Model Development and Application Based on Renal Pathology Images

This single-center retrospective observational cohort study will utilize digital renal pathology images along with corresponding clinical and laboratory data from patients who underwent kidney biopsy at Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, between January 1, 2019, and March 31, 2026. Deep learning and image analysis techniques will be employed to develop and validate models for the pathological diagnosis and classification of kidney diseases. The study will also assess model performance in evaluating disease activity and chronicity, as well as investigate the association between image-derived features and long-term renal outcomes. Diagnoses will be established by consensus among three senior renal pathologists, who will serve as the reference standard. A minimum of 8,000 eligible cases are planned for inclusion, which will be divided into training, validation, and independent test sets.

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This study is active but is not currently recruiting participants.

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Key information

Conditions

Kidney Diseases, Chronic Autoimmune Diseases Chronic Disease Disease Attributes Female Urogenital Diseases Female Urogenital Diseases and Pregnancy Complications Glomerular Diseases Glomerulonephritis Glomerulonephritis, IGA Glomerulonephritis, Membranous IgA Nephropathy (IgAN) Immune System Diseases Kidney Diseases Male Urogenital Diseases Membranous Nephropathy Nephritis Pathologic Processes Pathological Conditions, Signs and Symptoms Renal Insufficiency Renal Insufficiency, Chronic Tubulointerstitial Kidney Diseases Urogenital Diseases Urologic Diseases

Sex eligibility

All sexes

Study type

Observational

Primary location

Union Hospital, Tongji Medical College, Huazhong University of Science and Technology

Wuhan, Hubei, 430000, China

Who can participate

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

Inclusion criteria

Patients with kidney disease confirmed by kidney biopsy.
Availability of complete digital renal pathology images, including light microscopy, immunofluorescence microscopy, and/or electron microscopy images, as required for the relevant diagnostic task.
Availability of the clinical and laboratory data required for the planned analyses.

Exclusion criteria

Renal pathology images of insufficient quality for analysis.
Missing key clinical data required for the planned analyses.
Kidney allograft biopsy specimens.

Treatment and study plan

Primary outcomes

Diagnostic classification accuracy of the deep learning model, measured as the percentage of correctly classified renal pathology cases

Time frame: At completion of independent test-set evaluation using retrospective data collected from January 1, 2019, through March 31, 2026.

Diagnostic classification accuracy will be assessed in the independent test set by comparing the renal disease classification predicted by the prespecified deep learning model with the consensus diagnosis established by three senior renal pathologists. Accuracy will be calculated as the number of correctly classified cases divided by the total number of evaluable cases and reported as a percentage (%). The measurement tool will be the prespecified deep learning model evaluated against the expert consensus reference standard.

Secondary outcomes

Agreement between deep learning model predictions and expert consensus diagnoses, measured by the Cohen kappa coefficient

Time frame: At completion of independent test-set evaluation using retrospective data collected from January 1, 2019, through March 31, 2026.

Agreement between the renal disease classifications generated by the prespecified deep learning model and the consensus diagnoses established by three senior renal pathologists will be assessed using the Cohen kappa coefficient. The measurement tool will be a prespecified categorical agreement analysis comparing model-predicted classifications with the expert consensus reference standard. The Cohen kappa coefficient is a unitless measure, with higher values indicating greater agreement beyond chance.

Sponsors and collaborators

Lead sponsor

Union Hospital, Tongji Medical College, Huazhong University of Science and Technology

Other

Registry information

Important dates

Study start: 2019
Primary completion: 2026
Study completion: 2027
First posted: Sep 14, 2026
Registry last updated: Sep 14, 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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