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基于图像的癌症识别中的人工智能性能:系统性审查的总体审查

He-Li Xu1,2,3, Ting-Ting Gong4, Xin-Jian Song1,2,3

  • 1Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China.

Journal of medical Internet research
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人工智能 (AI) 显示出癌症诊断的前景,但临床实施需要进一步的研究. 这一总体审查批判性地评估了AI.

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人工智能的人工智能是人工智能.生物医学成像成像技术癌症的诊断 癌症的诊断进行元分析.系统性审查 系统性审查雨评价 雨评价

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 在瘤学瘤学.

背景情况:

  • 人工智能 (AI) 在癌症诊断方面提供了变革性的潜力,旨在改善患者的治疗结果.
  • 医学成像中的AI正在迅速发展,需要对其诊断能力进行全面评估.
  • 这项研究解决了对人工智能用于癌症成像诊断的证据的关键综合的需要.

研究的目的:

  • 进行总体审查,总结和批判性地评估基于AI的癌症成像诊断证据.
  • 评估各种癌症类型的AI算法诊断性能和证据质量.
  • 确定人工智能驱动的癌症诊断领域的障碍和未来研究领域.

主要方法:

  • 在主要数据库 (PubMed,Embase,Web of Science,Cochrane,IEEE) 中系统地搜索相关的系统审查.
  • 使用Joanna Briggs Institute (JBI) 批判性评估清单进行数据抽象和质量评估.
  • 使用推,评估,开发和评估 (GRADE) 标准的评级证据质量评估;诊断性能数据的叙述综合.

主要成果:

  • 分析了158项评估AI在8种主要癌症类型的非侵入性成像诊断中的研究.
  • 对于中枢神经系统癌症 (48%-100%) 的精度可变;在其他癌症部位的表现一致.
  • 大多数元分析显示了积极的总结性能,食道,乳腺和卵巢癌的显著范围;肺癌显示了较低的聚合特异性 (65%-80%).
  • 高质量 (JBI) 在80.4%的研究中,但整体GRADE评估表明证据质量中等至低.

结论:

  • 人工智能显示出加速,准确和客观的癌症诊断的巨大潜力.
  • 临床实施仍然存在障碍,需要研究人员,临床医生和政策制定者的共同努力.
  • 将人工智能的潜力转化为改善患者的治疗结果和医疗保健提供需要协作行动.