Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
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.
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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. |
| |
| 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. |
| |
| 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. |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Microscopic hyperspectral imaging system | Diagnostic Test | Microscopic hyperspectral imaging system |
|
| Measure | Description | Time Frame |
|---|---|---|
| Microhyperspectral image of urine specimen | Microhyperspectral images of urine samples from patients with four different glomerular diseases before treatment | 2023.4-2023.10 |
Not provided
Not provided
Inclusion Criteria:
Exclusion Criteria:
Not provided
Not provided
Patients with massive proteinuria were diagnosed as IgA nephropathy, idiopathic membranous nephropathy, diabetic nephropathy and minimal change nephropathy by renal biopsy.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Wang Zongsong | Contact | 18660190175 | wzsong3@163.com |
Not provided
Not provided
Not provided
| ID | Term |
|---|---|
| D005922 | Glomerulonephritis, IGA |
| D015433 | Glomerulonephritis, Membranous |
| D003928 | Diabetic Nephropathies |
| D009402 | Nephrosis, Lipoid |
| ID | Term |
|---|---|
| D005921 | Glomerulonephritis |
| D009393 | Nephritis |
| D007674 | Kidney Diseases |
| D014570 | Urologic Diseases |
Not provided
Not provided
Not provided
Not provided
Not provided
|
| 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. |
|
| D052776 |
| Female Urogenital Diseases |
| D005261 | Female Urogenital Diseases and Pregnancy Complications |
| D000091642 | Urogenital Diseases |
| D052801 | Male Urogenital Diseases |
| D001327 | Autoimmune Diseases |
| D007154 | Immune System Diseases |
| D048909 | Diabetes Complications |
| D003920 | Diabetes Mellitus |
| D004700 | Endocrine System Diseases |
| D009401 | Nephrosis |