Ignite Creation Date:
2025-12-24 @ 11:39 PM
Ignite Modification Date:
2025-12-25 @ 9:29 PM
Study NCT ID:
NCT05797051
Status:
UNKNOWN
Last Update Posted:
2023-05-03
First Post:
2023-03-18
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Application of Hyperspectral Imaging in the Diagnosis of Glomerular Diseases
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D005922', 'term': 'Glomerulonephritis, IGA'}, {'id': 'D015433', 'term': 'Glomerulonephritis, Membranous'}, {'id': 'D003928', 'term': 'Diabetic Nephropathies'}, {'id': 'D009402', 'term': 'Nephrosis, Lipoid'}], 'ancestors': [{'id': 'D005921', 'term': 'Glomerulonephritis'}, {'id': 'D009393', 'term': 'Nephritis'}, {'id': 'D007674', 'term': 'Kidney Diseases'}, {'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'}, {'id': 'D001327', 'term': 'Autoimmune Diseases'}, {'id': 'D007154', 'term': 'Immune System Diseases'}, {'id': 'D048909', 'term': 'Diabetes Complications'}, {'id': 'D003920', 'term': 'Diabetes Mellitus'}, {'id': 'D004700', 'term': 'Endocrine System Diseases'}, {'id': 'D009401', 'term': 'Nephrosis'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'OTHER', 'observationalModel': 'OTHER'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 80}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'UNKNOWN', 'lastKnownStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2023-05-30', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2023-04', 'completionDateStruct': {'date': '2023-09-20', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2023-04-29', 'studyFirstSubmitDate': '2023-03-18', 'studyFirstSubmitQcDate': '2023-03-31', 'lastUpdatePostDateStruct': {'date': '2023-05-03', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2023-04-04', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2023-08-20', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Microhyperspectral image of urine specimen', 'timeFrame': '2023.4-2023.10', 'description': 'Microhyperspectral images of urine samples from patients with four different glomerular diseases before treatment'}]}, 'oversightModule': {'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Glomerulonephritis, IGA', 'Glomerulonephritis, Membranous', 'Diabetic Nephropathies', 'Nephrosis, Lipoid', 'Hyperspectral Imaging']}, 'descriptionModule': {'briefSummary': 'Morning urine samples of patients with IgA nephropathy, idiopathic membranous nephropathy, diabetic nephropathy, and minimal degenerative nephropathy confirmed by renal needle biopsy in our hospital from November 2020 to January 2022 were collected. By scanning the morning urine samples of corresponding patients with microhyperspectral imager, machine learning and deep learning were used to classify microhyperspectral images, and the classification accuracy was greater than 85%. Thus, hyperspectral imaging technology could be used as a non-invasive diagnostic means to assist the diagnosis of glomerular diseases.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '90 Years', 'minimumAge': '18 Years', 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': 'Patients with massive proteinuria were diagnosed as IgA nephropathy, idiopathic membranous nephropathy, diabetic nephropathy and minimal change nephropathy by renal biopsy.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Over 18 years old;\n* Patients with IgA nephropathy, idiopathic membranous nephropathy, diabetic nephropathy, minimal change nephropathy confirmed by renal biopsy;\n* Had not received hormone and/or immunosuppressive therapy before renal biopsy;\n* Complete clinical data, all signed the "Admission Certificate of Qianfoshan Hospital of Shandong Province", and agreed to use relevant medical information, biological specimen examination and examination results for scientific research.\n\nExclusion Criteria:\n\n* There are factors causing secondary membranous nephropathy, such as immune diseases (systemic lupus erythematosus), tumors/infections (viral hepatitis), drugs or poisons, etc.;\n* Severe infection: fever, cough and expectoration, sore throat, abdominal pain, diarrhea, carbuncle and furuncle and other clinical manifestations of skin and soft tissue infection, blood routine white blood cell count beyond the normal range (10×109/L);\n* Severe cardiovascular disease: including chronic heart failure grade 3 or above and various arrhythmias;\n* Infectious diseases: active hepatitis, AIDS, syphilis, etc. ;\n* Tumor evidence: it has been found that there is a certain tumor or clinical manifestations, tumor markers, etc., suggesting the possibility of tumor.'