Ignite Creation Date:
2025-12-25 @ 4:49 AM
Ignite Modification Date:
2025-12-26 @ 3:50 AM
Study NCT ID:
NCT04335318
Status:
COMPLETED
Last Update Posted:
2021-04-08
First Post:
2020-04-02
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Real Life AI in Polyp Detection
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D003111', 'term': 'Colonic Polyps'}], 'ancestors': [{'id': 'D007417', 'term': 'Intestinal Polyps'}, {'id': 'D011127', 'term': 'Polyps'}, {'id': 'D020763', 'term': 'Pathological Conditions, Anatomical'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'NON_RANDOMIZED', 'maskingInfo': {'masking': 'NONE'}, 'primaryPurpose': 'DIAGNOSTIC', 'interventionModel': 'PARALLEL'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 230}}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2020-05-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2021-04', 'completionDateStruct': {'date': '2020-10-01', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2021-04-07', 'studyFirstSubmitDate': '2020-04-02', 'studyFirstSubmitQcDate': '2020-04-02', 'lastUpdatePostDateStruct': {'date': '2021-04-08', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2020-04-06', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2020-08-31', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Polyp detection rate comparison', 'timeFrame': '45 minutes', 'description': 'Number of polyps detected divided by number of colonoscopies'}, {'measure': 'Mean withdrawal time comparison', 'timeFrame': '45 minutes', 'description': 'Mean withdrawal time comparison'}], 'secondaryOutcomes': [{'measure': 'AI-Polyp bounding boxes - True Positive Evaluation', 'timeFrame': '45 minutes', 'description': '2 approaches: frame by frame analysis and temporal coherence analysis'}, {'measure': 'AI-Polyp bounding boxes - False Positive Quantitative Evaluation', 'timeFrame': '45 minutes', 'description': '3 approaches depending on window-time detection'}, {'measure': 'AI-Polyp bounding boxes - False Negative Evaluation', 'timeFrame': '45 minutes', 'description': 'Number of by bounding box missed polyps'}, {'measure': 'Reaction Time Analysis', 'timeFrame': '45 minutes', 'description': 'Comparison time of polyp detection in a human vs machine approach'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Deep Learning', 'Polyp detection rate', 'CNN'], 'conditions': ['Colonic Polyp']}, 'descriptionModule': {'briefSummary': 'The objective of this study is to compare the polyp detection rate (PDR) of endoscopists unaware of a commercially available artificial intelligence (AI) device for polyp detection during colonoscopy and the PDR of endoscopists with the aid of such a device. Moreover, an extensive characterization of the performance of this device will be done.', 'detailedDescription': 'Recently, there have been remarkable breakthroughs in the introduction of deep learning techniques, especially convolutional neural networks (CNNs), in assisting clinical diagnosis in different medical fields. One of these artificial intelligence (AI) devices to diagnose colon polyps during colonoscopy was launched in October 2019. Its intended use is to work as an adjunct to the endoscopist during a colonoscopy with the purpose of highlighting regions with visual characteristics consistent with different types of mucosal abnormalities.\n\nIt is essential to know whether deep learning algorithms can really help endoscopists during colonoscopies. Several studies have already addressed this issue with different approaches and results. However, one common drawback of these type of Machine vs Human retrospective studies is endoscopist bias. It is usually generated because of human natural competitive spirit against machine or human relaxation because of AI-reliance. This can have an effect in the overall results.\n\nThe investigators perfomed colonoscopies with the use of a commercially available AI system to detect colonic polyps and recorded them during clinical routine. Additionally from March 2019 - May 2019, 120 colonoscopy videos were performed and captured prospectively without the use of AI.\n\nIn this study, the investigators plan to retrospectively compare those two video sets regarding the polyp detection rate, withdrawal time and polyp identification characteristics of the AI system.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Colonoscopies for Polyp detection\n\nExclusion Criteria:\n\n* Colonoscopies for Inflammatory Bowel Disease (IBD).\n* Colonoscopies for work up of an active bleeding'}, 'identificationModule': {'nctId': 'NCT04335318', 'acronym': 'RELIANT', 'briefTitle': 'Real Life AI in Polyp Detection', 'organization': {'class': 'OTHER', 'fullName': 'Wuerzburg University Hospital'}, 'officialTitle': 'Real Life AI in Polyp Detection', 'orgStudyIdInfo': {'id': 'AI01'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'Colonoscopy with AI-assistance group', 'description': 'Colonoscopies were performed with AI-assistance.', 'interventionNames': ['Device: AI-assisted colonoscopy']}, {'type': 'NO_INTERVENTION', 'label': 'Standard Colonoscopy group', 'description': 'Standard clinical procedure'}], 'interventions': [{'name': 'AI-assisted colonoscopy', 'type': 'DEVICE', 'description': 'Colonoscopies performed with assistance of an AI tool that highlights the areas that are susceptible to be a polyp.', 'armGroupLabels': ['Colonoscopy with AI-assistance group']}]}, 'contactsLocationsModule': {'locations': [{'zip': '97080', 'city': 'Würzburg', 'state': 'Bavaria', 'country': 'Germany', 'facility': 'Universitätsklinikum Würzburg', 'geoPoint': {'lat': 49.79391, 'lon': 9.95121}}], 'overallOfficials': [{'name': 'Alexander Hann, PD Dr. Med', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Wuerzburg University Hospital'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Wuerzburg University Hospital', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Principal Investigator', 'investigatorFullName': 'Alexander Hann', 'investigatorAffiliation': 'Wuerzburg University Hospital'}}}}