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Recently, a CNN-based artificial intelligence (AI) system for polyp characterization has been developed by Fujifilm Co., Tokyo, Japan. It works in conjunction with BLI system. In the present study we prospectively evaluate whether the evaluation of the endoscopist combined with the CAD system output achieve > 90% accuracy in characterization (i.e. as adenomas or non-adenomas) of diminutive rectosigmoid polyps having histopathology as reference standard. Consecutive adult outpatients undergoing elective colonoscopy, in which at least one diminutive (<5 mm) rectosigmoid polyp is detected are included. During endoscopic procedures all polyps identified by the endoscopist are documented for size, location and morphology. All diminutive polyps are characterized by a three sequential steps process: I) endoscopist prediction: the endoscopist evaluates the polyp by using BLI through the BASIC classification; the confidence level (high vs. low) in histology prediction is recorded; II) AI prediction: the AI system is switched on and the output of the automatic evaluation is recorded; this outcome is rated as stable or unstable, depending of the consistency over time of the outcome; III) combined prediction: a final classification is provided by endoscopist in light of the results of the first and of the second step; the confidence level is recorded. All polyps are resected and retrieved in separate jars and sent for pathology assessment. Only polyps characterized with high confidence will be included in the per-polyp analysis; the high-confidence characterization rate will be also calculated; the rate of polyps characterized with a CAD stable outcome will be calculated. Operative characteristics (sensitivity, specificity, positive and negative predictive value and accuracy) in distinguishing adenomatous from non-adenomatous polyps, evaluated with high confidence, will be calculated for each diminutive polyp and for each diminutive rectosigmoid polyp, having histopathology report as reference standard. The post-polypectomy surveillance intervals will be calculated on the basis of polyp histology (reference standard) in all patients according to both USMSTF and ESGE guidelines.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Patients with at least one diminutive rectosigmoid polyp | Consecutive adult (>18 years) outpatients undergoing elective colonoscopy, in which at least one diminutive (<5 mm) rectosigmoid polyp is detected. Exclusion criteria:
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Polyp carachterization by combing endoscopist evaluation and Ai output | Diagnostic Test | A polyp characterization (adenoma vs. non adenoma) is provided by endoscopist in light of the results of this own evaluation and of the Ai system output. The confidence level (high vs. low) in polyp characterization is recorded. The combined evaluation is compared with histopathology results. |
| Measure | Description | Time Frame |
|---|---|---|
| Agreement of combined prediction with PIVI I statement | To prospectively evaluate whether the evaluation of the endoscopist combined with the CAD system output achieve > 90% accuracy in characterization (i.e. as adenomas or non-adenomas) of diminutive rectosigmoid polyps (i.e. PIVI I threshold) having histopathology as reference standard. | 6 months |
| Measure | Description | Time Frame |
|---|---|---|
| Endoscopist prediction | to calculate the performance measures (sensitivity, specificity, positive and negative predictive value) of the endoscopist alone in characterizing diminutive rectosigmoid polyps | 6 months |
| Ai prediction |
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Inclusion Criteria:
Exclusion Criteria:
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Consecutive adult outpatients undergoing elective colonoscopy, in which at least one diminutive (<5 mm) rectosigmoid polyp is detected
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Gastroenterology Unit, Valduce Hospital | Como | 22100 | Italy |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 35562098 | Derived | Rondonotti E, Hassan C, Tamanini G, Antonelli G, Andrisani G, Leonetti G, Paggi S, Amato A, Scardino G, Di Paolo D, Mandelli G, Lenoci N, Terreni N, Andrealli A, Maselli R, Spadaccini M, Galtieri PA, Correale L, Repici A, Di Matteo FM, Ambrosiani L, Filippi E, Sharma P, Radaelli F. Artificial intelligence-assisted optical diagnosis for the resect-and-discard strategy in clinical practice: the Artificial intelligence BLI Characterization (ABC) study. Endoscopy. 2023 Jan;55(1):14-22. doi: 10.1055/a-1852-0330. Epub 2022 May 13. |
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- to calculate the performance measures (sensitivity, specificity, positive and negative predictive value) of the AI system alone in characterizing diminutive rectosigmoid polyps
| 6 months |
| Agreement of combined prediction with PIVI II statement | - to evaluate if the evaluation of the endoscopist combined with the CAD system output achieve > 90% accuracy in the assignment of post-polypectomy surveillance intervals, according to US and EU guidelines, when combined with the histopathology assessment of polyps >5 mm in size | 6 months |