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PACS集成的机器学习乳腺密度分类器:临床验证.

John Lewin¹, Sven Schoenherr², Martin Seebass²

¹Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States of America.

Clinical imaging

|July 8, 2023

概括

此摘要是机器生成的。

一个新的人工智能工具准确地评估了乳房密度,显示了与放射科医生的高度一致. 这种机器学习模型预测了乳腺成像报告和数据系统 (BI-RADS) 的密度类别,有助于乳房图像分析.

关键词:

人工智能的人工智能是人工智能.乳腺密度乳腺密度乳房学乳房学乳房学

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科学领域:

放射学和医学成像学医学成像学
医疗保健中的人工智能
机器学习应用机器学习应用

背景情况:

准确的乳腺密度评估对于乳房扫描解释和乳腺癌风险分层至关重要.
目前的方法依赖于主观放射科医生的解释,导致观察者之间的变化.
新的AI工具为客观和一致的乳腺密度分类提供了潜力.

研究的目的:

评估基于机器学习的新型工具的性能,用于预测乳腺成像报告和数据系统 (BI-RADS) 的乳腺密度.
在不同的临床环境中,评估AI工具与放射科医生共识之间的准确性.

主要方法:

一个卷积神经网络在一个学术医疗中心 (Site A) 的33000次乳房扫描检查中受过训练.
人工智能工具的性能在来自A站的500项研究和来自第二个学术医疗中心 (B站) 的700项研究的单独数据集上得到验证.
放射学家的共识作为两个站点的绩效评估的基础真理.

主要成果:

人工智能分类器在四类BI-RADS密度分类中实现了84.6% (Site A) 和89.7% (Site B) 的准确性.
对于二进制分类 (密集与非密集),准确率为94.4% (A站) 和97.4% (B站).
人工智能分类器从来没有不同意超过一个密度类别的共识阅读.

结论:

自动化乳腺密度工具与专家放射科医生的评估有很高的一致性.
这种由人工智能驱动的工具显示出了提高乳房密度评估在乳房摄影中的一致性和准确性的巨大潜力.