一个用于大动脉紧急情况的自动查模型,使用卷积神经网络和剪切的计算机断层扫描,对大动脉血管图像进行剪切
Tomoki Wada1, Masamichi Takahashi2, Hiroki Matsunaga3
1Department of Radiology, Tokyo Metropolitan Bokutoh Hospital, 4-23-15 Kotobashi, Sumida-ku, Tokyo, Japan. towada-tky@umin.ac.jp.
概括
一种新的深卷积神经网络 (DCNN) 模型准确地选计算机断层扫描 (CTA) 扫描大动脉紧急情况. 这种人工智能工具有助于快速诊断和对关键大动脉疾病进行分拣.
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
- 心血管成像 - 心血管成像
背景情况:
- 像剖析和破裂这样的大动脉紧急情况需要快速诊断,以防止患者的病情恶化.
- 计算机断层扫描血管造影 (CTA) 对于诊断这些疾病至关重要.
- 当前的诊断工作流可以耗时,延迟关键干预.
研究的目的:
- 在CTA扫描上使用深卷积神经网络 (DCNN) 算法引入和评估用于大动脉紧急情况的新型自动查模型.
- 将拟议的DCNN模型 (模型A) 与常规模型 (模型B) 在识别大动脉紧急情况方面的性能进行比较.
主要方法:
- 开发了Model A,它首先在CTA图像中定位了大动脉,然后分析了切割的大动脉部分的病变.
- 开发了Model B,该模型直接评估大动脉损伤的CTA原始图像.
- 训练并测试了两种模型的数据集,分别是216和220次CTA扫描.
主要成果:
- 与模型B (0.972) 相比,模型A实现了0.995的曲线下面面积 (AUC) 在患者级别上对大动脉紧急情况的分类.
- 模型A在分类上升大动脉紧急情况方面也表现出强的表现,AUC为0.971.1.
- 该DCNN模型在识别患有关键大动脉疾病的患者方面表现出卓越的准确性.
结论:
- 基于DCNN的模型有效地选CTA扫描大动脉紧急情况,证明高诊断准确度.
- 这项技术可以促进CT扫描计算机辅助分拣系统的开发.
- 优先考虑紧急病例,并为有生命危险的大动脉疾病的患者提供更快的反应.
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