}, 'identificationModule': {'nctId': 'NCT05797051', 'briefTitle': 'Application of Hyperspectral Imaging in the Diagnosis of Glomerular Diseases', 'organization': {'class': 'OTHER', 'fullName': 'Qianfoshan Hospital'}, 'officialTitle': 'Application of Hyperspectral Imaging in the Diagnosis of Glomerular Diseases', 'orgStudyIdInfo': {'id': 'liquid biopsy-glomerulopathy'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'diabetic nephropathy', 'description': 'Urine samples were collected from patients with IgA nephropathy, idiopathic membranous nephropathy, diabetic nephropathy and minimal change nephropathy. The samples were centrifuged and frozen in a refrigerator at -80 degrees Celsius. The images were divided into a training set and a test set at a fixed ratio. The digital images were input into classification models such as one-dimensional convolutional neural network to learn and test. The training set was used for the training and parameter iteration of the artificial intelligence non-invasive fluid diagnosis model, and the test set was used for the recognition and interpretation of the model. The confusion matrix, accuracy and ROC curve were calculated through the interpretation results to evaluate the performance of the model.', 'interventionNames': ['Diagnostic Test: Microscopic hyperspectral imaging system']}, {'label': 'minimal change nephropathy', 'description': 'Urine samples were collected from patients with IgA nephropathy, idiopathic membranous nephropathy, diabetic nephropathy and minimal change nephropathy. The samples were centrifuged and frozen in a refrigerator at -80 degrees Celsius. The images were divided into a training set and a test set at a fixed ratio. The digital images were input into classification models such as one-dimensional convolutional neural network to learn and test. The training set was used for the training and parameter iteration of the artificial intelligence non-invasive fluid diagnosis model, and the test set was used for the recognition and interpretation of the model. The confusion matrix, accuracy and ROC curve were calculated through the interpretation results to evaluate the performance of the model.', 'interventionNames': ['Diagnostic Test: Microscopic hyperspectral imaging system']}, {'label': 'IgA nephropathy', 'description': 'Urine samples were collected from patients with IgA nephropathy, idiopathic membranous nephropathy, diabetic nephropathy and minimal change nephropathy. The samples were centrifuged and frozen in a refrigerator at -80 degrees Celsius. The images were divided into a training set and a test set at a fixed ratio. The digital images were input into classification models such as one-dimensional convolutional neural network to learn and test. The training set was used for the training and parameter iteration of the artificial intelligence non-invasive fluid diagnosis model, and the test set was used for the recognition and interpretation of the model. The confusion matrix, accuracy and ROC curve were calculated through the interpretation results to evaluate the performance of the model.', 'interventionNames': ['Diagnostic Test: Microscopic hyperspectral imaging system']}, {'label': 'idiopathic membranous nephropathy', 'description': 'Urine samples were collected from patients with IgA nephropathy, idiopathic membranous nephropathy, diabetic nephropathy and minimal change nephropathy. The samples were centrifuged and frozen in a refrigerator at -80 degrees Celsius. The images were divided into a training set and a test set at a fixed ratio. The digital images were input into classification models such as one-dimensional convolutional neural network to learn and test. The training set was used for the training and parameter iteration of the artificial intelligence non-invasive fluid diagnosis model, and the test set was used for the recognition and interpretation of the model. The confusion matrix, accuracy and ROC curve were calculated through the interpretation results to evaluate the performance of the model.', 'interventionNames': ['Diagnostic Test: Microscopic hyperspectral imaging system']}], 'interventions': [{'name': 'Microscopic hyperspectral imaging system', 'type': 'DIAGNOSTIC_TEST', 'description': 'Microscopic hyperspectral imaging system', 'armGroupLabels': ['IgA nephropathy', 'diabetic nephropathy', 'idiopathic membranous nephropathy', 'minimal change nephropathy']}]}, 'contactsLocationsModule': {'centralContacts': [{'name': 'Wang Zongsong', 'role': 'CONTACT', 'email': 'wzsong3@163.com', 'phone': '18660190175'}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Qianfoshan Hospital', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Qianfo Mountain Hospital of Shandong Province', 'investigatorFullName': 'Zunsong Wang', 'investigatorAffiliation': 'Qianfoshan Hospital'}}}}