AI-Enhanced Analysis of Breast Density and Background Parenchymal Enhancement (BPE)
试验速览
- 阶段
- 不适用
- 状态
- Enrolling By Invitation
- 发起方
- 入组人数
- 213
- 试验地点
- 1
- 主要终点
- Correlation between breast density, BPE, and age using AI-driven analysis.
研究概览
简要总结
This study expands upon previous research investigating the correlation between breast density, Background Parenchymal Enhancement (BPE), and age in contrast-enhanced mammography (CEM). By integrating Artificial Intelligence (AI) methodologies, including Artificial Neural Networks (ANNs) and deep learning models, the study aims to optimize the accuracy of predictions and validate prior findings obtained through multiple linear regression.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- Female
- 接受健康志愿者
- 否
入选标准
- 未提供
排除标准
- •Patients with prior breast cancer treatment that could alter BPE.
- •Incomplete imaging or missing classification data.
- •Contraindications to contrast-enhanced imaging.
结局指标
主要结局
Correlation between breast density, BPE, and age using AI-driven analysis.
时间窗: Data analysis within 12 months of study completion.
Evaluating whether AI models, including neural networks, can enhance prediction accuracy for BPE assessment compared to conventional multiple linear regression.
次要结局
- AI-based optimization of breast density and BPE classification(Within 12 months of study completion)
- Comparative performance of multiple linear regression vs. AI models.(Within 12 months of study completion.)
- Mean Squared Error (MSE) and explained variance in predictive models(Within 12 months of study completion)
研究者
Graziella di Grezia
Principal Investigator
Link Campus University