Using Magnetic Resonance Imaging and DL Methods to Explore the Diagnosis and Clinical Prognosis of Joint Synovitis.
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 350
- 试验地点
- 1
- 主要终点
- Patient's diagnosis
研究概览
简要总结
Through the high-throughput feature extraction of magnetic resonance images, the deep learning prediction model of joint synovial lesions is constructed used for the diagnosis, differential diagnosis and curative effect monitoring of joint synovial lesions.
详细描述
The study applies magnetic resonance and deep learning (DL) to the diagnosis of joint synovial lesions, aims to have a more comprehensive understanding of the pathophysiology of the occurrence and development of joint synovial lesions. As a non-invasive imaging method to assess the condition of the disease, DL methods excavates the deep features contained in the image, quantifies the joint synovial lesions, and then gives more information to the clinician in the diagnosis and differential diagnosis of the joint synovial lesions, provide important information for the planning of individualized treatment plans for patients with joint synovial diseases.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Control
- 时间视角
- Prospective
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Patients diagnosed with joint synovial disease through radiological examination, arthroscopy or pathological biopsy of the joint, or whose clinical manifestations meet the diagnostic criteria of the American College of Rheumatology (ACR) for joint synovial disease.
- •Patients received pre-treatment MR.
排除标准
- •Patients who have received surgery, medication or other systemic treatment before standardized MRI scan.
- •Poor image quality.
- •Articular hemorrhage.
结局指标
主要结局
Patient's diagnosis
时间窗: 2019-2022
Type of synovitis disease in patients with a clear comprehensive diagnosis
次要结局
未报告次要终点