深度学习用于在腹部CT上自动识别肠道阻塞.
Quentin Vanderbecq1,2, Maxence Gelard3, Jean-Christophe Pesquet3
1Department of Radiology, AP-HP.Sorbonne, Saint Antoine Hospital, 184 Rue du Faubourg Saint-Antoine, 75012, Paris, France. q.vanderbecq@gmail.com.
European radiology
|February 22, 2024
概括
这项研究开发了一个3D混合卷积神经网络 (CNN),在CT扫描上自动检测肠道阻塞 (BO). 人工智能模型显示出高精度和灵敏度,有可能改善放射科医生的工作流程和患者的结果.
科学领域:
- 医疗成像中的人工智能
- 放射学和诊断成像 放射学和诊断成像
- 机器学习用于疾病检测和检测
背景情况:
- 肠道阻塞 (BO) 的发生率越来越高,这给放射科医生带来了巨大的工作负载挑战.
- 对腹部计算机断层扫描 (CT) 扫描的自动化分析可以提高诊断效率和患者护理.
- 开发可靠的机器学习模型来识别BO对于及时干预至关重要.
研究的目的:
- 开发和评估一种机器学习模型,用于在腹部CT扫描中自动检测可疑的肠道阻塞 (BO).
- 评估3D混合卷积神经网络 (CNN) 的性能,用于BO的二进制分类.
主要方法:
- 使用了1345个怀疑BO患者的腹部CT扫描数据集,并使用了88个扫描数据集进行验证.
- 开发了一条预处理管道,包括腹盆区域定位和3D扫描裁剪.
- 训练和测试了几种神经网络架构,其中3D混合CNN实现了最佳性能.
主要成果:
- 在内部数据集上,3D混合CNN的F1得分为0.92和平衡精度为0.86.
- 在外部数据集上,该模型显示F1得分为0.89和平衡精度为0.89.89.
- 当对灵敏度进行校准时,该模型在内部数据集上实现了1.00的灵敏度和0.84的特异性.
结论:
- 开发的3D混合CNN显示了在腹部CT扫描中BO的自动二元分类的巨大潜力.
- 这种人工智能工具可以自动选择患者和CT优先级,从而提高放射科医生的工作流程.
- 该模型的高精度和灵敏度表明其在加快肠道阻塞诊断方面的实用性.
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