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Идёт набор NCT07721935

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

— Наблюдательное Urinary Stones Prostate Cancer Bladder Cancer

Ориентир для пациента и семьи

Простыми словами

Автоматическая сводка по структурированным данным реестра. Она помогает сориентироваться, но не заменяет официальный протокол или оценку врача.

Что изучают
Это наблюдательное исследование: исследуемое лечение участникам по протоколу не назначают.
Кому может быть актуально
Состояния в реестре: Urinary Stones, Prostate Cancer, Bladder Cancer. Базовые параметры: Без ограничений · Все.
Что важно проверить
Возраст, диагноз и пол — только базовые ориентиры. Предыдущее лечение, анализы и другие обязательные условия указаны ниже в критериях участия.
Где проводится
Китай
Следующий шаг
Сохраните исследование, покажите его лечащему врачу и уточните актуальный статус у исследовательского центра. Расходы, документы и поездка →

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Официальное название

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

Обзор

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.

Подробное описание

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.

Первичные конечные точки

  • Diagnostic Accuracy of UroAgent [Срок оценки: Retrospective cases - at data extraction (single time point); Prospective cases - at enrollment (single time point); no longitudinal follow-up.]
Вторичные конечные точки (4)
  • Expert Subjective Accuracy Rating [Срок оценки: Retrospective cases - at data extraction (single time point); Prospective cases - at enrollment (single time point); no longitudinal follow-up.]
  • Treatment Recommendation Appropriateness of UroAgent [Срок оценки: Retrospective cases - at data extraction (single time point); Prospective cases - at enrollment (single time point); no longitudinal follow-up.]
  • Clinical Safety of UroAgent Recommendations [Срок оценки: 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 [Срок оценки: Retrospective cases - at data extraction (single time point); Prospective cases - at enrollment (single time point); no longitudinal follow-up.]

Критерии участия

Критерии включения

  • 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.

Критерии исключения

1\. Missing clinical information, imaging data, or surgical video.

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Модель наблюдения
Когортное

Центры проведения

Китай · 3 центра
  • Sun Yat-sen Memorial Hospital, Sun Yat-sen University — Гуанчжоу
  • Shenshan Medical Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University — Shantou
  • Ganzhou People's Hospital — Ganzhou

Идентификаторы

NCT: NCT07721935 · SYSKY-2025-151-03

Первоисточники (государственные реестры)

Открыть это исследование на ClinicalTrials.gov ↗