新型人工智能胸部X射线诊断:在临床常规中对与人类医生达成协议的质量评估
Wolfram A Bosbach1,2, Luca Schoeni1, Jan Felix Senge3,4
1Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
概括
两个人工智能 (AI) 系统显示出诊断协议,与放射科医生对常见胸部X射线发现的诊断协议相当. 然而,人工智能性能在罕见病理和卧底X射线下降,这凸显了需要持续的人类监督的需要.
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:
- 对放射学服务的需求不断增加,需要创新的解决方案来提高诊断质量和效率.
- 对人类放射科医生进行人工智能 (AI) 系统的评估对于临床整合至关重要.
研究的目的:
- 评估在常规临床实践中两个商业AI胸部X射线系统和人类放射科医生的诊断协议.
- 为了比较人工智能系统对常见与罕见的胸部病理和不同患者位置的性能.
主要方法:
- 从瑞士大学医院拍摄的279张胸部X射线的回顾性分析.
- 对七种胸部病理的评估:心脏壮大,巩固,中质,结节,肺部溢液,肺胸和肺.
- 将AI报告 (Rayvolve和ChestView) 与放射科医生的常规报告进行比较,使用基于Python的性能指标和协议措施 (Gwet的AC1,平衡精度,MCC).
主要成果:
- 放射科医生和人工智能系统之间的中等到几乎完美的协议 (Gwet的AC1: 0.47-0.96).
- 均衡的准确度在0.67-0.85之间,在流方面达到峰值.
- 与卧卧研究相比,站立式放射学研究的同意率更高;对于罕见的病理 (中质,结节) 的同意率更低.
- 人工智能系统在整合和多流量方面表现出实质性的系统间协议.
结论:
- 商业AI胸部X射线系统的性能与放射科医生对于常见的胸部病理的性能相当.
- 诊断协议受患者的位置 (站立优于卧姿) 和病理罕见性的影响.
- 人类监督仍然是必不可少的,因为AI的局限性与不常见的发现和卧床研究.
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