Application of Hyperspectral Imaging in the Diagnosis of Glomerular Diseases

Sponsor

Qianfoshan Hospital (Other)

Overall Status

Not yet recruiting

CT.gov ID

NCT05797051

Collaborator

(none)

Enrollment

5.7

Anticipated Duration (Months)

Study Details

Study Description

Brief Summary

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.

Condition or Disease	Intervention/Treatment	Phase
Glomerulonephritis, IGA Glomerulonephritis, Membranous Diabetic Nephropathies Nephrosis, Lipoid Hyperspectral Imaging	Diagnostic Test: Microscopic hyperspectral imaging system

Study Design

Study Type:

Observational

Anticipated Enrollment :

80 participants

Observational Model:

Other

Time Perspective:

Other

Official Title:

Application of Hyperspectral Imaging in the Diagnosis of Glomerular Diseases

Anticipated Study Start Date :

Mar 30, 2023

Anticipated Primary Completion Date :

Aug 20, 2023

Anticipated Study Completion Date :

Sep 20, 2023

Arms and Interventions

Arm	Intervention/Treatment
diabetic nephropathy 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.	Diagnostic Test: Microscopic hyperspectral imaging system Microscopic hyperspectral imaging system
minimal change nephropathy 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.	Diagnostic Test: Microscopic hyperspectral imaging system Microscopic hyperspectral imaging system
IgA nephropathy 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.	Diagnostic Test: Microscopic hyperspectral imaging system Microscopic hyperspectral imaging system
idiopathic membranous nephropathy 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.	Diagnostic Test: Microscopic hyperspectral imaging system Microscopic hyperspectral imaging system

Outcome Measures

Primary Outcome Measures

Microhyperspectral image of urine specimen [2023.4-2023.10]
Microhyperspectral images of urine samples from patients with four different glomerular diseases before treatment

Eligibility Criteria

Criteria

Ages Eligible for Study:

18 Years to 90 Years

Sexes Eligible for Study:

All

Accepts Healthy Volunteers:

Inclusion Criteria:

Over 18 years old;
Patients with IgA nephropathy, idiopathic membranous nephropathy, diabetic nephropathy, minimal change nephropathy confirmed by renal biopsy;
Had not received hormone and/or immunosuppressive therapy before renal biopsy;
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.

Exclusion Criteria:

There are factors causing secondary membranous nephropathy, such as immune diseases (systemic lupus erythematosus), tumors/infections (viral hepatitis), drugs or poisons, etc.;
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);
Severe cardiovascular disease: including chronic heart failure grade 3 or above and various arrhythmias;
Infectious diseases: active hepatitis, AIDS, syphilis, etc. ;
Tumor evidence: it has been found that there is a certain tumor or clinical manifestations, tumor markers, etc., suggesting the possibility of tumor.