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Even if colonoscopy is considered the reference standard for the detection of colonic neoplasia, polyps are still missed. The risk of early post-colonoscopy cancer appeared to be independently predicted by a relatively low polyp/adenoma detection rate. When considering the very high prevalence of advanced neoplasia in the FIT-positive enriched population, the risk of post-colonoscopy interval cancer due to a suboptimal quality of colonoscopy may be substantial. Available evidence justifies therefore the implementation of efforts aimed at improving adenoma detection rate, based on retraining interventions and on the adoption of innovative technologies, designed to enhance the accuracy of the endoscopic examination. Artificial intelligence seems to improve the quality of medical diagnosis and treatment. In the field of gastrointestinal endoscopy, two potential roles of AI in colonoscopy have been examined so far: automated polyp detection (CADe) and automated polyp histology characterization (CADx). CADe can minimize the probability of missing a polyp during colonoscopy, thereby improving the adenoma detection rate (ADR) and potentially decreasing the incidence of interval cancer. GI Genius is the AI software that will be used in the present trial and is intended to be used as an adjunct to colonic endoscopy procedures to help endoscopists to detect in real time mucosal lesions (such as polyps and adenomas, including those with flat (non-polypoid) morphology) during standard screening and surveillance endoscopic mucosal evaluations. It is not intended to replace histopathological sampling as a means of diagnosis.
The objective of this study was to compare the diagnostic yield obtained by using CADe colonoscopy to the yield obtained by the standard colonoscopy (SC).
Even if colonoscopy is considered the reference standard for the detection of colonic neoplasia, polyps are still missed. In large administrative cohort or case-control studies, the risk of early post-colonoscopy cancer appeared to be independently predicted by a relatively low polyp/adenoma detection rate. The adenoma detection rate among different endoscopists has been shown to be strictly related with the risk of post-colonoscopy interval cancer. When considering the very high prevalence of advanced neoplasia in the FIT-positive enriched population, the risk of post-colonoscopy interval cancer due to a suboptimal quality of colonoscopy may be substantial. Available evidence justifies therefore the implementation of efforts aimed at improving adenoma detection rate, based on retraining interventions and on the adoption of innovative technologies, designed to enhance the accuracy of the endoscopic examination.Nowadays, Artificial intelligence (AI) is gaining increased attention and investigation, since it seems to improve the quality of medical diagnosis and treatment. In the field of gastrointestinal endoscopy, two potential roles of AI in colonoscopy have been examined so far: automated polyp detection (CADe) and automated polyp histology characterization (CADx). CADe can minimize the probability of missing a polyp during colonoscopy, thereby improving the adenoma detection rate (ADR) and potentially decreasing the incidence of interval cancer. GI Genius is the AI software that will be used in the present trial. The software is developed by Medtronic Inc. (Dublin, Ireland) and is intended to be used as an adjunct to colonic endoscopy procedures to help endoscopists to detect in real time mucosal lesions (such as polyps and adenomas, including those with flat (non-polypoid) morphology) during standard screening and surveillance endoscopic mucosal evaluations. It is not intended to replace histopathological sampling as a means of diagnosis.
