Urinary StonesNCT07721935
Development of an AI-Agent for Urological Disease Diagnosis and Treatment
An observational study of Urinary Stones and Prostate Cancer, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University at 3 sites in China.
Trial summary
Urological diseases such as urinary stones, prostate cancer, and bladder cancer are very common and often require highly specialized diagnosis and treatment. Today, the quality of care can vary between doctors, and there are not enough urology specialists to meet patient demand. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Urinary Stones, Prostate Cancer, Bladder Cancer
- How many people
- 2,000 participants
- Where
- 3 sitesChina
- Study dates
- 1 Apr 2025to 30 Jun 2027
- How long will I take part?
- 1 Day
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: The study population comprises patients diagnosed with urological diseases at Sun Yat-sen Memorial Hospital, including urinary stones, prostate cancer, bladder cancer, and other urological conditions. A total of 2,000 participants (cases) will be included: 1,500 retrospective cases with records from 2010 to 2025, and 500 prospective, consecutively enrolled cases. Both sexes are eligible, and the population is predominantly adult (the protocol does not specify an age cutoff; an age criterion of ≥18 years is recommended for registry entry, per the note in section 16). All included participants have complete clinical information, imaging data, and surgical video available for model development and validation. Records are de-identified prior to use. The population reflects the real-world case mix of a tertiary urology center.
Who may be able to join
- Diagnosed with a urological disease (e.g., urinary stones, prostate cancer, bladder cancer, and other urological conditions).
- Availability of complete clinical information, imaging data, and surgical video required for model development and validation.
Who cannot join
- 1. Missing clinical information, imaging data, or surgical video.
Trial locations and contacts
3 sites in 1 country. Contact the study team or a site near you to ask about taking part.
China3 sites, 3 recruiting
- Recruiting
Ganzhou People's Hospital
Ganzhou, Jiangxi, 341000
Cong Lai, +86 19872753573, laic@mail2.sysu.edu.cn
- Recruiting
Sun Yat-sen Memorial Hospital, Sun Yat-sen University
Guangzhou, Guangdong, 510000
Mingzhou Dai, +86 19924684262, daimzh3@mail2.sysu.edu.cn
- Recruiting
Shenshan Medical Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University
Shantou, Guangdong, 516600
Zhuang Tang, +86 13666144867, tangzh23@mail.sysu.edu.cn
About this study
Official title: Development of an AI-Agent for Diagnosis and Treatment of Urological Diseases
Urological diseases such as urinary stones, prostate cancer, and bladder cancer are very common and often require highly specialized diagnosis and treatment. Today, the quality of care can vary between doctors, and there are not enough urology specialists to meet patient demand. Artificial intelligence (AI) may help doctors make faster and more consistent decisions.
This study aims to develop and test an AI-powered assistant called "UroAgent" that supports doctors in diagnosing and treating urological diseases. UroAgent is built on a large language model trained specifically for urology and is connected to tools that help it retrieve medical knowledge and analyze images. To build and test UroAgent, the research team will use 1,500 past patient records from 2010-2025 and collect 500 new patient cases for validation, for a total of 2,000 cases. This is an observational study: no patient's medical treatment will be changed because of it. The goal is to create a reliable AI tool that helps improve urological care for patients.
Read the detailed description
This study protocol describes an observational study aiming to develop and validate UroAgent, an artificial-intelligence agent for the diagnosis and treatment of urological diseases. A total of 2,000 urological disease cases will be collected, comprising 1,500 retrospective cases recorded at the center between 2010 and 2025 for model development and 500 prospectively enrolled cases for independent performance validation. The primary evaluation is the concordance between UroAgent's diagnostic and treatment recommendations and the reference standards established by senior urologists, assessed through diagnostic accuracy, recommendation appropriateness, completeness, and safety; secondary evaluations include the agent's performance across disease subtypes (urinary stones, prostate cancer, bladder cancer) and its image-interpretation capability. All records will undergo de-identification, and the study will adhere to rigorous ethical standards and a pre-specified statistical analysis plan to provide robust evidence for the clinical application of this urology-specific AI agent.
