Chronic Glomerulonephritis, Chronic Kidney Disease
Conditions
Keywords
Artificial intelligence, TEM-AID, glomerulonephritis
Brief summary
This research project aims to collect images from patients with chronic glomerulonephritis. For each subject, the images obtained from renal biopsy will undergo evaluation by both the diagnostic model and the 'gold standard' diagnosis by pathologists. The researchers will test the subjects using the diagnostic model and compare the results with the known 'gold standard' diagnosis, in order to evaluate the AUC, specificity, and sensitivity of the diagnostic model.
Detailed description
Renal biopsy pathology is an essential gold standard for the diagnosis of most glomerular diseases, relying on the comprehensive evaluation of H&E staining, special stains (such as PAS, PASM, and Masson), immunofluorescence, and the ultrastructural study under transmission electron microscopy (TEM). This research project aims to collect images from patients with chronic glomerulonephritis. For each subject, the images obtained from renal biopsy will undergo evaluation by both the diagnostic model and the 'gold standard' diagnosis by pathologists. The researchers will test the subjects using the diagnostic model and compare the results with the known 'gold standard' diagnosis, in order to evaluate the AUC, specificity, and sensitivity of the diagnostic model.
Interventions
None listed
Sponsors
Zhujiang Hospital
Study design
Observational model
OTHER
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
15 Years to 79 Years
Healthy volunteers
No
Inclusion criteria
1. Voluntary signing of informed consent form; 2. Patients clinically diagnosed or suspected of having chronic kidney disease according to the 2023 KDIGO Clinical Practice Guideline for the Evaluation and Management of Kidney Disease; 3. Undergoing renal biopsy and pathological specimen preparation.
Exclusion criteria
1. Biopsy tissue from donor kidney or transplanted kidney; 2. Poor quality of pathological specimen, unable to conduct pathological diagnosis.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of the TEM-AID artificial intelligence model | baseline | The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of the TEM-AID artificial intelligence model for candidates will be calculated. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| The specificity of the TEM-AID artificial intelligence model. | baseline | The specificity of the TEM-AID artificial intelligence model for candidates will be calculated. |
| The sensitivity of the TEM-AID artificial intelligence model. | baseline | The sensitivity of the TEM-AID artificial intelligence model for candidates will be calculated. |
Countries
China
Contacts
CONTACTYangshu Zhou
Outcome results
None listed