用超声波评估甲状腺结节的可学习性:使用大数据集
Jiyoung Yoon1, Eunjung Lee2, Hye Sun Lee3
1Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea.
Ultrasound in medicine & biology
|September 27, 2023
概括
大量的超声波图像数据集改善了初学者对甲状腺结节的评估技能. 人工智能计算机辅助诊断 (AI-CAD) 进一步提高了所有读者的诊断性能,无论经验水平如何.
科学领域:
- 放射学 放射学是一门学科.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 通过超声波 (US) 评估甲状腺结节对于区分良性和恶性病例至关重要.
- 培训新手阅读者需要有效的方法和工具来实现诊断能力.
研究的目的:
- 为了评估甲状腺结节解释的可学习性,使用大量美国图像数据集.
- 评估AI-CAD对读者在甲状腺成像方面的不同经验水平的诊断实用性.
主要方法:
- 六名大学新生接受了训练,使用大量的甲状腺结节数据集 (13,560张图像) 进行训练.
- 在单独的测试组 (282张图像) 上,评估了训练前和训练后的诊断性能.
- 阅读者的表现与经验丰富的放射科医生进行了比较,并通过AI-CAD协助进一步评估.
主要成果:
- 大多数学生在培训课程后显示出更好的诊断性能.
- 虽然经验丰富的读者最初表现优于学生,但五名学生取得了与一名居民相当的成绩.
- 对于一些学生来说,AI-CAD显著提高了灵敏度,准确度和AUROC,并改善了所有经验丰富的阅读者的性能.
结论:
- 用大数据美国图像集进行自学可以补充甲状腺结节评估的传统培训.
- 在甲状腺成像中,AI-CAD提供了一种有价值的工具,可以增强不同经验水平的读者的诊断能力.
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