- Conditions
- Urinary Stones, Prostate Cancer, Bladder Cancer
- Keywords
- Urological Diseases, Urinary Stones, Prostate Cancer, Bladder Cancer, Artificial Intelligence, Large Language Model, AI Agent, Clinical Decision Support
- Medical subject headings
- Urinary Calculi, Prostatic Neoplasms, Urinary Bladder Neoplasms, Urologic Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Urological Disease Cohort | The cohort consists of patients diagnosed with urological diseases-including urinary stones, prostate cancer, and bladder cancer-at Sun Yat-sen Memorial Hospital. A total of 2,000 cases are included: 1,500 retrospective cases recorded between 2010 and 2025 (used for model development) and 500 prospectively and consecutively enrolled cases (used for independent validation); all records are de-identified. The intervention (technology) of interest is "UroAgent," an artificial-intelligence diagnostic-and-treatment agent built on a urology-specialized large language model with integrated tool modules (knowledge retrieval, image interpretation). For each case, UroAgent's diagnostic and treatment recommendations are generated and compared with the analyses provided by human urology specialists, to evaluate the agent's performance-diagnostic accuracy, recommendation appropriateness, completeness, and safety-against expert judgment. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic Accuracy of UroAgent
The primary outcome is UroAgent's diagnostic accuracy, measured as the F1 score of its leading diagnosis against the reference-standard final diagnosis. The reference standard is established by senior urologists from pathology, imaging, and clinical course. F1 = 2 × Precision × Recall / (Precision + Recall), computed per case and aggregated as macro-F1 across the 2,000-case cohort (1,500 retrospective + 500 prospective). Unit of measure: F1 score (range 0-1).
Measured: Retrospective cases - at data extraction (single time point); Prospective cases - at enrollment (single time point); no longitudinal follow-up.
Secondary outcomes (4)
Expert Subjective Accuracy Rating
Independent urologists rate the accuracy of UroAgent's diagnosis on a 5-point Likert scale (1 = completely inaccurate, 5 = completely accurate), blinded to model identity. Reported as the mean rating and the percentage of cases rated ≥ 4; disagreements resolved by a third urologist. Unit of measure: mean rating (1-5) and percentage of cases rated accurate (%).
Measured: Retrospective cases - at data extraction (single time point); Prospective cases - at enrollment (single time point); no longitudinal follow-up.
Treatment Recommendation Appropriateness of UroAgent
The proportion of cases in which UroAgent's management recommendation is rated appropriate. Two independent urologists rate each recommendation on a 5-point Likert appropriateness scale (1 = completely inappropriate, 5 = completely appropriate), blinded; "appropriate" defined as Likert ≥ 4; disagreement resolved by a third urologist. Unit of measure: percentage of cases (%) rated appropriate.
Measured: Retrospective cases - at data extraction (single time point); Prospective cases - at enrollment (single time point); no longitudinal follow-up.
Clinical Safety of UroAgent Recommendations
The rate of clinically unsafe recommendations. Each case is reviewed by senior urologists using a safety rubric and flagged (binary per case) for any contraindicated, erroneous, or potentially harmful recommendation. Unit of measure: percentage of cases (%) with ≥ 1 unsafe recommendation.
Measured: Retrospective cases - at data extraction (single time point); Prospective cases - at enrollment (single time point); no longitudinal follow-up.
Concordance and Non-Inferiority of UroAgent versus Clinician Diagnoses
On the same cases, UroAgent's diagnoses are compared with those of practicing urologists. Reported as the agreement rate (%) between UroAgent and clinician diagnoses, plus the diagnostic-accuracy difference in percentage points (pp); Cohen's κ reported as a supplementary statistic. Both are assessed against the reference-standard final diagnosis. Unit of measure: agreement rate (%) and accuracy difference (pp).
