- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT03842059
Computer-aided Detection for Colonoscopy
Computer-aided Detection With Deep Learning for Colorectal Adenoma During Colonoscopic Examination
Study Overview
Status
Intervention / Treatment
Detailed Description
Colonoscopy is a primary screening and follow-up tool to detect colorectal cancer, a third leading cause of cancer death in Taiwan. Most colorectal cancers (CRCs) arise from preexisting adenomas, and the adenoma-carcinoma sequence offers an opportunity for the screening and prevention of CRCs. The removal of adenomatous polyps can lower the incidence of CRCs and result in reduced motality from CRCs. The adenoma detection rate, the proportion of screening colonoscopies performed by a endoscopist that detect at least one colorectal adenoma or adenocarcinoma, has been recommended as a quality indicator. The adenoma detection rate was inversely associated with the risks of interval colorectal cancer, advanced-stage interval cancer, and fatal interval cancer. However, adenoma detection rates vary widely among endoscopists in both academic and community settings. Polyp miss rates as high as 20% have been reported for high definition resolution colonoscopy. An improvement in adenoma detection rate at screening colonoscopy, translates into reduced risks of interval colorectal cancer and colorectal cancer death. Computer-aided detection of polyps might assist endoscopists to reduce the miss rate and enhance screening performance during colonoscopy. Computer-aided diagnosis and computer-aided detection are computerized systems that learn and inference in medical fields. Computer-aided diagnosis has been developed in colon polyp classification.
Computer-assisted image analysis has the potential to further aid adenoma detection but has remained underdeveloped. A notable benefit of such a system is that no alteration of the colonoscope or procedure is necessary. Machine learning with a deep neural network has been successfully applied to many areas of science and technology, such as object recognition and detection of computer vision, speech recognition, natural language processing. We developed an artificial intelligent computer system (PX-1) with a deep neural network to analyze real-time video signals from the endoscopy station. This randomised controlled trial compared ADR between computer-assisted colonoscopy and standard colonoscopy.
Study Type
Enrollment (Anticipated)
Phase
- Not Applicable
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Description
Inclusion Criteria:
Patients aged ≥20 years, scheduled for colonoscopy for one of the following indications for colonoscopy, were invited to participate in this study: polyp surveillance, changed bowel habits and/or bloody stools, bowel complaints, a positive family history for CRC, a positive FOBT, abdominal pain, diarrhoea, post-polypectomy surveillance.
Exclusion Criteria:
We excluded patients from this study if: (1) they had known colonic neoplasia or inflammatory or other significant colonic disease, such as patients specifically presenting for polypectomy; (2) there was open bleeding or they were receiving an emergency colonoscopy; (3) they had previously previous colonic resection; (4) they were in poor general condition (more than American Society of Anesthesiologists grade III); (5) they were receiving anticoagulant medication; (6) they had severe comorbidity, including end-stage cardiovascular, pulmonary, liver or renal disease); (7) they were not able or refused to give informed written consent; (8) following enrolment and randomisation to one of the arms, those subjects who had inadequate colon preparation or in whom the caecum could not be reached were also excluded.
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Screening
- Allocation: Randomized
- Interventional Model: Parallel Assignment
- Masking: Double
Arms and Interventions
Participant Group / Arm |
Intervention / Treatment |
---|---|
Experimental: Computer-aided detection
|
We developed an artificial intelligent computer system with a deep neural network (PX-1) to analyze real-time video signals from the endoscopy station
|
Placebo Comparator: Standard colonoscopy
|
Standard colonoscopy
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Adenoma detection rate
Time Frame: During colonoscopic examination procedure
|
Adenoma detection rate
|
During colonoscopic examination procedure
|
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
adenomas detected per subject
Time Frame: During colonoscopic examination procedure
|
adenomas detected per subject
|
During colonoscopic examination procedure
|
Collaborators and Investigators
Sponsor
Study record dates
Study Major Dates
Study Start (Anticipated)
Primary Completion (Anticipated)
Study Completion (Anticipated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Other Study ID Numbers
- 107-2314-B-016 -011-MY2
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
Clinical Trials on Compare Between Computer-assisted Colonoscopy and Standard Colonoscopy
-
University of FloridaRecruitingLung Transplant | To Compare Lung Transplant Outcomes Between Study Patients and Standard Patients Who Have Already Undergone Lung Transplantation by Conventional ProceduresUnited States
-
Hospital Clinic of BarcelonaInstituto de Salud Carlos IIICompletedArtificial Intelligence | Colonoscopy | Colorectal Polyp | Histology | Hyperplastic Polyp | Computer-aided Diagnosis | Adenoma Colon PolypSpain
Clinical Trials on Standard colonoscopy
-
Dr. Horst Schmidt Klinik GmbHCompletedAdenoma | Colo-rectal CancerGermany
-
The University of Texas Health Science Center,...WithdrawnColorectal NeoplasmsUnited States
-
The Catholic University of KoreaUnknown
-
Northwestern UniversityTerminated
-
Chinese University of Hong KongCompletedFirst Colonoscopy ExaminationChina
-
Chinese University of Hong KongUnknownScreening ColonoscopyHong Kong
-
University of NaplesCompletedAdenomatous PolypsItaly
-
Portsmouth Hospitals NHS TrustUniversity of PortsmouthCompletedIntestinal Neoplasms | Digestive System Disease | Gastrointestinal Disease | Colorectal Neoplasia | Intestinal Disease | Gastrointestinal Neoplasm | Digestive NeoplasmsUnited Kingdom
-
Smart Medical Systems Ltd.CompletedColorectal Cancer | AdenomaGermany
-
Smart Medical Systems Ltd.CompletedColorectal Cancer | Adenoma | PolypsUnited States, Netherlands, Israel, United Kingdom, India, Germany, Denmark, Italy