Artificial Intelligence Model for Renal Allograft Rejection Identification, Lesion Quantification and Rejection Risk Prediction Based on Prospective Renal Transplant Pathology and Clinical Multidimensional Data

Running, not enrolling

Conditions studied: Graft Rejection, Kidney Transplantation, Glomerulonephritis, Polyomavirus Infections

In brief

This prospective research collects leftover kidney biopsy tissue slides and matching routine clinical data from patients who received kidney transplants and underwent standard kidney puncture biopsy at Zhejiang University School of Medicine First Affiliated Hospital starting November 2025. A total of around 1,000 patient samples will be included, covering transplant rejection (including TCMR and ABMR subtypes, acute and chronic rejection), polyomavirus infection and recurrent original kidney disease after transplantation. All study materials come from residual biopsy specimens generated during regular clinical examinations, with no extra invasive operations, additional medical costs or physical trauma for participants. We will scan pathological slides into digital images and combine them with patients' medical records, lab test results, medication history and follow-up information. After full anonymization and standardized labeling by senior renal pathologists following the Banff standard, we will build an artificial intelligence (AI) multi-task model. This AI system will serve three core clinical functions: accurately distinguish different types of transplant kidney lesions, quantitatively measure tissue damage caused by rejection, and predict the risk of recurrent rejection after surgery. We will optimize and verify the model's diagnostic accuracy, stability and reliability through dataset segmentation, cross validation and algorithm adjustment. For patients, this study brings no extra physical or economic burden. If suspicious pathological changes are found during data analysis, relevant clues will be fed back to attending doctors to support individual treatment management. For clinical providers, the finished AI tool can reduce pathologists' reading workload, lower missed diagnosis and misdiagnosis caused by individual experience differences, especially improve detection of subclinical and borderline rejection. It helps clinicians evaluate injury severity and forecast recurrence risk, so as to formulate personalized immunosuppression regimens, reduce rejection relapse and prolong graft survival. Strict privacy protection measures are implemented throughout the whole research process: all personal identifiable information will be completely removed, and encrypted classified data management is adopted to prevent information leakage. Every participant signs a written informed consent and retains the right to withdraw from the study at any time without affecting their regular medical care. All research procedures have passed ethical review supervision, and all collected data and specimens will be properly stored or destroyed in accordance with standardized medical management rules after the study ends. The research aims to fill the gap of prospective multi-dimensional AI auxiliary diagnosis research in kidney transplantation, promote standardized, intelligent and precise post-transplant pathological evaluation, and provide new technical support to improve long-term survival outcomes of kidney transplant recipients.

Key facts

Study ID: NCT07741552
Run by: Chen Dajin
People needed: 1000
Starts: 2025-11-01
Expected to finish: 2030-10-31
Last updated by the study team: 2026-08-03

Who can join

Age: 18 and older, up to 75. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

Patients who received kidney transplantation and underwent clinically indicated renal allograft biopsy at The First Affiliated Hospital, Zhejiang University School of Medicine starting November 2025.
Age range from 18 to 75 years old.
Complete clinical baseline data, laboratory test records, medication history and pathological information available in electronic medical system.
Residual paraffin biopsy slides are available after routine pathological examination.
Voluntarily provide written informed consent for the use of residual pathological specimens and clinical data for research.

You may not qualify if…

Poor-quality biopsy specimens including blurred staining, severe tissue damage or insufficient tissue volume which cannot support pathological image analysis.
Severe missing core clinical variables that cannot be supplemented via standardized data imputation.
Complicated with severe irreversible dysfunction of heart, liver, brain or other vital organs affecting long-term clinical follow-up data collection.
Unable to complete routine clinical follow-up, resulting in unavailable outcome data for recurrent rejection labeling.
Refuse to participate or withdraw informed consent.

Where it is running

The First Affiliated Hospital, Zhejiang University School of Medicine — Hangzhou, Zhejiang, China

Full record on ClinicalTrials.gov

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