Den här översättningen är inte klar ännu. Den här sidan är just nu på engelska.

Gå till den engelska sidan

Cure My Disease

Don't miss your cure!

 Log in / Sign up

Can AI learn to spot kidney disease in biopsy images?

NCT ID NCT07816874

Diagnosis Sponsor: Union Hospital, Tongji Medical College, Huazhong University of Science and Technology Source: ClinicalTrials.gov ↗

What the study statuses mean

This study's is highlighted.

Recruitment status, easiest to join first

Recruiting now
This trial is taking on new participants right now.
Not yet recruiting
Registered, but not yet taking participants.
By invitation only
Not open to general applications. Only people the study team invites can take part.
Paused
Paused for now. It may or may not start again.
Ongoing This study
Running, but no longer taking on new participants.
Completed
The trial has finished. Results may not be published yet.
Stopped early
Stopped early, before it reached the end. That can be for many reasons, including safety.
Cancelled
Cancelled before anyone took part.

Expanded access (not trials)

Expanded access
Not a trial. This treatment can be requested outside a study, case by case, for people who qualify.
Expanded access (paused)
Not a trial. The treatment can normally be requested outside a study, but is unavailable right now.
Expanded access (ended)
Not a trial. The treatment could once be requested outside a study, but no longer can.
Approved
The treatment has been approved, so it is available normally rather than through this programme.

When the status isn't known

Details not published
The full record has not been published yet, so there is little to show here.
Status unknown
This status has not been confirmed recently, so it may be out of date.

First seen Sep 14, 2026 · Last updated Sep 15, 2026 · Updated 1 time

Summary

Researchers are building deep learning models that analyze digital images of kidney biopsy tissue to classify kidney diseases such as IgA nephropathy and membranous nephropathy. The study uses pathology images and clinical data from thousands of patients who had kidney biopsies at one hospital. The goal is to see whether the models can match the diagnoses made by three senior kidney pathologists, and whether image features relate to long-term kidney outcomes.

What this could mean

Our plain-language read of the trial. This is informational only, not medical advice or a prediction.

Active substance
a deep learning image analysis model applied to digital kidney biopsy images
What this could lead to
If the models perform well, they could help pathologists classify kidney diseases faster and more consistently, and could reveal image patterns linked to how kidney disease progresses.
What could go wrong
This is a retrospective study using images from one hospital, so the models may not work as well on scans from other centers or patient groups. The models are being tested against expert diagnoses, not against real patient outcomes.

This is an AI summary of the original study and may miss details. Read our disclaimer.

Study facts

What this study's own registry entry says, in plain language.

Participants

8,000 people

The number who actually took part.

Started

Jan 2019

Expected to finish

May 2027

An estimate. End dates often move.

Lead sponsor

Other sponsor

The registry's catch-all category, for sponsors it does not file as a company, a government agency, or a research network.

Who can take part

This study's own entry requirements. Only the study team can say for certain whether you qualify.

Who is studied

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.

Ages

Children (under 18), adults (18 to 64) and older adults (65 and over)

Sex

Anyone

Show the full entry requirements

Copied word for word from the study's registry entry, so the wording is the study team's rather than ours.

Inclusion Criteria: 1. Patients with kidney disease confirmed by kidney biopsy. 2. Availability of complete digital renal pathology images, including light microscopy, immunofluorescence microscopy, and/or electron microscopy images, as required for the relevant diagnostic task. 3. Availability of the clinical and laboratory data required for the planned analyses. Exclusion Criteria: 1. Renal pathology images of insufficient quality for analysis. 2. Missing key clinical data required for the planned analyses. 3. Kidney allograft biopsy specimens.

Get updates

Get notified about this study

Sign up to get updates when this study changes or when new studies for Glomerular diseases are added.

Vår säkerhetsrekommendation!

Genom att skicka in godkänner du våra Användarvillkor

Conditions

The condition(s) this trial relates to.

chronic kidney disease chronic renal failure syndrome glomerular disorder IgA glomerulonephritis membranous glomerulonephritis

As listed by the trial registrant

The condition terms exactly as the trial's registrant entered them.

Glomerular diseases IgA nephropathy (IGAN) Kidney diseases, chronic Membranous nephropathy Tubulointerstitial kidney diseases

Contacts and locations

Locations

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

    Wuhan, Hubei, 430000, China

More trials for these conditions

Other studies related to the condition(s) this trial covers.