Nat Commun:基于计算机断层扫描(CT)的深度学习系统预测心血管疾病风险

2021-02-07 xiaozeng MedSci原创

心血管疾病是一类最常见的可预防的致死性疾病,在欧洲和美国的死亡率分别为45%和31%。

血管疾病是一类最常见的可预防的致死性疾病,在欧洲和美国的死亡率分别为45%和31%。目前对于该疾病可以提供有效的生活方式和药理预防,但确定最受益的人群仍是一项持续的挑战。

包括年龄和性别等传统的危险因素,在预测个人心血管疾病的准确性上仍有限。因此,急需进一步提高个体心血管疾病风险预测和分层。


已知的不良心血管事件的最强预测因素之一是冠状动脉钙化,可通过计算机断层扫描(CT)进行量化。CT冠状动脉钙化评分是衡量冠状动脉粥样硬化负担的一项指标,是最被广泛接受的心血管风险指标之一。然而,由于需要相关专业知识、时间和专业设备,因此无法对这些信息进行常规的量化。

深度学习系统相关评估步骤示意图

在该研究中,研究人员展示了一个强大且省时的深度学习系统,其可在常规心脏门控和非门控CT上自动定量冠状动脉钙离子,以准确预测心血管事件。


研究人员通过来自四个公认的前瞻性队列和随机对照试验中的20,084例样本信息进行该系统的评估,这些样本信息主要包括:来自弗雷明汉心脏研究(FHS)的健康的无症状社区住宅样本;国家肺癌筛查试验(NLST)中年龄较大的无症状重度吸烟者;在影像学评估胸痛的前瞻性多中心研究(PROMISE)中在门诊评估的有症状的稳定胸痛人群;ROMICAT-II试验中有症状的急性胸痛人群,以评估可疑的冠状动脉疾病。

深度学习系统结果可以预测钙评分

结果显示,该自动定量评分是心血管事件的一个有力预测指标,且与危险因素无关(多变量校正后风险比高达4.3),该评分系统也显示出了与手动定量的高度相关性,以及具有可靠的重测可靠性。


总而言之,该研究结果揭示了深度学习系统对心血管事件的自动预测的临床价值。该深度学习方法可以根据在几种临床情况下获得的医学图像自动进行心血管疾病风险预测。这些观察结果也为在筛查和医院环境中实施该技术提供了理论依据,从而以高速度和低成本改善了人口健康问题。


原始出处:

Zeleznik, R., Foldyna, B., Eslami, P. et al. Deep convolutional neural networks to predict cardiovascular risk from computed tomography. Nat Commun 12, 715 (29 January 2021).

 

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    2021-05-06 1256ceb5m30暂无昵称

    强大的预测系统

    0

  5. 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  6. 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  7. 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  8. 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    2021-02-08 ms7000001204488220

    心血管疾病有一部分可通过改变生活习惯可预防

    0

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    2021-02-07 神盾医疗局局长Jack

    不错的研究

    0

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