深度学习在乳房学中的可复制性和可解释性:对文献的系统性审查
Deeksha Bhalla1, Krithika Rangarajan1, Tany Chandra1
1Department of Radiodiagnosis, All India Institute of Medical Sciences, New Delhi, India.
The Indian journal of radiology & imaging
|June 24, 2024
概括
深度学习在乳腺癌检测中显示出有前途的潜力,但大多数研究缺乏可重现性和可解释性. 未来的研究需要可重现的临床评估和透明的AI模型.
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:
- 对于乳房镜的深度学习有丰富的文献,但质量和可重复性差异很大.
- 评估现有的深度学习研究对乳腺癌检测的质量对于推动该领域的发展至关重要.
研究的目的:
- 评估已发表的深度学习文献的可复制性,以检测乳腺癌在乳房摄影中.
- 在这个领域中确定设计深度学习模型的最佳实践.
主要方法:
- 搜索了PubMed和Scopus的研究,使用深度学习在乳房镜中进行病变检测或分类.
- 开发并应用了经过修改的诊断准确性研究质量评估 (mQUADAS-2) 工具来评估研究质量.
- 记录了研究的关键细节,包括数据集,模型架构和性能指标 (AUC,灵敏度,特异性).
主要成果:
- 在12123个记录中,107个研究符合纳入标准;103个由于患者选择而存在偏差的高风险.
- 只有四项研究具有足够的质量,其中三项使用定制模型,一项使用商业网络.
- 结合放射学家和人工智能读数的AUC (0.955) 和特异性 (98.5%) 比单个评估更高;没有研究提供了超出局部化的解释.
结论:
- 深度学习对改善乳房影像解释有很大的潜力.
- 对深度学习模型进行可重复的临床评估至关重要.
- 开发可解释的人工智能网络对于临床采用和信任至关重要.
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