A Multi-center Study of Breast Mass Screening and Diagnosis Using Deep Learning AI-based on Real-time Ultrasound Examination
试验速览
- 阶段
- 不适用
- 入组人数
- 1,122
- 试验地点
- 1
- 主要终点
- Diagnostic performance of breast mass using deep learning AI-based real-time ultrasound examination
研究概览
简要总结
This multi-center study intends to evaluate the value of the detection and differential diagnosis of breast mass using deep learning AI-based real-time ultrasound examination.
详细描述
As the most common cancer expected to occur all over the world, extensive population screening plays a very important role in the early diagnosis and prognosis of the breast cancer. X-ray and ultrasound are the most commonly used screening methods, and ultrasound is especially important for Asian women with dense breasts. However, ultrasound is greatly affected by the operator's skill and experience, and the diagnostic accuracy varies greatly.
Artificial intelligence (AI) is a new method emerging in recent years, active in many medical fields and can effectively improve the diagnostic efficiency. However, previous researches on the application of AI in ultrasound are focused on single or multi-modality static ultrasound images. This multi-center study intends to evaluate the value of the detection and differential diagnosis of breast mass using deep learning AI-based real-time ultrasound examination.
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Prospective
入排标准
- 性别
- Female
- 接受健康志愿者
- 否
入选标准
- •Females who undergo ultrasound examination for a complaint of breast lesion;
- •The breast lesion that will obtain definite pathological diagnosis or follow-up at least two years.
排除标准
- •The breast lesion that has received CNB or FNA;
- •The breast cancer patient who has received neoadjuvant chemotherapy.
结局指标
主要结局
Diagnostic performance of breast mass using deep learning AI-based real-time ultrasound examination
时间窗: 12 months
Pathology as a gold standard, to evaluate the diagnostic performance (sensitivity, specificity and accuracy)
次要结局
未报告次要终点