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结合图像相似性和预测性人工智能模型,降低甲状腺结节诊断中的主观性,改善恶性癌症预测.

Govind Nair1, Aishwarya Vedula2, Ethan Thomas Johnson3

  • 1Saint Louis University Medical Scholars Program, Saint Louis University, Saint Louis, Missouri.

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概括
此摘要是机器生成的。

这项研究表明,人工智能 (AI) 工具有效地风险分层甲状腺结节,可能减少不必要的细针向. 人工智能在数据集中表现出强的表现,与ACR TI-RADS分数相对应得很好.

关键词:
在这里,我们可以看到AIAIAI.这就是TI-RADS.图像的相似性 图像的相似性这是一种微创的侵入性疾病.预测性的AI预测性AI甲状腺癌是一种癌症.甲状腺恶性瘤是一种恶性瘤.甲状腺结节 甲状腺结节

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科学领域:

  • 医学成像分析分析 医学成像分析
  • 医疗保健中的人工智能
  • 医疗程序的诊断准确性 医疗程序的诊断准确性

背景情况:

  • 甲状腺结节很常见,需要准确的风险分层来确定需要进行细针吸收 (FNA) 等侵入性手术的必要性.
  • 目前的风险分层依赖于成像功能和评分系统,例如美国放射学学院甲状腺成像和数据系统 (ACR TI-RADS).
  • 预测性人工智能 (AI) 为提高甲状腺结节风险评估的准确性和效率提供了一个潜在的工具.

研究的目的:

  • 评估结合预测人工智能 (AI) 和图像相似性模型对甲状腺结节风险分层的有效性.
  • 评估AI应用程序在预测恶性瘤方面的表现,并与ACR TI-RADS评分系统相关联.
  • 确定人工智能工具对减少甲状腺结节诊断中细针吸入 (FNA) 的需求的潜在影响.

主要方法:

  • 通过使用两个不同的甲状腺结节超声波图像数据集进行了一项追溯的外部验证研究.
  • 这些数据集包括来自斯坦福大学的192个结节和来自私人诊所的118个结节,通过细胞学或外科病理学证实了最终的诊断.
  • 人工智能应用程序被用来预测结节的诊断,并分配一个ACR TI-RADS评分.

主要成果:

  • 在斯坦福数据集中,人工智能获得了1.0的灵敏度,0.55的特异性,0.18的PPV和1.0的NPV (AUC-ROC:0.78).
  • 在私人实践数据集中,AI表现出更高的性能,灵敏度为0.91,特异性为0.95,PPV为0.8,NPV为0.98 (AUC-ROC:0.93,精度:0.94).
  • 人工智能的ACR TI-RADS得分显示出强烈的多色相关性 (斯坦福大学的0.67,私人实践中的0.94).

结论:

  • 人工智能应用程序在两个数据集中表现出强大的灵敏度和负预测值,这表明它在排除恶性瘤方面的可靠性.
  • 人工智能工具显示,它有可能将细针吸收 (FNA) 降低61.5%,并且与ACR TI-RADS有很强的相关性,这表明诊断效率有所提高.
  • 积极预测值的变化凸显了需要在各种临床环境中进行一致的图像选择和考虑恶性瘤患病率,以便广泛实施.