Trial Title:
Computer-aided Detection During Screening Colonoscopy
NCT ID:
NCT05734820
Condition:
Colorectal Polyp
Colorectal Cancer
Colorectal Adenoma
Conditions: Official terms:
Colorectal Neoplasms
Adenoma
Polyps
Conditions: Keywords:
Artificial intelligence
Colonoscopy
colorectal cancer
Study type:
Interventional
Study phase:
N/A
Overall status:
Recruiting
Study design:
Allocation:
Non-Randomized
Intervention model:
Crossover Assignment
Intervention model description:
Blinded, single center, controlled, prospective trial
Primary purpose:
Diagnostic
Masking:
Single (Care Provider)
Intervention:
Intervention type:
Diagnostic Test
Intervention name:
HD- colonoscopy
Description:
HD-colonoscopy performed by an expert or non-expert endoscopist. All lesions will be
recorded, assessed, and removed for histological analysis.
Arm group label:
AI-HD colonoscopy + HD-colonoscopy
Arm group label:
HD-colonoscopy + AI-HD colonoscopy
Intervention type:
Diagnostic Test
Intervention name:
HD-colonoscopy assisted by AI
Description:
HD-colonoscopy with AI-assisted polyp detector. New polyps detected by AI will be
recorded, removed, and studied.
Arm group label:
AI-HD colonoscopy + HD-colonoscopy
Arm group label:
HD-colonoscopy + AI-HD colonoscopy
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.
Criteria for 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
Gender:
All
Minimum age:
45 Years
Maximum age:
89 Years
Healthy volunteers:
No
Locations:
Facility:
Name:
Instituto Ecuatoriano de Enfermedades Digestivas (IECED)
Address:
City:
Guayaquil
Zip:
090505
Country:
Ecuador
Status:
Recruiting
Contact:
Last name:
Carlos Robles-Medranda, MD FASGE
Phone:
+59342109180
Email:
carlosoakm@yahoo.es
Investigator:
Last name:
Hannah Pitanga-Lukashok, MD
Email:
Sub-Investigator
Investigator:
Last name:
Maria Egas-Izquierdo, MD
Email:
Sub-Investigator
Investigator:
Last name:
Carlos Cifuentes-Gordillo, MD
Email:
Sub-Investigator
Investigator:
Last name:
Miguel Puga-Tejada, MD
Email:
Sub-Investigator
Investigator:
Last name:
Jorge Baquerizo-Burgos, MD
Email:
Sub-Investigator
Investigator:
Last name:
Domenica Cunto, MD
Email:
Sub-Investigator
Investigator:
Last name:
Martha Arevalo-Mora, MD
Email:
Sub-Investigator
Investigator:
Last name:
Juan Alcivar-Vasquez, MD
Email:
Sub-Investigator
Investigator:
Last name:
Raquel Del Valle, MD
Email:
Sub-Investigator
Investigator:
Last name:
Haydee Alvarado-Escobar, MD
Email:
Sub-Investigator
Investigator:
Last name:
Daniela Tabacelia, MD
Email:
Sub-Investigator
Investigator:
Last name:
Carlos Robles-Medranda, MD FASGE
Email:
Principal Investigator
Start date:
January 11, 2020
Completion date:
September 1, 2024
Lead sponsor:
Agency:
Instituto Ecuatoriano de Enfermedades Digestivas
Agency class:
Other
Source:
Instituto Ecuatoriano de Enfermedades Digestivas
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT05734820