The objective of this study was to compare the diagnostic yield obtained by using CADe colonoscopy to the yield obtained by the standard colonoscopy (SC). As the risk of progression is higher for large than for small adenomas the specific contribution of the new technique in reducing the miss rate of large neoplasms represents an important outcome to be assessed in the study. In addition, given the suggested association of a higher miss-rate of serrated and flat lesions with an increased risk of early post-colonoscopy CRC, the benefit of the new technique in reducing the miss rate of these lesions will be assessed.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Artificial intelligence arm | Experimental | Patients undergoing colonoscopy with artificial intelligence. |
|
| standard colonoscopy arm | Active Comparator | Patients undergoing colonoscopy with standard colonscopy |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| CADe colonoscopy using GI Genius device | Device | We wanted to compare the diagnostic yield obtained by using CADe colonoscopy to the yield obtained by the standard colonoscopy (SC) in a FIT-positive screening population. |
| Measure | Description | Time Frame |
|---|---|---|
| Rate of advanced adenomas | The percentage of patients with adenomas with high-grade displasia in CADe colonoscopy group will be recorded and compared with the rate of patients with adenomas with high-grade displasia in standard colonoscopy group. | When available the histological report of polyps removed (up to 3 weeks). |
| Rate of patients detected with 3 or more adenomas. | The percentage of patients with 3 or more adenomas (serrated adenomas will also be considered in the calculation) in CADe colonoscopy group will be compared with the rate of patients with 3 or more adenomas (including serrated adenomas) in standard colonoscopy group. | When available the histological report of polyps removed (up to 3 weeks). |
| Measure | Description | Time Frame |
|---|---|---|
| Overall adenoma and polyp detection rate, flat adenoma and serrated polyps/adenomas. | The percentage of adenomas, polyps (in general), flat adenoma and serrated polyps/adenoma detected will be recorded and compared between the groups. | When available the histological report of polyps removed (up to 3 weeks). |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Cristiano Spada, MD, PhD | Fondazione Poliambulanza Istituto Ospedaliero | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Fondazione Poliambulanza Istituto Ospedaliero | Brescia | Italy |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 41563802 | Derived | Spada C, Cesaro P, Fuccio L, Salvi D, Ferrari C, Barbaro F, Bizzotto A, Butitta F, Gerardi V, Lovera M, Milluzzo SM, Minelli Grazioli L, Olivari N, Pecere S, Piccirelli S, Pugliano CL, Pesatori EV, Quadarella A, Tettoni E, Zani C, Ricciardiello L, Costamagna G. Impact of Artificial Intelligence for Detection of Precancerous Colonic Lesions in a Fecal Immunochemical Blood Test-Based Organized Screening Program in Italy: A Randomized Control Trial. United European Gastroenterol J. 2026 Feb;14(1):e70176. doi: 10.1002/ueg2.70176. |
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| Size of lesions detected |
The size of lesion detected will be measured in millimiters and compared between the groups. |
| Immediately after the procedure. |
| Rate of neoplasia by colonic site | The percentage of patients with neoplasia of proximal (right colonic segments) or distal (left colonic segments and rectum) site will be assessed and compared between the groups. | Immediately after the procedure. |
| Post-colonoscopy surveilance | the time interval, expressed in years, to the next suggested follow-up colonoscopy will be assessed and compared between the groups. | When available the histological report of polyps removed (up to 3 weeks). |
| Time of cecal intubation. | The time to reach the cecum will be measured in minutes, recorded and compared between the groups. | Immediately after the procedure. |
| Withdrawal and total procedure time. | The time of withdrawal (from cecum to anus) and of the overall colonoscopy (from anus to anus) will be measured in minutes, recorded and compared between the groups. | Immediately after the procedure. |
| Learning curve. | The above-mentioned outcomes will be calculated for each endoscopist at 3, 6, 9 and 12 months. | 3, 6, 9 and 12 months. |
| Patient experience | Visual Analogue Scale (VAS) (0 minimum to 10 maximum) will be recorded to assess the pain before and after the colonoscopy and results will be compared between the groups. | Immediately after the procedure. |
| Specific contribution of AI | Proportion of patients diagnosed with polyps which were detected only by Artificial intelligence | Immediately after the procedure. |
| ID | Term |
|---|---|
| D003110 | Colonic Neoplasms |
| D003111 | Colonic Polyps |
| D012004 | Rectal Neoplasms |
| ID | Term |
|---|---|
| D015179 | Colorectal Neoplasms |
| D007414 | Intestinal Neoplasms |
| D005770 | Gastrointestinal Neoplasms |
| D004067 | Digestive System Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D004066 | Digestive System Diseases |
| D005767 | Gastrointestinal Diseases |
| D003108 | Colonic Diseases |
| D007410 | Intestinal Diseases |
| D007417 | Intestinal Polyps |
| D011127 | Polyps |
| D020763 | Pathological Conditions, Anatomical |
| D013568 | Pathological Conditions, Signs and Symptoms |
| D012002 | Rectal Diseases |
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