基于计算机断层扫描 (CT) 在良性和恶性肺结节中的密度的人工智能诊断价值:回顾性调查
Wei Fan1, Huitong Liu2, Yan Zhang1
1Department of Radiology, Shaanxi Provincial People's Hospital, Xi'an, China.
PeerJ
|January 8, 2024
概括
与医生相比,人工智能 (AI) 在检测肺结节 (PNs) 中表现出更高的灵敏度. 这种人工智能驱动的计算机断层扫描 (CT) 分析为早期肺癌检测和患者管理提供了更高的准确性.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 肺结节 (PNs) 需要准确的检测和表征,以区分良性和恶性类型.
- 计算机断层扫描 (CT) 是用于PN检测的主要成像方式.
- 区分良性PN和恶性PN仍然是一个临床挑战,影响患者管理.
研究的目的:
- 评估人工智能 (AI) 在使用CT密度检测和管理良性和恶性肺结节 (PNs) 的诊断性能.
- 为了比较AI的诊断准确性与医生在PN特征化中的诊断准确性.
主要方法:
- 对130名病理确诊PNS患者的回顾性分析.
- 人工智能软件分析了PNS的CT图像,医生也解释了相同的图像.
- 病理诊断是评估AI和医生的表现的黄金标准.
主要成果:
- 人工智能在PN检测方面实现了94.69%的灵敏度,超过了医生的灵敏度 (85.40%).
- 人工智能的查能力在统计上优于医生读数 (p < 0.05).
- 人工智能和医生读数显示了可比的诊断值,曲线下的面积 (AUC) 分别为0.798和0.814.
结论:
- 基于人工智能的CT诊断显示,它有望提高早期肺癌检测的精度.
- 人工智能可以提供更准确的证据来支持PN的临床管理决策.
- 这项研究表明,人工智能可以提高肺结节诊断的准确性.
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