Clinical trial NCT05734820: Computer-aided Detection During Screening Colonoscopy
Official title: Real-time Computer-aided Polyp/Adenoma Detection During Screening Colonoscopy: a Single-center Crossover Trial
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: NA
Conditions: Colorectal Polyp
Colorectal Cancer
Colorectal Adenoma
Keywords: Artificial intelligence
Colonoscopy
colorectal cancer
Lead sponsor: Instituto Ecuatoriano de Enfermedades Digestivas
Sponsor class: OTHER
First posted: 2023-02-21
Enrollment: 312
Sex: ALL
Minimum age (years): 45
Maximum age (years): 89
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NON_RANDOMIZED
Intervention model: CROSSOVER
Intervention model description: Blinded, single center, controlled, prospective trial
Primary purpose: DIAGNOSTIC

Brief summary: Nowadays, colonoscopy is considered the gold standard for the detection of lesions in the colorectal mucosa. However, around 25% of polyps may be missed during the conventional colonoscopy. Based on this, new technological tools aimed to improve the quality of the procedures, diminishing the technical and operator-related factors associated with the missed lesions. These tools use artificial intelligence (AI), a computer system able to perform human tasks after a previous training process from a large dataset. The DiscoveryTM AI-assisted polyp detector (Pentax Medical, Hoya Group, Tokyo, Japan) is a newly developed detection system based on AI. It was designed to alert and direct the attention to potential mucosal lesions. According to its remarkable features, it may increase the polyp and adenoma detection rates (PDR and ADR, respectively) and decrease the adenoma miss rate (AMR).

Based on the above, the investigators aim to assess the real-world effectiveness of the DiscoveryTM AI-assisted polyp detector system in clinical practice and compare the results between expert (seniors) and non-expert (juniors) endoscopists.

Detailed description: Colorectal cancer (CRC) is worldwide the second and third cancer-related cause of death in men and women, respectively. For the detection of lesions in the mucosa (premalignant and malignant), colonoscopy has been considered the gold standard. However, up to 25% of lesions can be missed during conventional colonoscopy. Some technical (i.e., bowel preparation) and operator-related (i.e., expertise, and fatigue) factors are related to these missing lesions.

During the rapid-growing technological era, new tools were launched to improve the quality and performance of colonoscopies. Through the assistance of artificial intelligence (AI) an identification of a pattern can be achieved after a previous training from a large dataset of images. The DiscoveryTM AI-assisted polyp detector (Pentax Medical, Hoya Group, Tokyo, Japan), is a computer-assisted polyp/adenoma detection system based on AI. It detects classic adenomas and flat lesions, distinguished features like mucus cap or rim of debris with the advantage of a real-time and simultaneous multiple polyp detection. It was developed to minimize the missed lesions increasing as a result the polyp detection rate (PDR) and the adenoma detection rate (ADR).

Lately, published data evaluating the AI-assisted polyp detectors has demonstrate high sensitivity, specificity, and interobserver agreement. Due to the importance of CRC diagnosis and prompt treatment, and taking advantage of the newly introduced DiscoveryTM AI system, the investigators aim to assess the real-world effectiveness of this AI-assisted polyp detector system in clinical practice and compare the results between expert (seniors) and non-expert (juniors) endoscopists.

Eligibility criteria: Inclusion Criteria:

* Adults ≥45 years old
* Patients referred for screening colonoscopy
* Adequate bowel preparation, Boston Bowel Preparation Scale (BBPS) ≥8
* Patients who authorized for endoscopic approach.

Exclusion Criteria:

* Pregnancy
* Any clinical condition which makes endoscopy inviable.
* Patients with history of Colorectal Carcinoma.
* Patients with history of Inflammatory Bowel Disease (IBD)
* Inability to provide informed consent

Primary outcomes:
measure: Adenoma detection rate (ADR)
timeFrame: up to one month
measure: Polyp detection rate (PDR)
timeFrame: up to two hours
measure: Diagnostic performance of AI-assisted polyp detector
timeFrame: up to three years
Secondary outcomes:
measure: Adenoma Miss Rate (AMR)
timeFrame: Up to one month
Other outcomes:
Locations:
city: Guayaquil
country: Ecuador
facility: Instituto Ecuatoriano de Enfermedades Digestivas (IECED)
state: Guayas
Study officials:
affiliation: Instituto Ecuatoriano de Enfermedades Digestivas (IECED)
name: Carlos Robles-Medranda, MD FASGE
role: PRINCIPAL_INVESTIGATOR
References:
citation: Wang P, Berzin TM, Glissen Brown JR, Bharadwaj S, Becq A, Xiao X, Liu P, Li L, Song Y, Zhang D, Li Y, Xu G, Tu M, Liu X. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study. Gut. 2019 Oct;68(10):1813-1819. doi: 10.1136/gutjnl-2018-317500. Epub 2019 Feb 27.
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citation: Parsa N, Byrne MF. Artificial intelligence for identification and characterization of colonic polyps. Ther Adv Gastrointest Endosc. 2021 Jun 29;14:26317745211014698. doi: 10.1177/26317745211014698. eCollection 2021 Jan-Dec.
citation: Gong D, Wu L, Zhang J, Mu G, Shen L, Liu J, Wang Z, Zhou W, An P, Huang X, Jiang X, Li Y, Wan X, Hu S, Chen Y, Hu X, Xu Y, Zhu X, Li S, Yao L, He X, Chen D, Huang L, Wei X, Wang X, Yu H. Detection of colorectal adenomas with a real-time computer-aided system (ENDOANGEL): a randomised controlled study. Lancet Gastroenterol Hepatol. 2020 Apr;5(4):352-361. doi: 10.1016/S2468-1253(19)30413-3. Epub 2020 Jan 22.