NCT07816874|Active Not Recruiting
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Sponsor study identifier
observational
8,000
1 country
1
Study Start
January 1, 2019
Actual (reported)Primary Completion
March 31, 2026
Actual (reported)Study Completion
May 10, 2027
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
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.
Registry-reported information. Elapsed time and enrollment totals alone do not measure study progress or recruitment pace.
May 10, 2027
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
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Dates and qualifiers reported by the registry. Estimated dates do not confirm that a milestone has occurred.
January 1, 2019
Actual (reported)March 31, 2026
Actual (reported)August 30, 2026
Actual (reported)September 14, 2026
Actual (reported)May 10, 2027
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
Diagnostic classification accuracy of the deep learning model, measured as the percentage of correctly classified renal pathology cases
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.
At completion of independent test-set evaluation using retrospective data collected from January 1, 2019, through March 31, 2026.
Agreement between deep learning model predictions and expert consensus diagnoses, measured by the Cohen kappa coefficient
At completion of independent test-set evaluation using retrospective data collected from January 1, 2019, through March 31, 2026.
The study population will comprise patients who underwent native kidney biopsy at Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, between January 1, 2019, and March 31, 2026. Eligible cases will be retrospectively identified from the hospital's electronic medical record and pathology image systems. Patients with biopsy-confirmed kidney diseases and available digital renal pathology images and corresponding clinical and laboratory data will be included for model development and validation. Kidney allograft biopsy cases will not be included.
Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Wuhan, Hubei, 430000, China
8,000 participants (actual enrollment reported by the registry).
First Submitted
August 30, 2026
First Posted
September 14, 2026
Study Start
January 1, 2019
Actual (reported)A Study to Evaluate the Safety and Efficacy of DDO-3055 in Healthy Volunteers and Patients With Chronic Kidney Disease
Contact the study team to confirm eligibility.
Primary Completion
March 31, 2026
Actual (reported)Study Completion
May 10, 2027
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
Last Updated
September 14, 2026
Record last verified: 2026-08
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Source: ClinicalTrials.gov.
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