一个人工智能算法用于多色计算机断层扫描的肺栓塞检测:在虚拟单色图像上的性能
Eline Langius-Wiffen1, Ingrid M Nijholt2, Rogier A van Dijk2
1Department of Radiology, Isala Hospital, Dr. Van Heesweg 2, 8025 AB, Zwolle, The Netherlands. elinelangius@gmail.com.
European radiology
|August 5, 2023
概括
在传统的多色色计算机断层扫描 (CT) 图像上训练的人工智能 (AI) 算法在检测肺栓塞 (PE) 时,与虚拟单色图像 (VMI) 的性能相当. 这一发现支持AI在CT成像中的持续使用,尽管技术进步.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 虚拟单色图像 (VMI) 在CT扫描中提高了对比度和噪声比.
- 从传统的多彩CT图像 (CPI) 转向VMI,AI的性能可能会受到影响.
- 已建立的AI算法需要对新的成像技术进行验证.
研究的目的:
- 评估人工智能算法用于肺栓塞 (PE) 检测的诊断性能.
- 为了比较人工智能算法在传统多色图像 (CPI) 和虚拟单色图像 (VMI) 上的性能.
- 评估在较旧的CT数据上训练的AI可靠性,以便使用较新的VMI技术.
主要方法:
- 对114名疑似PE患者的回顾性分析,使用从光谱CT获得的VMI和CPI配对.
- 一个成熟的AI算法被用于将CT肺血管造影 (CTPA) 扫描分类为PE的阳性或阴性.
- 诊断准确度指标 (灵敏度,特异性,预测值,概率比率) 在VMI和CPI之间进行了比较,使用了一个全面的参考标准.
主要成果:
- 人工智能算法在CPI和VMI上表现出可比的诊断准确性.
- 对于PE检测的灵敏度在CPI上为77.5%,在VMI上为85.0% (p=0.08).
- 在CPI上,特异性为96.0%,在VMI上为94.6% (p=0.32),没有发现显著差异.
结论:
- 在CPI上训练的AI算法的诊断性能在应用到VMI时没有显著下降.
- 这表明,商业上可用的AI算法可以安全地用于VMI,支持CT成像中的AI可持续性.
- 建议在临床实践中进行持续监测,尽管有令人放心的发现.
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