ISSN 1004-4140
CN 11-3017/P

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

不同机器学习方法对新型冠状病毒感染与社区获得性肺炎鉴别诊断分析

康兆庭 欧阳雪晖 柴军

康兆庭, 欧阳雪晖, 柴军. 不同机器学习方法对新型冠状病毒感染与社区获得性肺炎鉴别诊断分析[J]. CT理论与应用研究, 2023, 32(5): 685-694. DOI: 10.15953/j.ctta.2023.079
引用本文: 康兆庭, 欧阳雪晖, 柴军. 不同机器学习方法对新型冠状病毒感染与社区获得性肺炎鉴别诊断分析[J]. CT理论与应用研究, 2023, 32(5): 685-694. DOI: 10.15953/j.ctta.2023.079
KANG Z T, OUYANG X H, CHAI J. Differential Diagnosis of COVID-19 and Community-acquired Pneumonia Using Different Machine Learning Methods[J]. CT Theory and Applications, 2023, 32(5): 685-694. DOI: 10.15953/j.ctta.2023.079. (in Chinese)
Citation: KANG Z T, OUYANG X H, CHAI J. Differential Diagnosis of COVID-19 and Community-acquired Pneumonia Using Different Machine Learning Methods[J]. CT Theory and Applications, 2023, 32(5): 685-694. DOI: 10.15953/j.ctta.2023.079. (in Chinese)

不同机器学习方法对新型冠状病毒感染与社区获得性肺炎鉴别诊断分析

doi: 10.15953/j.ctta.2023.079
基金项目: 内蒙古自治区人民医院院内科研基金(基于机器学习弥漫低级别胶质瘤多参数MRI放射基因组学的病理分型与预后研究(2020YN17));内蒙古自然科学基金(基于机器学习对弥漫低级别胶质瘤多参数MR放射基因组学的分子分型与预后研究(2021LHMS08066));2022年度自治区医疗卫生科技计划项目(基于CT图像特征分析的机器学习算法预测非小细胞肺癌的病理分期与预后(202201038))。
详细信息
    作者简介:

    康兆庭:男,内蒙古自治区人民医院影像医学科主治医师,主要从事影像诊断学、影像组学及机器学习等方面的研究,E-mail:848299984@qq.com

    通讯作者:

    男,内蒙古自治区人民医院影像医学科主任医师,主要从事影像诊断学、影像组学及机器学习等方面的研究,E-mail:amaschai@126.com

  • 中图分类号: O  242;R  814;R  563.1

Differential Diagnosis of COVID-19 and Community-acquired Pneumonia Using Different Machine Learning Methods

  • 摘要: 目的:利用深度学习技术,全自动标注病变的计算机断层扫描(CT)数据,开发准确快速区分新型冠状病毒感染(COVID-19)和其他社区获得性肺炎的人工智能模型。方法:回顾性分析248例COVID-19患者及347例其他肺炎患者的资料,进行COVID-19与其他肺炎分类;在人工智能肺分割提取后将异常的CT图像特征降维,输入几种经典强化机器学习模型、三维卷积神经网络(3D CNN)和注意力多示例学习(Attention-MIL)深层神经网络架构中,模型诊断性能利用受试者工作特性(ROC)曲线、精确召回率(PR)曲线、曲线下面积(AUC)、敏感性、特异性、准确性指标进行评价。结果:在经典机器学习模型中K邻近算法(KNN)具有较好的效果,在外部测试集上的AUC值和平均精度(AP)值分别为0.79和0.89,平衡F分数(F1)值为0.76,准确率为0.75,敏感性为0.76,精确率为0.77;经典的3D CNN在外部测试集上效果良好,AUC值和AP值分别为0.64和0.82,F1值为0.71,准确率为0.78,敏感性为0.66,精确率为0.62;Attention-MIL模型在外部测试集上表现出更好的鲁棒性,AUC值和AP值分别为0.85和0.94,F1值达到0.82,准确率为0.92,敏感性为0.74,精确率为0.76。结论:与强化影像组学和3D CNN模型相比,深度学习Attention-MIL模型在鉴别诊断COVID-19和其他社区获得性肺炎上表现出更高的效能。

     

  • 图  1  新型冠状病毒肺炎COVID-19检测神经网络架构

    Figure  1.  Corona Virus Disease 2019 (COVID-19) detection neural network architecture

    图  2  组学模型在外部测试集中的ROC曲线及准确率

    Figure  2.  ROC curve-level and accuracy of the omics model in the external test set

