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人工智能对肺结节的诊断效率基于CT扫描

Wei Pan¹, Xutao Fang¹, Zhiyi Zang¹

¹Department of Cardio-Thoracic Surgery, The 904th Hospital of Joint Logistic Support Force of PLA Wuxi 214000, Jiangsu, China.

American journal of translational research

|June 12, 2023

概括

人工智能 (AI) 在CT扫描上诊断肺结节 (PNs) 显示出有希望的准确性,大大减少了阅读时间. 然而,人工智能与较低级别的PN扎,需要更大的数据集来提高诊断效率.

科学领域:

医疗成像医学成像
人工智能在医学中的应用
肺部诊断肺部诊断肺部诊断

背景情况:

肺结节 (PNs) 是计算机断层扫描 (CT) 扫描的常见发现.
准确的PNS诊断对于有效的肺癌管理至关重要.
人工智能正在越来越多地被探索,以协助医学图像解释.

研究的目的:

为了评估AI的诊断准确性与人类放射科医生相比,PNs在CT扫描上的诊断准确性.
评估性能指标,包括准确性,错误诊断,错误诊断和真负率.
为了比较AI和人类解释之间的图像阅读时间.

主要方法:

从309名参与者的360个PNS (251个恶性,109个良性) 的回顾性分析.
CT图像由人工智能和经验丰富的放射科医生进行审查.
性能指标是使用术后病理结果作为黄金标准来计算的.

主要成果:

人工智能为PNS实现了81.94%的准确率,错误诊断率为15.14%,误诊率为24.77%.
人类放射科医生证明了更高的准确性 (83.06%) 与较低的错误诊断 (22.31%) 和误诊 (4.59%) 率.
人工智能显著减少了图像读取时间 (195.4s vs 581.1s),但对低度和中度恶性PNS的准确性较低.

结论:

人工智能为放射科医生在CT扫描上进行PN诊断提供了可比的准确性,并且更快.
对于低度和中度恶性PNS,AI的诊断效率有限.
建议扩大机器学习数据集,以提高AI在检测低级肺结节的准确性.

关键词:

肺癌的CT图像扫描.人工智能的人工智能是人工智能.肺部结节肺部结节肺部结节

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