Ignite Creation Date:
2026-03-26 @ 3:18 PM
Ignite Modification Date:
2026-03-31 @ 1:55 PM
Study NCT ID:
NCT07326358
Status:
RECRUITING
Last Update Posted:
2026-01-08
First Post:
2025-12-26
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
AI System for Anatomic Recognition & Lesion Detection in Nasopharyngolaryngoscopy: A Prospective Study
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2026-03-25'}, 'conditionBrowseModule': {'meshes': [{'id': 'D009303', 'term': 'Nasopharyngeal Neoplasms'}, {'id': 'D007818', 'term': 'Laryngeal Diseases'}], 'ancestors': [{'id': 'D010610', 'term': 'Pharyngeal Neoplasms'}, {'id': 'D010039', 'term': 'Otorhinolaryngologic Neoplasms'}, {'id': 'D006258', 'term': 'Head and Neck Neoplasms'}, {'id': 'D009371', 'term': 'Neoplasms by Site'}, {'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D009302', 'term': 'Nasopharyngeal Diseases'}, {'id': 'D010608', 'term': 'Pharyngeal Diseases'}, {'id': 'D009057', 'term': 'Stomatognathic Diseases'}, {'id': 'D010038', 'term': 'Otorhinolaryngologic Diseases'}, {'id': 'D012140', 'term': 'Respiratory Tract Diseases'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'OTHER', 'observationalModel': 'OTHER'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 500}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2025-12-12', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-12', 'completionDateStruct': {'date': '2027-03-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-12-26', 'studyFirstSubmitDate': '2025-12-26', 'studyFirstSubmitQcDate': '2025-12-26', 'lastUpdatePostDateStruct': {'date': '2026-01-08', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2026-01-08', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-12-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'performance of lesion detection', 'timeFrame': 'Within 3 months after the completion of prospective data collection', 'description': 'The area under the receiver operating characteristic curve (ROC-AUC) of the model for abnormal lesion detection'}, {'measure': 'performance of anatomic site recognition', 'timeFrame': 'Within 3 months after the completion of prospective data collection', 'description': 'The average precision (AP) of the model for recognizing nasopharyngeal and laryngeal anatomic sites'}], 'secondaryOutcomes': [{'measure': 'Comparison of diagnostic performance between the model and physicians', 'timeFrame': 'Within 3 months after the completion of prospective data collection', 'description': 'Differences in sensitivity, specificity, and overall accuracy between the AI model and endoscopists with different years of experience'}]}, 'oversightModule': {'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Nasopharyngeal Neoplasms', 'Laryngeal Disease']}, 'descriptionModule': {'briefSummary': 'An artificial intelligence-assisted system is trained and validated by collecting nasopharyngolaryngoscopy images from patients.', 'detailedDescription': 'To address the clinical pain points of traditional nasopharyngolaryngoscopy, such as incomplete visualization, inaccurate identification, and unclear imaging, this study will retrospectively collect nasopharyngolaryngoscopy images and baseline information (including gender and age) of patients who underwent nasopharyngolaryngoscopy at participating centers for model training and validation. Deep learning algorithms will be applied to construct the model. The final clinical performance evaluation of the model will be conducted using an independent, prospectively collected test cohort.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': 'Image data of patients who underwent nasopharyngolaryngoscopy and met the research requirements were collected from various sub-centers nationwide.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Age ≥ 18 years;\n* Underwent standard electronic nasopharyngolaryngoscopy;\n* Patients who underwent biopsy sampling have a clear pathological diagnosis;\n* Signed a written informed consent form.\n\nExclusion Criteria:\n\n* Image quality is substandard with severe motion artifacts;\n* Lesion images are unclear and incomplete.'}, 'identificationModule': {'nctId': 'NCT07326358', 'briefTitle': 'AI System for Anatomic Recognition & Lesion Detection in Nasopharyngolaryngoscopy: A Prospective Study', 'organization': {'class': 'OTHER', 'fullName': 'Ruijin Hospital'}, 'officialTitle': 'Development and Validation of an Artificial Intelligence System for Anatomic Site Recognition and Lesion Detection Based on Electronic Nasopharyngolaryngoscopic Images: A Prospective Multicenter Study', 'orgStudyIdInfo': {'id': '2025-811'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Model training and validation cohorts', 'description': 'A deep learning model is trained using the training dataset and validated with the internal validation set.', 'interventionNames': ['Other: Diagnostic']}, {'label': 'Prospective test cohort', 'description': 'Patients are prospectively enrolled, nasopharyngolaryngoscopy examination videos are collected, and the video data are processed to form a prospective test dataset, which is then used for testing.', 'interventionNames': ['Other: Diagnostic']}], 'interventions': [{'name': 'Diagnostic', 'type': 'OTHER', 'description': 'The deep learning model is trained using the training dataset and tested with the internal validation set.', 'armGroupLabels': ['Model training and validation cohorts']}, {'name': 'Diagnostic', 'type': 'OTHER', 'description': 'The prospective dataset is used for the comparative testing of the model and physicians.', 'armGroupLabels': ['Prospective test cohort']}]}, 'contactsLocationsModule': {'locations': [{'city': 'Shanghai', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Bin Ye, MD PhD', 'role': 'CONTACT', 'email': 'aydyebin@126.com', 'phone': '+8615216616895'}], 'facility': 'Ruijin Hospital, Shanghai Jiao Tong University School of Medicine', 'geoPoint': {'lat': 31.22222, 'lon': 121.45806}}], 'centralContacts': [{'name': 'Bin Ye, MD PhD', 'role': 'CONTACT', 'email': 'aydyebin@126.com', 'phone': '+8615216616895'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'UNDECIDED'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Ruijin Hospital', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}