人工智能在胸部放射图中的诊断性能,来自急诊部的转诊
Julia López Alcolea1, Ana Fernández Alfonso1, Raquel Cano Alonso1
1Hospital Universitario QuironSalud Madrid, 28223 Madrid, Spain.
Diagnostics (Basel, Switzerland)
|November 27, 2024
概括
这项研究评估了人工智能软件和放射科医生解释胸部X射线. 虽然AI表现有希望,但它在肺结节方面扎,突出了AI性能改善的领域,用于胸部X射线分析.
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:
- 将AI集成到胸部X射线评估中,有可能改善诊断和工作流程.
- 在临床环境中对人工智能的现实性能评估至关重要.
研究的目的:
- 为了评估AI软件 (Arterys MICA v29.4.0) 的灵敏度 (Se) 和特异性 (Sp),与解释急诊室 (ED) 胸部X射线的放射科医生进行对比.
- 建立高级放射科医生的评估作为黄金标准 (GS),并比较AI-居民一致性,可疑病例频率和AI检测未经训练的变量.
主要方法:
- 784张ED胸部X射线的回顾性观察研究.
- 训练有素的人工智能可以检测五种类型:肺结节,不透明性,肺,肺胸和骨折,具有安全标签.
- 人工智能和居民绩效与黄金标准的比较.
主要成果:
- 在AI和住院患者中,对骨折和肺胸部的高敏感性 (100%).
- 对肺不透明度的中度敏感性 (AI:76%,居民:71%) 和可接受的肺部溢出 (AI:60%,居民:67%).
- 与居民 (75%) 相比,AI对肺结节的敏感性较低 (33%);AI报告了更多可疑的病例,大部分是GS负面的.
- 在AI和居住者之间相当一致 (Kappa=0.3),在肺部溢液方面中等 (Kappa=0.5). 人工智能错过了16-20%的发现,比如中椎异常和手术材料.
结论:
- 人工智能在检测初级胸部X射线发现方面具有实用性,除了肺结节.
- 高负预测值 (NPV) 表明AI在选应用中的潜力.
- 建议进一步培训人工智能和扩大检测能力,以提高临床适用性.
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