Ignite Creation Date:
2025-12-25 @ 12:14 AM
Ignite Modification Date:
2026-01-03 @ 1:47 PM
Study NCT ID:
NCT06481358
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2024-07-01
First Post:
2024-06-25
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Deep Learning-based Artificial Intelligence for the Diagnosis of Small Bowel Obstruction
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D007415', 'term': 'Intestinal Obstruction'}], 'ancestors': [{'id': 'D007410', 'term': 'Intestinal Diseases'}, {'id': 'D005767', 'term': 'Gastrointestinal Diseases'}, {'id': 'D004066', 'term': 'Digestive System Diseases'}]}, 'interventionBrowseModule': {'meshes': [{'id': 'D001185', 'term': 'Artificial Intelligence'}], 'ancestors': [{'id': 'D000465', 'term': 'Algorithms'}, {'id': 'D055641', 'term': 'Mathematical Concepts'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'OTHER', 'observationalModel': 'OTHER'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 17}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'ACTIVE_NOT_RECRUITING', 'startDateStruct': {'date': '2022-09-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-06', 'completionDateStruct': {'date': '2024-10-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2024-06-25', 'studyFirstSubmitDate': '2024-06-25', 'studyFirstSubmitQcDate': '2024-06-25', 'lastUpdatePostDateStruct': {'date': '2024-07-01', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-07-01', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2023-06-06', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'The diagnosis of the obstruction site', 'timeFrame': 'September, 2024', 'description': 'Accuracy of diagnosis of the obstruction site'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Bowel Obstruction', 'Artificial Intelligence']}, 'descriptionModule': {'briefSummary': 'The study will compare the diagnostic accuracy and time to diagnosis of computed tomography images of patients with suspected intestinal obstruction seen in the emergency room by residents and surgeons, with and without artificial intelligence.', 'detailedDescription': 'DESIGN: This is an diagnostic study. SETTING: We developed a deep learning-based AI technology to automatically extract the intestinal tract from CT images using 5 200 CT images of 158 patients. The CT images of patients who visited the emergency department and were suspected of small bowel obstruction between June 6 and July 26, 2018, were obtained from two tertiary referral centers, which were used as the test samples. Data analysis was completed in December 2023.\n\nPARTICIPANTS: Residents and surgeons participated in the study. INTERVENTIONS: Residents and surgeons were divided into two groups: one group read using the AI technology, and the other group read without the AI technology.\n\nMAIN OUTCOMES AND MEASURES: Participants indicated whether or not small bowel obstruction and obstruction location. The time for diagnosis was also collected. We applied a hierarchical Bayesian model.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD', 'ADULT', 'OLDER_ADULT'], 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': '20 people', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Persons with documented consent\n\nExclusion Criteria:\n\n* Persons without documented consent'}, 'identificationModule': {'nctId': 'NCT06481358', 'briefTitle': 'Deep Learning-based Artificial Intelligence for the Diagnosis of Small Bowel Obstruction', 'organization': {'class': 'OTHER', 'fullName': 'Nagoya University'}, 'officialTitle': 'Study Using Deep Learning-based Artificial Intelligence for the Diagnosis of Small Bowel Obstruction', 'orgStudyIdInfo': {'id': '2022-0188'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'AI group', 'description': 'Participants read CT images with AI.', 'interventionNames': ['Diagnostic Test: Artificial intelligence']}, {'label': 'Manual group', 'description': 'Participants read CT images without AI'}], 'interventions': [{'name': 'Artificial intelligence', 'type': 'DIAGNOSTIC_TEST', 'description': 'AI extract intestinal region and reconstruct into 3D image.', 'armGroupLabels': ['AI group']}]}, 'contactsLocationsModule': {'locations': [{'zip': '4668560', 'city': 'Nagoya', 'state': 'Aichi-ken', 'country': 'Japan', 'facility': 'Nagoya University Graduate School of Medicine', 'geoPoint': {'lat': 35.18147, 'lon': 136.90641}}], 'overallOfficials': [{'name': 'Hieoo Uchida, PhD.', 'role': 'STUDY_CHAIR', 'affiliation': 'Nagoya University Graduate School of Medicine, Pediatric Surgery'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Nagoya University', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Medical Staff', 'investigatorFullName': 'Aitaro Takimoto', 'investigatorAffiliation': 'Nagoya University'}}}}