Ignite Creation Date:
2025-12-25 @ 1:24 AM
Ignite Modification Date:
2025-12-25 @ 11:34 PM
Study NCT ID:
NCT07152093
Status:
COMPLETED
Last Update Posted:
2025-11-18
First Post:
2025-08-21
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Development of an Artificial Intelligence-Based Model for Predicting Difficult Intubation Using Video Laryngoscopic Images and Cormack-Lehane Classification
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 132}, 'targetDuration': '1 Day', 'patientRegistry': True}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2025-05-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-11', 'completionDateStruct': {'date': '2025-10-01', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2025-11-17', 'studyFirstSubmitDate': '2025-08-21', 'studyFirstSubmitQcDate': '2025-08-28', 'lastUpdatePostDateStruct': {'date': '2025-11-18', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2025-09-03', 'type': 'ESTIMATED'}, 'primaryCompletionDateStruct': {'date': '2025-09-30', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Accuracy of Machine Learning Model in Predicting Difficult Intubation Based on Video Laryngoscopy Images', 'timeFrame': 'Immediately after data collection and model training', 'description': "The primary outcome is the classification accuracy of the machine learning algorithm in identifying difficult intubation cases (Cormack-Lehane grade 3-4) from video laryngoscopy images, compared with expert anesthesiologists' consensus. Accuracy will be reported as a percentage."}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Difficult Airway', 'Cormack-Lehane', 'Video Laryngoscopy', 'Artificial Intelligence', 'Machine Learning'], 'conditions': ['Difficult Airway Intubation']}, 'descriptionModule': {'briefSummary': 'This prospective observational study aims to develop an artificial intelligence model that can automatically determine the Cormack-Lehane classification from video laryngoscopy images in patients undergoing elective surgery. It also aims to predict the risk of difficult intubation based on this classification. The resulting data will evaluate the applicability of AI-supported decision support systems in clinical airway management.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '65 Years', 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'The study population will consist of adult patients undergoing elective surgery under general anesthesia at the operating rooms of Düzce University Medical Faculty Hospital. All patients will have their airways assessed using video laryngoscopy as part of routine anesthesia induction. Only patients without known upper airway pathology will be included.\n\nPatients will be prospectively and consecutively recruited. Video laryngoscopy images will be captured during intubation and used for machine learning analysis. The Cormack-Lehane grade will be independently confirmed by two experienced anesthesiologists. Patients will be classified into normal and difficult intubation groups.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* 18-65 years\n\nElective surgery\n\nASA I-II\n\nNo upper airway pathology\n\nExclusion Criteria:\n\n* Known history of difficult intubation\n\nMorbid obesity (BMI \\> 40)\n\nPregnancy\n\nHistory of upper airway surgery'}, 'identificationModule': {'nctId': 'NCT07152093', 'briefTitle': 'Development of an Artificial Intelligence-Based Model for Predicting Difficult Intubation Using Video Laryngoscopic Images and Cormack-Lehane Classification', 'organization': {'class': 'OTHER', 'fullName': 'Duzce University'}, 'officialTitle': 'Development of an Artificial Intelligence-Based Model for Predicting Difficult Intubation Using Video Laryngoscopic Images and Cormack-Lehane Classification', 'orgStudyIdInfo': {'id': '2025/183'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Group 1: Normal Intubation Group', 'description': 'Intubations in patients assessed as Cormack-Lehane (CL) Class 1-2.'}, {'label': 'Difficult Intubation Group', 'description': 'Intubations in patients evaluated as Cormack-Lehane Class 3-4.'}]}, 'contactsLocationsModule': {'locations': [{'city': 'Düzce', 'state': 'Merkez', 'country': 'Turkey (Türkiye)', 'facility': 'Duzce University Faculty of Medicine, Department of Anesthesiology and Reanimation', 'geoPoint': {'lat': 40.83889, 'lon': 31.16389}}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Duzce University', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Principal Investigator, Assistant Professor of Anesthesiology and Reanimation, Düzce University Faculty of Medicine', 'investigatorFullName': 'Gizem Demir Şenoğlu', 'investigatorAffiliation': 'Duzce University'}}}}