Measured: Retrospective cases - at data extraction (single time point); Prospective cases - at enrollment (single time point); no longitudinal follow-up.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted23 Jul 2026
- Study start1 Apr 2025
- Primary completion31 Dec 2026 (estimated)
- Study completion30 Jun 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Urinary StonesStudies of the same condition, open and under way.
- More trials run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Other
- Enrolment
- 2,000 participants (estimated)
- Target duration
- 1 Day
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Collaborators
- Ganzhou City People's Hospital
- Responsible party
- Principal investigator, Xu Kewei, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07721935
- Sponsor's protocol ID
- SYSKY-2025-151-03
- Status last verified
- Jul 2026
- First submitted
- 12 Jul 2026
- Last updated
- 23 Jul 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data (IPD) will not be shared. This single-center observational study uses de-identified clinical records, imaging, and surgical video. The retrospective cases (2010-2025) were not collected with consent for external data sharing, and applicable personal-information protection regulations (e.g., China's Personal Information Protection Law) together with institutional policy prohibit transfer of patient-level data outside the sponsoring hospital. The study's primary deliverable is the UroAgent model and aggregated performance results, which will be disseminated through peer-reviewed publications and the trial registry record. Collaboration requests will be considered case-by-case under institutional approval.
Study at a glance
From the registry record, last updated by the sponsor on 23 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development of an AI-Agent for Diagnosis and Treatment of Urological Diseases
- ClinicalTrials.gov ID
- NCT07721935
- Acronym / sponsor ID
- SYSKY-2025-151-03
- Conditions
- Urinary Stones, Prostate Cancer, Bladder Cancer
- Keywords
- Urological Diseases, Urinary Stones, Prostate Cancer, Bladder Cancer, Artificial Intelligence, Large Language Model, AI Agent, Clinical Decision Support
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 2,000 participants (estimated)
- Lead sponsor
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityUniversity, hospital or other
- Collaborators
- Ganzhou City People's Hospital
- Study start
- 1 Apr 2025 (actual)
- Primary completion
- 31 Dec 2026 (estimated)
- Study completion
- 30 Jun 2027 (estimated)
- First posted
- 23 Jul 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Urological diseases such as urinary stones, prostate cancer, and bladder cancer are very common and often require highly specialized diagnosis and treatment. Today, the quality of care can vary between doctors, and there are not enough urology specialists to meet patient demand. Artificial intelligence (AI) may help doctors make faster and more consistent decisions.
This study aims to develop and test an AI-powered assistant called "UroAgent" that supports doctors in diagnosing and treating urological diseases. UroAgent is built on a large language model trained specifically for urology and is connected to tools that help it retrieve medical knowledge and analyze images. To build and test UroAgent, the research team will use 1,500 past patient records from 2010-2025 and collect 500 new patient cases for validation, for a total of 2,000 cases. This is an observational study: no patient's medical treatment will be changed because of it. The goal is to create a reliable AI tool that helps improve urological care for patients.
