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NCT Number: NCT07330362

The Clinical Application of Artificial Intelligence Assisted Renal Biopsy Diagnosis System.

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.

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Key information

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Voluntary signing of informed consent form;
  • 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;
  • Undergoing renal biopsy and pathological specimen preparation.

Exclusion criteria

  • Biopsy tissue from donor kidney or transplanted kidney;
  • Poor quality of pathological specimen, unable to conduct pathological diagnosis.

Treatment and study plan

Primary outcomes

  1. The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of the TEM-AID artificial intelligence model

    Time frame: 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 outcomes

  1. The specificity of the TEM-AID artificial intelligence model.

    Time frame: baseline

    The specificity of the TEM-AID artificial intelligence model for candidates will be calculated.

  2. The sensitivity of the TEM-AID artificial intelligence model.

    Time frame: baseline

    The sensitivity of the TEM-AID artificial intelligence model for candidates will be calculated.

Study contacts

Contact information is provided by the study sponsor or research team.

Yangshu Zhou

CONTACT

[email protected]

020-61643888

Sponsors and collaborators

Lead sponsor

Zhujiang Hospital

Other

Registry information

Official study title

The Clinical Application of Artificial Intelligence Assisted Renal Biopsy Image Diagnosis System.

Important dates

Study start
2024
Primary completion
2026
Study completion
2026
First posted
Jan 9, 2026
Registry last updated
Jan 27, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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