Ignite Creation Date:
2025-12-25 @ 2:20 AM
Ignite Modification Date:
2025-12-27 @ 11:26 PM
Study NCT ID:
NCT04859634
Status:
UNKNOWN
Last Update Posted:
2021-04-26
First Post:
2021-04-15
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Real-time Artificial Intelligence System for Detecting Multiple Ocular Fundus Lesions by Ultra-widefield Fundus Imaging
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 2000}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'UNKNOWN', 'lastKnownStatus': 'RECRUITING', 'startDateStruct': {'date': '2020-11-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2021-04', 'completionDateStruct': {'date': '2022-12-25', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2021-04-23', 'studyFirstSubmitDate': '2021-04-15', 'studyFirstSubmitQcDate': '2021-04-23', 'lastUpdatePostDateStruct': {'date': '2021-04-26', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2021-04-26', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2022-02-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Accuracy', 'timeFrame': '8 months', 'description': 'Performance of artificial intelligence system for detecting multiple ocular fundus lesions based on ultra-widefield fundus imaging.'}], 'secondaryOutcomes': [{'measure': 'Sensitivity', 'timeFrame': '8 months', 'description': 'Performance of artificial intelligence system for detecting multiple ocular fundus lesions based on ultra-widefield fundus imaging.'}, {'measure': 'Specificity', 'timeFrame': '8 months', 'description': 'Performance of artificial intelligence system for detecting multiple ocular fundus lesions based on ultra-widefield fundus imaging.'}, {'measure': "Cohen's kappa coefficient", 'timeFrame': '8 months', 'description': 'The comparison between the performacne of AI system and ophthalmologists of three degrees of expertise.'}, {'measure': 'False-positive rate', 'timeFrame': '8 months', 'description': 'Features of Misclassification'}, {'measure': 'False-negative rate', 'timeFrame': '8 months', 'description': 'Features of Misclassification'}, {'measure': 'Data processing time of AI system', 'timeFrame': '8 months', 'description': 'Data processing time of AI system.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Artificial Intelligence', 'Deep learning', 'Ultra-widefield Fundus Imaging', 'Ocular Fundus Lesions', 'Diagnostic Screening Programs'], 'conditions': ['Artificial Intelligence', 'Diagnostic Imaging', 'Abnormality of the Fundus', 'Diagnostic Screening Programs']}, 'descriptionModule': {'briefSummary': 'This prospective multicenter study will evaluate the efficacy of a real-time artificial intelligence system for detecting multiple ocular fundus lesions by ultra-widefield fundus imaging in real-world settings.', 'detailedDescription': 'The ocular fundus can show signs of both ocular diseases (e.g., lattice degeneration, retinal detachment and glaucoma) and systemic diseases (e.g., hypertension, diabetes and leukemia). The routine fundus examination is conducive for early detection of these diseases. However, manual conducting fundus examination needs an experienced retina ophthalmologist, and is time-consuming and labor-intensive, which is difficult for its routine implementation on large scale.\n\nThis study will develop an artificial intelligence system integrating with ultra-widefield fundus imaging to automatically screen for multiple ocular fundus lesions in real time and evaluate its performance in different real-world settings. The efficacy of the system will compare to the final diagnoses of each participant made by experienced ophthalmologists.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD', 'ADULT', 'OLDER_ADULT'], 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'All the participants who agree to take ultra-widefield fundus images.', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\nAll the participants who agree to take ultra-widefield fundus images.