基于深度学习的算法用于对比增强CT的偶发性肺栓塞的自动检测:一个多中心多供应商研究.
Hana Farzaneh1, Jacqueline Junn2, Yasmina Chaibi3
1Department of Radiology, Massachusetts General Hospital, Boston, MA, 02114, United States.
Radiology advances
|October 8, 2025
概括
一个人工智能工具在CT扫描上准确地检测到偶发性肺栓塞 (iPE),准确率为90%. 这种人工智能解决方案提供了快速的结果,有可能提高临床实践中的检测速度和准确性.
科学领域:
- 放射学 放射学是一门学科.
- 医疗成像医学成像
- 人工智能在医学中的应用
背景情况:
- 偶发性肺栓塞 (iPE) 越来越多地被检测到在对比增强计算机断层扫描 (CECT) 扫描中,用于其他指示.
- 人工智能有可能提高iPE的检测准确性和及时性.
- 由于诊断成像研究的首要重点,可能会出现IPE报告不足的情况.
研究的目的:
- 评估用于检测iPE的独立AI解决方案的诊断性能和选效率.
- 评估AI在最初未用于PE评估的CECT考试中识别iPE的能力.
主要方法:
- 基于深度学习的软件 (CINA-iPE) 用于分析疑似iPE的CET图像.
- 从5个临床中心收集了回顾性CECT,形成了一个平衡的数据集.
- 一个参考标准是由三个美国董事会认证的放射科医生建立的.
主要成果:
- 人工智能实现了87.8%的灵敏度和92.0%的IPE检测特异性,整体准确率为90.0%.
- 该系统在1.5分钟内处理了结果,从而实现了快速通知.
- 放射科医生之间的分歧发生在50%的假阳性病例和45.5%的错过的PE病例中,通常涉及复杂的发现.
结论:
- 在非PE CECT研究中,CINA-iPE应用程序在识别偶然PE时表现出很高的准确性.
- 人工智能处理结果的快速可用性可以帮助优先解释,并可能增强iPE检测.
- 人工智能工具有望提高偶然肺栓塞检测的准确性和速度.
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