Development of an AI-Agent for Urological Disease Diagnosis and Treatment
1 other identifier
observational
2,000
1 country
3
Brief 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. 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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Apr 2025
Typical duration for all trials
3 active sites
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
Study Start
First participant enrolled
April 1, 2025
CompletedFirst Submitted
Initial submission to the registry
July 12, 2026
CompletedFirst Posted
Study publicly available on registry
July 23, 2026
CompletedPrimary Completion
Last participant's last visit for primary outcome
December 31, 2026
ExpectedStudy Completion
Last participant's last visit for all outcomes
June 30, 2027
July 23, 2026
July 1, 2026
1.8 years
July 12, 2026
July 18, 2026
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
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).
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
Retrospective cases - at data extraction (single time point); Prospective cases - at enrollment (single time point); no longitudinal follow-up.
Treatment Recommendation Appropriateness of UroAgent
Retrospective cases - at data extraction (single time point); Prospective cases - at enrollment (single time point); no longitudinal follow-up.
Clinical Safety of UroAgent Recommendations
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
Retrospective cases - at data extraction (single time point); Prospective cases - at enrollment (single time point); no longitudinal follow-up.
Study Arms (1)
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.
Eligibility Criteria
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.
You may qualify if:
- 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.
You may not qualify if:
- \. Missing clinical information, imaging data, or surgical video.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (3)
Sun Yat-sen Memorial Hospital, Sun Yat-sen University
Guangzhou, Guangdong, 510000, China
Shenshan Medical Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University
Shantou, Guangdong, 516600, China
Ganzhou People's Hospital
Ganzhou, Jiangxi, 341000, China
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Central Study Contacts
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- OTHER
- Target Duration
- 1 Day
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Principal Investigator, Clinical Professor
Study Record Dates
First Submitted
July 12, 2026
First Posted
July 23, 2026
Study Start
April 1, 2025
Primary Completion (Estimated)
December 31, 2026
Study Completion (Estimated)
June 30, 2027
Last Updated
July 23, 2026
Record last verified: 2026-07
Data Sharing
- IPD Sharing
- Will not share
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.