在3D TOF-MRA上检测内动脉瘤的联合双阶段卷积神经网络
Yuxi Zhou1, Yifeng Yang1, Ting Fang1
1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, People's Republic of China.
Physics in medicine and biology
|August 22, 2023
概括
一个新的AI模型,JSCD-Net,有效地使用3DMRI扫描检测内动脉瘤,显示高灵敏度和低假阳性率,以帮助放射科医生.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 神经学 神经学
背景情况:
- 内动脉瘤对健康构成重大风险.
- 准确和高效的检测对于及时干预至关重要.
- 当前的诊断方法可能耗时且容易出现错误.
研究的目的:
- 开发一个3D卷积神经网络,用于自动化内动脉瘤检测.
- 提高放射科医生的效率和简化临床工作流程.
- 为了自动评估动脉瘤的位置和程度.
主要方法:
- 一项回顾性研究使用了3D飞行时间磁共振血管学图像.
- 建议建立一个联合细分和分类网络 (JSCD-Net).
- JSCD-Net使用U-Net++进行预检测,并联合训练多路径网络以最大限度地减少假阳性.
主要成果:
- 在内部数据上,JSCD-Net实现了91.2%的灵敏度,3.55个假阳性/扫描.
- 在外部数据上,灵敏度为97.2%,错误阳性/扫描结果为2.7.
- 该模型在内部和外部数据集中显示出强大的性能.
结论:
- 拟议的JSCD-Net模型显示了对内动脉瘤检测的高灵敏度.
- 它提供了一个合理的假阳性率,表明临床效用.
- 这种人工智能工具有可能显著帮助对内动脉瘤的临床诊断.
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