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| Name | Class |
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| Google LLC. | INDUSTRY |
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Colonoscopy is the gold standard for detection and removal of precancerous lesions, and has been amply shown to reduce mortality. However, the miss rate for polyps during colonoscopies is 22-28%, while 20-24% of the missed lesions are histologically confirmed precancerous adenomas. To address this shortcoming, the investigators propose a new polyp detection system based on deep learning, which can alert the operator in real-time to the presence and location of polyps during a colonoscopy. The investigators dub the system DEEP: (DEEP) DEtection of Elusive Polyps. The DEEP system was trained on 3,611 hours of colonoscopy videos derived from two sources, and was validated on a set comprising 1,393 hours of video, coming from a third, unrelated source. For the validation set, the ground truth labelling was provided by offline gastroenterologist annotators, who were able to watch the video in slow-motion and pause/rewind as required; two or three specialist annotators examined each video.
This is a prospective, non-blinded, non-randomized pilot study of patients undergoing elective screening and surveillance colonoscopies using DEEP.
The aim of the study is to:
Assess the:
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Intervention Arm | Experimental | Consecutive patients undergoing screening or surveillance colonoscopy in whom a new polyp detection system based on deep learning will be used during the procedure. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| AI polyp detection system based on deep learning | Device | A Polyp detection system based on deep learning and artificial intelligence, which can alert the operator in real-time to the presence and location of polyps during a colonoscopy. |
| Measure | Description | Time Frame |
|---|---|---|
| Number of Additional Polyps Detected by the DEEP System in Real Time Colonoscopy | During the colonoscopy procedure, in real time when a polyp is found, the colonoscopist will rate the polyp as an elusive polyp detected by the system that might have been missed or a polyp that would have been detected with or without the system. The outcome measure will be reported as the average of additional polyps detected per colonoscopy by the DEEP system | Through study completion, an average of 12 months |
| The Rate of Adverse Events During the Study Attributed or Not to the Use of the DEEP System | Prospective assessment adverse events during the study. The following adverse event will be monitored: Perforation, bleeding, and cardiorespiratory adverse events during the procedure | Until discharge, assessed up to 7 days |
| Measure | Description | Time Frame |
|---|---|---|
| Rate of False Positives (False Alarms) Per Colonoscopy | During the colonoscopy procedure, in real time after each polyp found by the DEEP system, the colonoscopist will rate the polyp as either a true polyp or a false positive detection or a "false alarm" this measure will be reported as the average of false positive detection per colonoscopy | Through study completion, an average of 12 months |
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Inclusion Criteria:
Exclusion Criteria:
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Digestive Diseases Institute, Shaare Zedek Medical Center | Jerusalem | 90301 | Israel |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 34216598 | Derived | Livovsky DM, Veikherman D, Golany T, Aides A, Dashinsky V, Rabani N, Ben Shimol D, Blau Y, Katzir L, Shimshoni I, Liu Y, Segol O, Goldin E, Corrado G, Lachter J, Matias Y, Rivlin E, Freedman D. Detection of elusive polyps using a large-scale artificial intelligence system (with videos). Gastrointest Endosc. 2021 Dec;94(6):1099-1109.e10. doi: 10.1016/j.gie.2021.06.021. Epub 2021 Jun 30. |
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Data will be shared only on request and after consent form the patient and the institutional ethics committee
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| ID | Title | Description |
|---|---|---|
| FG000 | Intervention Arm | Consecutive patients undergoing screening or surveillance colonoscopy in whom a new polyp detection system based on deep learning will be used during the procedure. AI polyp detection system based on deep learning: A Polyp detection system based on deep learning and artificial intelligence, which can alert the operator in real-time to the presence and location of polyps during a colonoscopy. |
| Title | Milestones | Reasons Not Completed | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Overall Study |
|
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| ID | Title | Description |
|---|---|---|