Detailed description
This study protocol describes an observational study aiming to develop and validate UroAgent, an artificial-intelligence agent for the diagnosis and treatment of urological diseases. A total of 2,000 urological disease cases will be collected, comprising 1,500 retrospective cases recorded at the center between 2010 and 2025 for model development and 500 prospectively enrolled cases for independent performance validation. The primary evaluation is the concordance between UroAgent's diagnostic and treatment recommendations and the reference standards established by senior urologists, assessed through diagnostic accuracy, recommendation appropriateness, completeness, and safety; secondary evaluations include the agent's performance across disease subtypes (urinary stones, prostate cancer, bladder cancer) and its image-interpretation capability. All records will undergo de-identification, and the study will adhere to rigorous ethical standards and a pre-specified statistical analysis plan to provide robust evidence for the clinical application of this urology-specific AI agent.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Target follow-up
- 1 Day
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Urological Disease Cohort | – | –The cohort consists of patients diagnosed with urological diseases-including urinary stones, prostate cancer, and bladder cancer-at Sun Yat-sen Memorial Hospital. A total of 2,000 cases are included: 1,500 retrospective cases recorded between 2010 and 2025 (used for model development) and 500 prospectively and consecutively enrolled cases (used for independent validation); all records are de-identified. The intervention (technology) of interest is "UroAgent," an artificial-intelligence diagnostic-and-treatment agent built on a urology-specialized large language model with integrated tool modules (knowledge retrieval, image interpretation). For each case, UroAgent's diagnostic and treatment recommendations are generated and compared with the analyses provided by human urology specialists, to evaluate the agent's performance-diagnostic accuracy, recommendation appropriateness, completeness, and safety-against expert judgment. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- The study population comprises patients diagnosed with urological diseases at Sun Yat-sen Memorial Hospital, including urinary stones, prostate cancer, bladder cancer, and other urological conditions. A total of 2,000 participants (cases) will be included: 1,500 retrospective cases with records from 2010 to 2025, and 500 prospective, consecutively enrolled cases. Both sexes are eligible, and the population is predominantly adult (the protocol does not specify an age cutoff; an age criterion of ≥18 years is recommended for registry entry, per the note in section 16). All included participants have complete clinical information, imaging data, and surgical video available for model development and validation. Records are de-identified prior to use. The population reflects the real-world case mix of a tertiary urology center.
Key inclusion criteria (2 listed)
- Diagnosed with a urological disease (e.g., urinary stones, prostate cancer, bladder cancer, and other urological conditions).
- Availability of complete clinical information, imaging data, and surgical video required for model development and validation.
Key exclusion criteria (1 listed)
- 1. Missing clinical information, imaging data, or surgical video.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Diagnostic Accuracy of UroAgent
Time frame: Retrospective cases - at data extraction (single time point); Prospective cases - at enrollment (single time point); no longitudinal follow-up.
The primary outcome is UroAgent's diagnostic accuracy, measured as the F1 score of its leading diagnosis against the reference-standard final diagnosis. The reference standard is established by senior urologists from pathology, imaging, and clinical course. F1 = 2 × Precision × Recall / (Precision + Recall), computed per case and aggregated as macro-F1 across the 2,000-case cohort (1,500 retrospective + 500 prospective). Unit of measure: F1 score (range 0-1).
4 secondary outcomes
Expert Subjective Accuracy Rating
Time frame: Retrospective cases - at data extraction (single time point); Prospective cases - at enrollment (single time point); no longitudinal follow-up.
Treatment Recommendation Appropriateness of UroAgent
Time frame: Retrospective cases - at data extraction (single time point); Prospective cases - at enrollment (single time point); no longitudinal follow-up.
Clinical Safety of UroAgent Recommendations
Time frame: Retrospective cases - at data extraction (single time point); Prospective cases - at enrollment (single time point); no longitudinal follow-up.
Concordance and Non-Inferiority of UroAgent versus Clinician Diagnoses
Time frame: Retrospective cases - at data extraction (single time point); Prospective cases - at enrollment (single time point); no longitudinal follow-up.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 23 Jul 2026.
- Individual participant dataSharing planned: No. Individual participant data (IPD) will not be shared. This single-center observational study uses de-identified clinical records, imaging, and surgical video. The retrospective cases (2010-2025) were not collected with consent for external data sharing, and applicable personal-information protection regulations (e.g., China's Personal Information Protection Law) together with institutional policy prohibit transfer of patient-level data outside the sponsoring hospital. The study's primary deliverable is the UroAgent model and aggregated performance results, which will be disseminated through peer-reviewed publications and the trial registry record. Collaboration requests will be considered case-by-case under institutional approval.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
3 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Urinary Stones and Prostate Cancer, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University at 3 sites in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 23 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 sites in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Mingzhou Dai (+86 19924684262, daimzh3@mail2.sysu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic Accuracy of UroAgent (time frame: Retrospective cases - at data extraction (single time point); Prospective cases - at enrollment (single time point); no longitudinal follow-up.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 23 Jul 2026; AzTrials last refreshed its copy on 7 Nov 2026.