基于深度学习和放射学的机器学习方法的比较,用于在低剂量计算机断层扫描图像上识别慢性阻塞性肺病
Yu Guan1, Di Zhang1, Xiuxiu Zhou1
1Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China.
Quantitative imaging in medicine and surgery
|March 28, 2024
概括
使用放射学特征和临床数据的机器学习模型在识别慢性阻塞性肺部疾病 (COPD) 方面表现优异,与CT图像上的深度学习相比. 这表明ML对COPD检测更有益.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 肺部病理学 肺部病理学
背景情况:
- 慢性阻塞性肺病 (COPD) 诊断依赖于各种方法,放射学和人工智能 (AI) 是有前途的.
- 深度学习 (DL) 和基于放射学的机器学习 (ML) 对于使用低剂量计算机断层扫描 (LDCT) 预测COPD的比较性能仍然不清楚.
研究的目的:
- 建立和比较五种不同的预测模型在LDCT图像上识别COPD的性能.
- 确定最佳方法 (DL与ML) 和COPD检测的特征集.
主要方法:
- 使用LDCT扫描对1024名受试者 (169名COPD患者,855名对照人) 的回顾性分析.
- 开发了五种模型:用放射性物质进行ML (模型1),用放射性物质,肺密度和临床数据进行ML (模型2),用CT图像进行DL (模型3),用CT图像,肺密度和临床数据进行DL (模型4),以及用CT图像,放射性物质,肺密度和临床数据进行DL (模型5).
- 使用准确度,灵敏度,特异性,NPV,PPV和AUC的模型性能与使用DeLong测试的AUC进行比较.
主要成果:
- 模型2利用ML与放射学,肺密度和临床数据,在测试组中实现了最高的AUC (0.73).
- 模型2也显示了最高的灵敏度 (0.84) 和精度 (0.81).
- 模型2显著优于模型1 (P=0.03),这表明结合特征的好处.
结论:
- 结合基于CT的放射学特征,肺密度参数和人口/临床特征的机器学习模型优于仅基于COPD识别的CT图像的深度学习模型.
- 在准确识别COPD方面,ML方法更适合和有益.
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