    图  3  DCNN模型在外部测试集上的ROC和PR曲线

    Figure  3.  ROC and PR curves of the DCNN model in the external test set

    图  4  Attention-MIL模型在外部测试集上的ROC和PR曲线

    Figure  4.  ROC and PR curves for the attention-MIL model in the flight set

    图  5  注意力机制针对不同示例的赋予权重大小分析

    Figure  5.  The attention mechanism is weighted for different examples

    图  6  新型冠状病毒感染、社区获得性肺炎者使用梯度加权激活映射或Grad-CAM方法生成的注意力热图

    热图是标准的Jet颜色图,并与原始图像重叠。红色突出显示与预测类别关联的激活区域。

    Figure  6.  Coronavirus disease 2019 (COVID-19), a representative example of attention heatmaps generated with data from individuals with community-acquired pneumonia using gradient-weighted category activation mapping or the Grad-CAM method-pneumonia

    表  1  不同医院患者的统计数据汇总

    Table  1.   Summary of the statistical data of patients from different hospitals

    不同医院肺炎患者病例数(CT数)/例 年龄/岁男/例女/例
    内蒙古人民医院         COVID-1980 45±13.1134~773644
      CAP102 56±14.1245~675745
    金门县人民医院         COVID-19143(143) 44.95±15.12 2~867370
    浙江省人民医院         COVID-194(4) 43±13.1326~59 1 3
      CAP35(35) 42.08±14.9510~662114
    浙江大学医学院附属邵逸夫医院  COVID-198(8) 42.75±6.3333~51 4 4
      CAP210(334) 44.05±16.7715~85103 107
    台州市中心医院         COVID-1913(13) 47.76±14.2231~74 6 7
    下载: 导出CSV

    表  2  各种方法在外部测试集上的表现评价指标

    Table  2.   Performance evaluation indicators for each method on independent test sets

    测试集 F1值准确率/%召回率/%精确率/%
       Adaboost0.550.560.550.55
       bagging0.660.650.680.67
       KNN0.760.750.770.77
       logistic0.720.750.740.72
       MLP0.690.690.710.69
       nusvc0.740.750.760.75
       SVC0.680.690.680.69
       xgboost0.600.600.620.59
    下载: 导出CSV

    表  3  不同机器学习框架在COVID-19独立测试集上的性能

    Table  3.   Performance of different machine learning frameworks on COVID-19 independent test sets

    Group/COVID-19 敏感性/%特异性/%AUCP
        KNN776773P<0.001
        3D CNN786976P<0.001
        Attention-MIL909685P<0.001
    下载: 导出CSV
  • [1] CHEN N, ZHOU M, DONG X, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: A descriptive study[J]. Lancet, 2020, 395(10223): 507−513. DOI: 10.1016/S0140-6736(20)30211-7.
    [2] GAO Y, YAN L, HUANG Y, et al. Structure of the RNA-dependent RNA polymerase from COVID-19 virus[J]. Science, 2020, 368(6492): 779−782. DOI: 10.1126/science.abb7498.
    [3] LI Q, GUAN X, WU P, et al. Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia[J]. New England Journal of Medicine, 2020, 382(13): 1199−1207. DOI: 10.1056/NEJMoa2001316.
    [4] HOLSHUE M L, DEBOLT C, LINDQUIST S, et al. First case of 2019 novel coronavirus in the United States[J]. New England Journal of Medicine, 2020, 382(10): 929−936. DOI: 10.1056/NEJMoa2001191.
    [5] AI T, YANG Z, HOU H, et al. Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: A report of 1014 cases[J]. Radiology, 2020, 296(2): E32−E40. DOI: 10.1148/radiol.2020200642.
    [6] FANG Y, ZHANG H, XIE J, et al. Sensitivity of chest CT for COVID-19: Comparison to RT-PCR[J]. Radiology, 2020, 296(2): E115−E117. DOI: 10.1148/radiol.2020200432.
    [7] 刘玉建, 仲建全, 冯浩, 等. 新型冠状病毒肺炎患者的高分辨率 CT 影像学特征[J]. 医疗装备, 2022,35(11): 1−4.

