基于深度学习的计算机辅助诊断在冠状动脉中,以计分的CT检测肺结节:初步研究
Seung Yun Lee1, Ji Weon Lee1, Jung Im Jung1
1Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
Yonsei medical journal
|March 26, 2025
概括
深度学习计算机辅助诊断 (DL-CAD) 显著提高了对冠状动脉 (CAC) 计分CT扫描的肺结节检测灵敏度. 这种人工智能工具可以提高诊断准确度,但不会大幅增加假阳性.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 肺部医学 肺部医学
背景情况:
- 冠状动脉 (CAC) 评分计算机断层扫描 (CT) 扫描主要用于心血管风险评估.
- 偶然的肺结节可能会在CAC得分的CT扫描中检测到,需要准确的识别.
- 基于深度学习的计算机辅助诊断 (DL-CAD) 提供了提高结节检测的潜力.
研究的目的:
- 评估DL-CAD的可行性和临床实用性,用于检测CAC得分CT上的肺结节.
- 评估DL-CAD对初级放射科医生的诊断绩效的影响.
主要方法:
- 对273名接受CAC得分CT的患者进行了回顾性分析.
- 采用DL-CAD系统使用薄截面图像用于结节检测.
- 两个初级读者审查了带有和没有DL-CAD辅助的扫描,与经验丰富的放射科医生的共识小组进行了比较.
主要成果:
- DL-CAD显著提高了检测肺结节的阅读器灵敏度 (例如,阅读器1:65%至80%,p<0.001).
- 在DL-CAD的帮助下,没有观察到每例虚假阳性率的显著增加.
- 每个患者的敏感性也显示了DL-CAD显著改善 (读者1: 73%至84%,p<0.001).
结论:
- 应用于薄截面图像的DL-CAD有效地帮助不那么有经验的读者在CAC评分CT上识别肺结节.
- 该系统提高了检测灵敏度,同时保持了可接受的假阳性率.
- 这项技术有望提高心脏CT上偶然肺部发现的诊断产量.
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