\n\nExclusion Criteria:\n\n1. Patients who cannot cooperate with a photographer such as some paralytics, the patients with dementia and severe psychopaths.\n2. Patients who do not agree to sign informed consent.'}, 'identificationModule': {'nctId': 'NCT04859634', 'briefTitle': 'Real-time Artificial Intelligence System for Detecting Multiple Ocular Fundus Lesions by Ultra-widefield Fundus Imaging', 'organization': {'class': 'OTHER', 'fullName': 'Sun Yat-sen University'}, 'officialTitle': 'Real-time Artificial Intelligence System for Detecting Multiple Ocular Fundus Lesions by Ultra-widefield Fundus Imaging: A Prospective Multicenter Study', 'orgStudyIdInfo': {'id': 'UWFAIDS2019-China-06'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Zhongshan Ophthalmic Center', 'description': 'The participant only needs to take an ultra-widefield fundus image as usual.', 'interventionNames': ['Device: Taking an ultra-widefield fundus image']}, {'label': 'Shenzhen Ophthalmic Center', 'description': 'The participant only needs to take an ultra-widefield fundus image as usual.', 'interventionNames': ['Device: Taking an ultra-widefield fundus image']}, {'label': 'Beijin Tongren Hospital', 'description': 'The participant only needs to take an ultra-widefield fundus image as usual.', 'interventionNames': ['Device: Taking an ultra-widefield fundus image']}, {'label': 'Xudong Ophthalmic Center', 'description': 'The participant only needs to take an ultra-widefield fundus image as usual.', 'interventionNames': ['Device: Taking an ultra-widefield fundus image']}, {'label': 'IKang Physical Examination Center', 'description': 'The participant only needs to take an ultra-widefield fundus image as usual.', 'interventionNames': ['Device: Taking an ultra-widefield fundus image']}, {'label': "Yangxi General Hospital People's Hospital", 'description': 'The participant only needs to take an ultra-widefield fundus image as usual.', 'interventionNames': ['Device: Taking an ultra-widefield fundus image']}, {'label': "Guangdong Provincial People's Hospital", 'description': 'The participant only needs to take an ultra-widefield fundus image as usual.', 'interventionNames': ['Device: Taking an ultra-widefield fundus image']}], 'interventions': [{'name': 'Taking an ultra-widefield fundus image', 'type': 'DEVICE', 'description': 'The participant only needs to take an ultra-widefield fundus image as usual.', 'armGroupLabels': ['Beijin Tongren Hospital', "Guangdong Provincial People's Hospital", 'IKang Physical Examination Center', 'Shenzhen Ophthalmic Center', 'Xudong Ophthalmic Center', "Yangxi General Hospital People's Hospital", 'Zhongshan Ophthalmic Center']}]}, 'contactsLocationsModule': {'locations': [{'zip': '510060', 'city': 'Guangzhou', 'state': 'Guangdong', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Haotian Lin, M.D., Ph.D', 'role': 'CONTACT', 'email': 'haot.lin@hotmail.com', 'phone': '8613802793086'}, {'name': 'Xiaohang Wu, M.D., Ph.D', 'role': 'CONTACT', 'email': 'wuxiaohang_zoc@qq.com', 'phone': '8615913177657'}], 'facility': 'Zhongshan Ophthalmic Center, Sun Yat-sen University', 'geoPoint': {'lat': 23.11667, 'lon': 113.25}}], 'centralContacts': [{'name': 'Haotian Lin, MD, PhD', 'role': 'CONTACT', 'email': 'haot.lin@hotmail.com', 'phone': '8613802793086'}, {'name': 'Zhongwen Li, MD', 'role': 'CONTACT', 'email': 'cuitx3@mail2.sysu.edu.cn', 'phone': '8618138726682'}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Sun Yat-sen University', 'class': 'OTHER'}, 'collaborators': [{'name': 'Shenzhen Eye Hospital', 'class': 'OTHER'}, {'name': 'Xudong Ophthalmic Hospital', 'class': 'UNKNOWN'}, {'name': 'IKang Physical Examination Center', 'class': 'UNKNOWN'}, {'name': 'Beijing Tongren Hospital', 'class': 'OTHER'}, {'name': "Guangdong Provincial People's Hospital", 'class': 'OTHER'}, {'name': "Yangxi General Hospital People's Hospital", 'class': 'UNKNOWN'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Professor', 'investigatorFullName': 'Haotian Lin', 'investigatorAffiliation': 'Sun Yat-sen University'}}}}