肺胸部诊断的深度学习:系统性审查和元分析
Takahiro Sugibayashi1, Shannon L Walston1, Toshimasa Matsumoto1,2
1Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan.
深度学习 (DL) 模型的诊断性能与医生用于肺胸部检测的诊断性能相似. 然而,大多数研究都有高偏差风险,这表明需要在该领域进一步进行人工智能研究.
科学领域:
- 医学成像分析 医学成像分析
- 放射学中的人工智能
- 诊断性绩效评估的诊断性绩效评估是如何进行的
背景情况:
- 深度学习 (DL) 是人工智能 (AI) 的一个子集,越来越多地用于协助肺胸部诊断.
- 之前没有进行过任何元分析来评估DL在肺胸检测方面的诊断性能.
结论:
- 对肺胸部的DL模型的诊断性能与人类医生的诊断性能相似.
- 大多数研究中存在偏差的高风险,需要谨慎地解释结果.
- 进一步的研究对于在肺胸部诊断中推进人工智能和解决已识别的局限性至关重要.
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