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NCT Number: NCT07721935

Development of an AI-Agent for Urological Disease Diagnosis and Treatment

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.

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Key information

Conditions

Urinary Stones Bladder Cancer Calculi Female Urogenital Diseases Female Urogenital Diseases and Pregnancy Complications Genital Diseases Genital Diseases, Male Genital Neoplasms, Male Male Urogenital Diseases Neoplasms Neoplasms by Site Pathological Conditions, Anatomical Pathological Conditions, Signs and Symptoms Prostate Cancer Prostatic Diseases Prostatic Neoplasms Urinary Bladder Diseases Urinary Bladder Neoplasms Urinary Calculi Urogenital Diseases Urogenital Neoplasms Urolithiasis Urologic Diseases Urologic Neoplasms

Sex eligibility

All sexes

Study type

Observational

Primary location

Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China

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About this study

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.

Who can participate

Healthy volunteers accepted: No

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

Inclusion criteria

  • 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.

Exclusion criteria

  • Missing clinical information, imaging data, or surgical video.

Treatment and study plan

Primary outcomes

  1. 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).

Secondary outcomes

  1. 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.

    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 (%).

  2. 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.

    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.

  3. 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.

    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.

  4. 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.

    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).

Study contacts

Contact information is provided by the study sponsor or research team.

Mingzhou Dai

CONTACT

[email protected]

+86 19924684262

Sponsors and collaborators

Lead sponsor

Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

Other

Collaborators

  • Ganzhou City People's Hospital
  • Shenshan Medical Center, Memorial Hospital of Sun Yat-sen University

Registry information

Official study title

Development of an AI-Agent for Diagnosis and Treatment of Urological Diseases

Important dates

Study start
2025
Primary completion
2026
Study completion
2027
First posted
Jul 23, 2026
Registry last updated
Jul 23, 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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