用于解释查乳房影像的AI:在双重阅读实践中错过癌症的含义以及具有挑战性的病变
Zhengqiang Jiang1, Ziba Gandomkar2, Phuong Dung Yun Trieu2
1Discipline of Medical Imaging Sciences, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia. zhengqiang.jiang@sydney.edu.au.
Scientific reports
|May 24, 2024
概括
这项研究评估了人工智能 (AI) 模型,用于检测乳房影像中错过的癌症. 人工智能工具在识别双重阅读实践中忽略的微妙癌症方面表现出显著的灵敏度,这表明了提高查准确性的潜力.
科学领域:
- 医疗成像医学成像
- 医疗保健中的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 双读性乳房造影是一个标准的查实践,以尽量减少错过的癌症.
- 在现有的双重阅读协议中,将人工智能 (AI) 作为代用第二读者集成到现有双重阅读协议中的有效性仍在调查中.
- 在双读设置中,两位放射科医生都错过了微妙的癌症,这对AI检测构成了挑战.
研究的目的:
- 评估两种先进的人工智能模型 (GMIC和GLAM) 在乳房影像查计划中检测回顾性发现的遗漏癌症的有效性.
- 探索人工智能模型和放射科医生在病变定位上的一致性,考虑不同程度的放射科医生一致性.
- 评估放射科医生关于损伤位置的协议如何影响AI性能.
主要方法:
- 两个人工智能模型,全球意识多个实例分类器 (GMIC) 和全球本地激活地图 (GLAM),为这项研究进行了微调.
- 这些模型对729例癌症病例进行了评估,重点关注由三名放射科医生组成的小组追溯识别的癌症.
- 放射科医生对病变注释的一致性被分类 (几乎完美,实质性,中度,差),并使用相似性 (SIM) 和Kullback-Leibler分歧 (KLD) 度量在突出度地图上测量AI-放射科医生一致性.
主要成果:
- 大约24.82%的癌症在回顾性审查中被归类为错过.
- 人工智能模型对遗漏的癌症表现出了显著的敏感性:GMIC为82.98%和GLAM为79.79%.
- 与真实查检测的癌症相比,错过的癌症的AI灵敏度更高 (89.54%为GMIC,87.25%为GLAM),性能与更高的放射科医生一致水平正相关.
结论:
- 即使在既定的双重阅读乳房扫描实践中,人工智能工具也可能检测出人类读者错过的微妙癌症.
- 人工智能模型在识别错过的癌症方面的表现受到放射科医生在病变定位方面的清晰度和一致的影响.
- 对放射科医生来说,具有挑战性的病变也给人工智能带来了困难,凸显了对持续人工智能开发和整合战略的需求.
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