利用深度学习和计算机断层扫描来确定非结核性菌根肺病患者的肺结节活动
Andrew C Lancaster1,2, Mitchell E Cardin1, Jan A Nguyen1
1Division of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD.
Journal of thoracic imaging
|April 19, 2024
概括
深层卷积神经网络 (DCNN) 在CT扫描上有效地将急性和慢性肺结节分类为非结核性菌根性肺病 (NTM-LD). 该DCNN模型表现出与放射科医生相当的性能,为改善NTM-LD诊断和管理提供了潜力.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 非结核菌肺病 (NTM-LD) 的诊断可能是具有挑战性的.
- 分类肺结节活动 (急性与慢性) 对于NTM-LD管理至关重要.
- 计算机断层扫描 (CT) 是NTM-LD的主要成像方式.
研究的目的:
- 开发和评估一个深层卷积神经网络 (DCNN),用于NTM-LD中分类急性和慢性肺结节.
- 将DCNN模型的性能与人类放射科医生的性能进行比较.
主要方法:
- 来自CT扫描的650个NTM-LD肺结节的数据集进行了策划.
- 在2DCT图像上训练了一个11层DCNN模型.
- 用ROC曲线下的面积,灵敏度,特异性和精度来评估模型性能,并与3名放射科医生进行比较.
主要成果:
- DCNN模型的AUC为0.806,具有76%的灵敏度,68%的特异性和72%的准确性.
- 模型的性能与放射科医生的平均性能相当.
- 放射科医生的AUC范围在0.693到0.771.7之间.
结论:
- 一个DCNN模型是可行的分类NTM-LD肺结节活动在CT.
- DCNN模型的诊断性能与经验丰富的放射科医生相美.
- 这种由人工智能驱动的方法显示了提高NTM-LD诊断和患者管理的前景.
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