Catholic University Hospital

  collaborator OTHER

• aetherAI Co., Ltd

  collaborator UNKNOWN

• National Taiwan University Hospital

  lead OTHER

Study Design

Allocation

RANDOMIZED

Purpose

SCREENING

Masking

DOUBLE

Model

PARALLEL

Eligibility

Min Age

40 Years

Max Age

80 Years

Sex

ALL

Healthy Volunteers

Yes

Timeline & Regulatory

Start

2022-03-01

Primary Completion

2025-12-31

Completion

2025-12-31

Countries

• Taiwan

Study Locations