| BG000 | Intervention Arm | Consecutive patients undergoing screening or surveillance colonoscopy in whom a new polyp detection system based on deep learning will be used during the procedure. AI polyp detection system based on deep learning: A Polyp detection system based on deep learning and artificial intelligence, which can alert the operator in real-time to the presence and location of polyps during a colonoscopy. |
| Units | Counts |
|---|---|
| Participants |
|
| Title | Description | Population Description | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Denominator Units Selected | Denominators | Classes |
|---|---|---|---|---|---|---|---|---|---|
| Age, Continuous | Mean |
| Type | Title | Description | Population Description | Reporting Status | Anticipated Posting Date | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Time Frame | Units Analyzed | Denominator Units Selected | Arm/Group Information | Denominators | Classes | Analyses | |||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Primary | Number of Additional Polyps Detected by the DEEP System in Real Time Colonoscopy | During the colonoscopy procedure, in real time when a polyp is found, the colonoscopist will rate the polyp as an elusive polyp detected by the system that might have been missed or a polyp that would have been detected with or without the system. The outcome measure will be reported as the average of additional polyps detected per colonoscopy by the DEEP system | Posted | Mean | 95% Confidence Interval | Added polyps detected per colonoscopy | Through study completion, an average of 12 months |
|
6 months after the end of the study
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| ID | Title | Description | Deaths (Affected) | Deaths (At Risk) | Serious Events (Affected) | Serious Events (At Risk) | Other Events (Affected) | Other Events (At Risk) |
|---|---|---|---|---|---|---|---|---|
| EG000 | Intervention Arm | Consecutive patients undergoing screening or surveillance colonoscopy in whom a new polyp detection system based on deep learning will be used during the procedure. AI polyp detection system based on deep learning: A Polyp detection system based on deep learning and artificial intelligence, which can alert the operator in real-time to the presence and location of polyps during a colonoscopy. |
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| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| Dr. Dan Meir Livovsky | Shaare Zedek Medical Center | +34603833576 | danlivo@yahoo.com |
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot_SAP | Yes | Yes | No | Study Protocol and Statistical Analysis Plan | Feb 9, 2021 | Feb 9, 2021 | Prot_SAP_000.pdf |
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| ID | Term |
|---|---|
| D003111 | Colonic Polyps |
| ID | Term |
|---|---|
| D007417 | Intestinal Polyps |
| D011127 | Polyps |
| D020763 | Pathological Conditions, Anatomical |
| D013568 | Pathological Conditions, Signs and Symptoms |
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| Colonoscopist User Experience While Using the DEEP System in a 5 Point Scale | At the end of the procedures the colonoscopist will be requires to answer the question "from a scale of 1-5 how useful did you find the system in this procedure?", where higher scores represent more usefulness. This measure will be reported as the average score form all 100 procedures. | Through study completion, an average of 12 months |
| years |
|
| Sex: Female, Male | Count of Participants | Participants | No |
|
| Ethnicity (NIH/OMB) | Count of Participants | Participants |
|
| Region of Enrollment | Number | participants |
|
|
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| Primary | The Rate of Adverse Events During the Study Attributed or Not to the Use of the DEEP System | Prospective assessment adverse events during the study. The following adverse event will be monitored: Perforation, bleeding, and cardiorespiratory adverse events during the procedure | Posted | Number | Participants | Until discharge, assessed up to 7 days |
|
|
|
| Secondary | Rate of False Positives (False Alarms) Per Colonoscopy | During the colonoscopy procedure, in real time after each polyp found by the DEEP system, the colonoscopist will rate the polyp as either a true polyp or a false positive detection or a "false alarm" this measure will be reported as the average of false positive detection per colonoscopy | Posted | Mean | 95% Confidence Interval | False positive alarms per colonoscopy | Through study completion, an average of 12 months |
|
|
|
| Secondary | Colonoscopist User Experience While Using the DEEP System in a 5 Point Scale | At the end of the procedures the colonoscopist will be requires to answer the question "from a scale of 1-5 how useful did you find the system in this procedure?", where higher scores represent more usefulness. This measure will be reported as the average score form all 100 procedures. | Posted | Mean | 95% Confidence Interval | score on a scale | Through study completion, an average of 12 months |
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| 100 |
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