An Artificial Intelligence Model for Screening and Diagnosis of Renal Tumors Based on Non-Contrast CT
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 10,000
- 试验地点
- 1
- 主要终点
- Building an intelligent diagnostic system for renal diseases based on CT scans.
研究概览
简要总结
The goal of this observational study is to learn whether the artificial intelligence method can automatically identify and diagnose renal lesions using non-contrast CT or opportunistic screening.
详细描述
This study first establishes an AI model capable of effectively detecting and diagnosing kidney lesions based on a multicenter retrospective cohort study. Then, the AI model is applied to a large-scale real-world retrospective and prospective population to validate and improve its effectiveness.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Other
入排标准
- 年龄范围
- 18 Years 至 80 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Patients who underwent an abdominal CT examination.
- •Patients with renal lesions were managed according to standard clinical pathways, which included follow-up, biopsy, or surgery.
- •Malignant lesions were pathologically confirmed; benign lesions were confirmed by either pathological diagnosis or imaging follow-up.
- •No prior treatment had been received for the renal disease.
排除标准
- •Patients refuse to undergo recommended follow-up, biopsy, or surgery, which precluded definitive diagnosis of the renal lesion.
- •Absence of complete pathological confirmation for lesions suspected to be malignant.
- •Patients have received any form of prior treatment for the renal lesion.
- •Poor image quality that hampered diagnostic evaluation.
结局指标
主要结局
Building an intelligent diagnostic system for renal diseases based on CT scans.
时间窗: 1 year
To construct an intelligent system for the detection of renal mass lesions and their differentiation into cysts, benign, and malignant neoplasms.
次要结局
- Further develop artificial intelligence model to effectively diagnose pathological types of common renal tumors.(1 year)
研究者
Yajia Gu, MD
Director, Head of Radiology, Principal Investigator, Clinical Professor
Fudan University