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自动化库斯对静脉瘤的分类
Aaron Kujawa1, Reuben Dorent1, Steve Connor1,2,3
1School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
Frontiers in radiology
|July 26, 2023
概括
这项研究引入了一条人工智能管道,用于自动化前庭神经瘤 (VS) 细分和MRI扫描上的Koos分类. 人工智能实现了与神经外科医生可比的准确性,潜在地改善了患者管理.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 神经外科 神经外科
背景情况:
- 库斯分级尺度对于根据瘤大小和脑干压缩来分类前庭神经瘤 (VS) 是至关重要的.
- 准确的VS分类对于有效的患者管理和治疗规划至关重要.
- 目前的手动分类方法可能耗时,并且受观察者之间的变化影响.
研究的目的:
- 开发和验证人工智能 (AI) 管道,用于自动细分和从MRI中对前置神经瘤 (VS) 的Koos分类.
- 提高临床工作流程中VS分级的效率和一致性.
- 提供一个工具,为VS患者提供更好的患者管理.
主要方法:
- 使用卷积神经网络 (CNN) 开发了一个AI管道,用于VS细分和Koos等级预测.
- 该管道使用了对比度增强的T1加权和高分辨率T2加权MR图像.
- 结合CNN和随机森林分类器的整体方法被用于分类,在手动细分的瘤和专家分配的Koos等级上进行训练.
主要成果:
- 人工智能管道在308名患者的数据集上实现了加权宏观平均F1得分为89.3%,准确率为89.3%.
- 性能指标与两名经验丰富的神经外科医生的平均表现相当 (F1得分:89.1%,准确率:88.6%).
- 观察到高的评价者间可靠性 (kappa = 0.68) 和评价者内部可靠性 (kappa = 0.95 和 0.82).
结论:
- 开发的AI框架是第一个使用Koos规模自动化VS分类的AI框架.
- 人工智能的性能与神经外科医生相当,这表明它有可能简化临床工作流程.
- 模型,代码和基本真相数据将公开发布,以支持进一步的研究和开发.
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