Ignite Creation Date:
2025-12-26 @ 10:44 PM
Ignite Modification Date:
2025-12-26 @ 10:44 PM
Study NCT ID:
NCT05223712
Status:
UNKNOWN
Last Update Posted:
2022-02-04
First Post:
2022-01-23
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Artificial Intelligence System for the Detection and Prediction of Kidney Diseases Using Ocular Information
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D007674', 'term': 'Kidney Diseases'}], 'ancestors': [{'id': 'D014570', 'term': 'Urologic Diseases'}, {'id': 'D052776', 'term': 'Female Urogenital Diseases'}, {'id': 'D005261', 'term': 'Female Urogenital Diseases and Pregnancy Complications'}, {'id': 'D000091642', 'term': 'Urogenital Diseases'}, {'id': 'D052801', 'term': 'Male Urogenital Diseases'}]}}, 'protocolSection': {'designModule': {'bioSpec': {'retention': 'SAMPLES_WITH_DNA', 'description': 'Blood, urine and renal biopsy samples from CKD patients.'}, 'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'OTHER', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 4000}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'UNKNOWN', 'lastKnownStatus': 'RECRUITING', 'startDateStruct': {'date': '2021-08-28', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2022-01', 'completionDateStruct': {'date': '2022-12', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2022-01-25', 'studyFirstSubmitDate': '2022-01-23', 'studyFirstSubmitQcDate': '2022-01-25', 'lastUpdatePostDateStruct': {'date': '2022-02-04', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2022-02-04', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2022-12', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Area under the receiver operating characteristic curve of the deep learning system', 'timeFrame': 'baseline', 'description': 'The investigators will calculate the area under the receiver operating characteristic curve of deep learning system and compare this index between deep learning system and human doctors'}], 'secondaryOutcomes': [{'measure': 'Sensitivity and specificity of the deep learning system', 'timeFrame': 'baseline', 'description': 'The investigators will calculate the sensitivity and specifity of deep learning system and compare this index between deep learning system and human doctors'}]}, 'oversightModule': {'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Kidney Diseases', 'Artificial Intelligence', 'Eye information'], 'conditions': ['Artificial Intelligence', 'Ophthalmology', 'Kidney Diseases']}, 'descriptionModule': {'briefSummary': 'This is an retrospective and prospective multicenter study to develop and validate an artificial intelligent (AI) aided diagnosis, therapeutic effect assessment model including chronic kidney disease (CKD) and dialysis patients starting from April 2009, which is based on ophthalmic examinations (e.g. retinal fundus photography, slit-lamp images, OCTA, etc.) and CKD diagnostic and therapeutic data (routine clinical evaluations and laboratory data), to provide a reliable basis and guideline for clinical diagnosis and treatment.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': 'Participants who had slit-lamp, retinal fundus photography and kidney disease tests at the Department of Nephrology, First Affiliated Hospital of Sun Yat-sen University and Medical Centre of Aikang Health Care, Guangzhou, China', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Patients previously received kidney biopsy, ophthalmic examinations and routine examinations of the department of nephrology during in-hospital period with BCVA\\>0.5.\n\nExclusion Criteria:\n\n* Patients without retinal fundus images or kidney diseases.\n* The quality of the retinal fundus images can not meet the requirement for furthur analysis.\n* Severe loss of results of routine examinations of the department of nephrology.'}, 'identificationModule': {'nctId': 'NCT05223712', 'briefTitle': 'Artificial Intelligence System for the Detection and Prediction of Kidney Diseases Using Ocular Information', 'organization': {'class': 'OTHER', 'fullName': 'Sun Yat-sen University'}, 'officialTitle': 'Artificial Intelligence System for the Detection and Prediction of Kidney Diseases Using Ocular Information', 'orgStudyIdInfo': {'id': 'AIKD-2021'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Development Dataset 01', 'description': 'Slit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Department of Nephrology of the First Affiliated Hospital of Sun Yat-sen University', 'interventionNames': ['Other: Diagnostic Test: Chronic Kidney Diseases']}, {'label': 'Development Dataset 02', 'description': 'Slit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Medical Centre of Aikang Health Care, Guangzhou, China', 'interventionNames': ['Other: Diagnostic Test: Chronic Kidney Diseases']}, {'label': 'Validation Dataset 01', 'description': 'Slit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Department of Nephrology of the First Affiliated Hospital of Sun Yat-sen University', 'interventionNames': ['Other: Diagnostic Test: Chronic Kidney Diseases']}, {'label': 'Validation Dataset 02', 'description': 'Slit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Medical Centre of Aikang Health Care, Guangzhou, China', 'interventionNames': ['Other: Diagnostic Test: Chronic Kidney Diseases']}, {'label': 'Test Dataset 01', 'description': 'Slit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Department of Nephrology of the First Affiliated Hospital of Sun Yat-sen University', 'interventionNames': ['Other: Diagnostic Test: Chronic Kidney Diseases']}, {'label': 'Test Dataset 02', 'description': 'Slit-lamp, retinal fundus images, OCTA and kidney diseases examinations collected from Medical Centre of Aikang Health Care, Guangzhou, China', 'interventionNames': ['Other: Diagnostic Test: Chronic Kidney Diseases']}], 'interventions': [{'name': 'Diagnostic Test: Chronic Kidney Diseases', 'type': 'OTHER', 'description': 'The development datasets were used to train the deep learning model, which was validated and tested by the other 4 datasets.', 'armGroupLabels': ['Development Dataset 01', 'Development Dataset 02', 'Test Dataset 01', 'Test Dataset 02', 'Validation Dataset 01', 'Validation Dataset 02']}]}, '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': 'Qianni Wu, M.D., Ph.D', 'role': 'CONTACT', 'email': 'wuqianni@gzzoc.com', 'phone': '+8615521506995'}], 'facility': 'Zhongshan Ophthalmic Center, Sun Yat-sen University', 'geoPoint': {'lat': 23.11667, 'lon': 113.25}}], 'centralContacts': [{'name': 'Haotian Lin, Ph. D', 'role': 'CONTACT', 'email': 'gddlht@aliyun.com', 'phone': '13802793086'}], 'overallOfficials': [{'name': 'Yizhi Liu, M.D., Ph.D.', 'role': 'STUDY_CHAIR', 'affiliation': 'Zhongshan Ophthalmic Center, Sun Yat-sen University'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Sun Yat-sen University', 'class': 'OTHER'}, 'collaborators': [{'name': 'First Affiliated Hospital, Sun Yat-Sen University', 'class': 'OTHER'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Principal Investigator', 'investigatorFullName': 'Haotian Lin', 'investigatorAffiliation': 'Sun Yat-sen University'}}}}