Artificial Intelligence in the Detection of Right Sided Colonic Polyp in Different Operator Experience

NCT ID: NCT05990218

Last Updated: 2024-12-13

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Clinical Phase

NA

Total Enrollment

240 participants

Study Classification

INTERVENTIONAL

Study Start Date

2023-02-13

Study Completion Date

2026-02-28

Brief Summary

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Colonoscopy is the gold standard modality for the detection of colonic polyp. However, miss polyp occurs especially in right sided colon. Artificial intelligence (AI) is one of the modality to improve polyp detection but the benefit of AI in operators with different endoscopic experience is still limited. This study aimed to evaluate the efficacy of AI in the detection of right sided colonic polyp in operators with different endoscopic experience by using double insertion of right side colon, back-to-back basis.

Detailed Description

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Conditions

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Colon Polyp

Keywords

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colon polyp artificial intellegence colonoscopy

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

Patients randomized to 4 arms: control with experienced operator, control with beginner operator, AI with experienced operator, AI with beginner operator
Primary Study Purpose

SCREENING

Blinding Strategy

SINGLE

Participants
The patients were masked from being randomized to operator and endoscopic method

Study Groups

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control, experienced

Patients received colonoscopy with double insertion of right sided colon under white light by experienced endoscopist

Group Type ACTIVE_COMPARATOR

control

Intervention Type DEVICE

The patient received endoscopy under conventional white light for polyp detection during second endoscopic withdrawal.

control, beginner

Patients received colonoscopy with double insertion of right sided colon under white light by beginner endoscopist

Group Type ACTIVE_COMPARATOR

control

Intervention Type DEVICE

The patient received endoscopy under conventional white light for polyp detection during second endoscopic withdrawal.

AI, experience

Patients received colonoscopy with double insertion of right sided colon under AI by experienced endoscopist

Group Type EXPERIMENTAL

Artificial intellegence. CADe syste,

Intervention Type DEVICE

The patient received endoscopy under CADe system for polyp detection during second endoscopic withdrawal.

AI, beginner

Patients received colonoscopy with double insertion of right sided colon under AI by beginner endoscopist

Group Type EXPERIMENTAL

Artificial intellegence. CADe syste,

Intervention Type DEVICE

The patient received endoscopy under CADe system for polyp detection during second endoscopic withdrawal.

Interventions

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Artificial intellegence. CADe syste,

The patient received endoscopy under CADe system for polyp detection during second endoscopic withdrawal.

Intervention Type DEVICE

control

The patient received endoscopy under conventional white light for polyp detection during second endoscopic withdrawal.

Intervention Type DEVICE

Eligibility Criteria

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Inclusion Criteria

* participants age 40-80 years

Exclusion Criteria

* History of colonic surgery (except appendectomy)
* Lower gastrointestinal bleeding
* unstable vital sign during endoscopy of pregnancy
* history of inflammatory bowel disease, polyposis syndrome, colon cancer, colonic stricture, abnormal coaglulation, organ failure
Minimum Eligible Age

40 Years

Maximum Eligible Age

80 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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Department of Medical Services Ministry of Public Health of Thailand

OTHER_GOV

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Locations

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Rajavithi Hospital

Bangkok, Bangkok, Thailand

Site Status RECRUITING

Countries

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Thailand

Facility Contacts

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Tanyaporn Chantarojanasiri, MD

Role: primary

Tanyaporn Chantarojanasiri, MD

Role: backup

References

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Kamba S, Tamai N, Saitoh I, Matsui H, Horiuchi H, Kobayashi M, Sakamoto T, Ego M, Fukuda A, Tonouchi A, Shimahara Y, Nishikawa M, Nishino H, Saito Y, Sumiyama K. Reducing adenoma miss rate of colonoscopy assisted by artificial intelligence: a multicenter randomized controlled trial. J Gastroenterol. 2021 Aug;56(8):746-757. doi: 10.1007/s00535-021-01808-w. Epub 2021 Jul 3.

Reference Type BACKGROUND
PMID: 34218329 (View on PubMed)

Other Identifiers

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031/2566

Identifier Type: -

Identifier Source: org_study_id