使用可解释的人工智能来描述Mirai乳腺癌风险预测模型中的特征
Contact us if these videos are not relevant.
Contact us if these videos are not relevant.
在PubMed上查看摘要
概括
此摘要是机器生成的。人工智能 (AI) 工具Mirai识别了乳腺化,以改善病变检测和癌症风险预测. 可以解释的人工智能证实Mirai从特定的化特征中学习, 提高诊断能力.
科学领域
- 放射学
- 人工智能
- 医学成像
背景情况
- 乳房扫描对于乳腺癌查至关重要.
- 人工智能工具正在开发中,
- 了解人工智能功能相关性是临床整合的关键.
研究的目的
- 评估Mirai的提取特征是否与乳房摄影观察一致.
- 确定这些特征是否有意义地对癌症风险进行预测.
- 评估AI识别的特征的临床相关性.
主要方法
- 从EMBED数据集中对29,374张乳房图进行了回顾性分析.
- 使用以特征为中心的可解释AI管道来评估512个Mirai特征.
- 使用接收器操作特征曲线下的面积 (AUC) 进行损伤检测和风险预测.
主要成果
- 与只有化 (CalcMirai) 或只有质量 (MassMirai) 的模型相比,Mirai在病变检测方面表现出更好的表现.
- 在Mirai和CalcMirai之间没有发现5年癌症风险预测的显著差异.
- 与Mirai相比,MassMirai在风险预测方面表现较差.
结论
- 可以解释的AI证实Mirai隐含地识别了乳房病变特征,尤其是化.
- Mirai利用化特征的能力对于病变检测和风险预测都是有价值的.
- 这项研究证实了人工智能提取的特征在乳房镜中的临床相关性.
相关概念视频
Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...