人工智能可以在CXR图像上生成诊断报告以获得放射科医生的批准吗? 多个阅读器和多个案例观察员的性能研究
Lin Guo1, Li Xia1, Qiuting Zheng2
1Shenzhen Zhiying Medical Imaging, Shenzhen, Guangdong, China.
Journal of X-ray science and technology
|October 18, 2024
概括
这项研究表明,人工智能系统 (MOM-ClaSeg) 在胸部X射线 (CXR) 中显著提高了肺部异常检测的准确性和效率. 放射科医生的AI辅助单读比传统的双读更有效.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医学成像分析 医学成像分析
背景情况:
- 在异质胸部X射线 (CXR) 图像中检测肺部异常,并生成报告是具有挑战性和耗时的.
- 目前的方法往往需要放射科医生的大量努力和时间.
研究的目的:
- 评估一种新型人工智能系统MOM-ClaSeg在提高放射科医生检测肺部异常的精度和效率方面的有效性.
- 为了比较人工智能辅助的单个放射科医生阅读与传统的双重阅读,使用多个阅读器,多个案例 (MRMC) 研究设计.
主要方法:
- 来自12家医院的36000多张CXR图像的回顾性收集.
- 两种阅读方法的比较:由两位放射科医生 (对照组) 进行的传统双重阅读与由人工智能生成的报告 (实验) 增强的单个放射科医生阅读.
主要成果:
- 与双重阅读相比,人工智能辅助的单读显著提高了1.49%的诊断准确度和10.95%的灵敏度 (P < 0.001).
- 诊断特异性显示出最小的,非显著的差异 (0.22%,P = 0.255).
- 平均图像读取和诊断时间在人工智能辅助下减少了54.70% (P < 0.001).
结论:
- MOM-ClaSeg人工智能系统可以充当"第一个读者",提高放射科医生的性能.
- 与CXR解释的传统双读相比,人工智能辅助的单读实现了更高的诊断准确性和效率.
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