全民对人工智能的评估和放射科医生对查性乳房图片的评估
Johanne Kühl1, Mohammad Talal Elhakim2,3, Sarah Wordenskjold Stougaard1
1Department of Clinical Research, University of Southern Denmark, Kløvervænget 10, 2ndfloor, 5000, Odense C, Denmark.
European radiology
|November 8, 2023
概括
独立的人工智能 (AI) 在特定性相匹配时,在检测乳腺癌方面表现出与人类放射科医生的准确性相美. 虽然人工智能识别了不同的癌症组合,但它的整合可以提高查计划中的检测率.
科学领域:
- 放射学和医学成像学 医学成像学
- 医疗保健中的人工智能
- 在瘤学瘤学.
背景情况:
- 乳房扫描查对于早期发现乳腺癌至关重要.
- 目前的查依赖于放射科医生的双重阅读,这是一个资源密集的过程.
- 人工智能系统有可能提高选效率和准确性.
研究的目的:
- 验证一个人工智能 (AI) 系统用于独立的乳腺癌检测.
- 为了比较人工智能系统的性能与大规模查人口中的第一阅读乳腺放射科医生.
- 评估人工智能替代或支持人类读者在乳房镜查中的可行性.
主要方法:
- 从2014年8月到2018年8月,对249,402次乳房镜检查进行了回顾性分析.
- 查被双读放射科医生评估,并进行了仲裁.
- 应用了两个AI截止点 (AI_sens和AI_spec),与第一个读者的灵敏度和特异性相匹配.
- 关键准确度指标包括灵敏度,特异性,PPV,NPV和召回率.
主要成果:
- 与第一次阅读者相比,AI_sens显示了较低的特异性和PPV,以及较高的回忆率.
- 在所有措施中,AI_spec表现出与初读者相比的准确性.
- 无论是AI_sens还是AI_spec都检测到屏幕检测癌症的数量较少,但比第一读者检测的间隔癌症更多.
- 与放射科医生相比,人工智能系统发现了不同的癌症组成.
结论:
- 当特异性匹配时,独立人工智能可以达到与第一读放射科医生相当的准确性.
- 人工智能和放射科医生检测出不同的乳腺癌谱.
- 将人工智能整合到查中可以提高癌症检测率,特别是对于间隔癌症,并设置适当的值.
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