    LIU Y J, ZHONG J Q, FENG H, et al. Imaging characteristics of high resolution CT for patients with corona virus disease 2019[J]. Medical Equipment, 2022, 35(11): 1−4. (in Chinese).
    [8] HUANG C, WANG Y, LI X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China[J]. Lancet, 2020, 395(10223): 497−506. DOI: 10.1016/S0140-6736(20)30183-5.
    [9] MEI X, LEE H C, DIAO K Y, et al. Artificial intelligence-enabled rapid diagnosis of patients with COVID-19[J]. Nature Medicine, 2020, 26(8): 1224−1228. DOI: 10.1038/s41591-020-0931-3.
    [10] CHEN Y, FAN S, CHEN Y, et al. Vessel segmentation from volumetric images: A multi-scale double-pathway network with class-balanced loss at the voxel level[J]. Medical Physics, 2021, 48(7): 3804−3814. DOI: 10.1002/mp.14934.
    [11] YE H, GAO F, YIN Y, et al. Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network[J]. European Radiology, 2019, 29(11): 6191−6201. DOI: 10.1007/s00330-019-06163-2.
    [12] KERMANG D S, GOLDBAUM M, CAI W, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning[J]. Cell, 2018, 172(5): 1122−1131.e9. DOI: 10.1016/j.cell.2018.02.010.
    [13] RAJARAMAN S, CANDEMIR S, KIM I, et al. Visualization and interpretation of convolutional neural network predictions in detecting pneumonia in pediatric chest radiographs[J]. Applied Sciences-Basel, 2018, 8(10): 1715. DOI: 10.3390/app8101715.
    [14] WYNANTS L, Van CALSTER B, COLLINS G S, et al. Prediction models for diagnosis and prognosis of COVID-19 infection: Systematic review and critical appraisal[J]. British Medical Journal, 2020, 369: m1328. DOI: 10.1136/bmj.m1328.
    [15] ZHANG X, WANG D, SHAO J, et al. A deep learning integrated radiomics model for identification of coronavirus disease 2019 using computed tomography[J]. Scientific Reports, 2021, 11(1): 3938. DOI: 10.1038/s41598-021-83237-6.
    [16] HUANG Y Q, LIANG C H, HE L. Preoperative prediction of lymph node metastasis in colorectal cancer[J]. Journal of Clinical Oncology, 2016, 34(18): 2157−64. DOI: 10.1200/JCO.2015.65.9128.
    [17] PARMAR C, GROSSMANN P, BUSSINK J, et al. Machine learning methods for quantitative radiomic biomarkers[J]. Scientific Reports, 2015, 15: 13087. DOI: 10.1038/srep13087.
    [18] NIETHAMMER M, KWITT R, VIALARD F X. Metric learning for image registration[J]. Proc EEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019: 8455-8464. DOI: 10.1109/cvpr.2019.00866.
    [19] ZHANG X, LU D, GAO P, et al. Survival-relevant high-risk subregion identification for glioblastoma patients: The MRI-based multiple instance learning approach[J]. European Radiology, 2020, 30(10): 5602−5610. DOI: 10.1007/s00330-020-06912-8.
    [20] LIU Y, FU Q, PENG X, et al. Attention-based deep multiple-instance learning for classifying circular RNA and other long non-coding RNA[J]. Genes (Basel), 2021, 12(12): 2018. DOI: 10.3390/genes12122018.
    [21] DELONG E R, DELOONG D M, CLARKE-PEARSON D L. Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach[J]. Biometrics, 1988, 44(3): 837−845. doi: 10.2307/2531595
    [22] SELVARAJU R R, COGSWELL M, DAS A, et al. Grad-cam: Visual explanations from deep networks via gradient-based localization[J]. Proceedings of the IEEE International Conference on Computer Vision, 2017. DOI: 10.1109/ICCV.2017.74.
    [23] MARTIN J, TENA N, ASUERO A G. Current state of diagnostic, screening and surveillance testing methods for COVID-19 from an analytical chemistry point of view[J]. Microchemical Journal, 2021, 167: 106305. DOI: 10.1016/j.microc.2021.106305.
    [24] XU X, JIANG X, MA C, et al. A deep learning system to screen novel coronavirus disease 2019 pneumonia[J]. Engineering (Beijing), 2020, 6(10): 1122−1129. DOI: 10.1016/j.eng.2020.04.010.
    [25] ABBAS A, ABDELSAMEA M, GABER M. Classification of covid-19 in chest X-ray images using DeTraC deep convolutional neural network[J]. Applied Intelligence, 2021, 51(2): 854−864. DOI: 10.1007/s10489-020-01829-7.
    [26] GOZES O, FRID-ADAR M, SAGIE N, et al. Detection and analysis of COVID-19 in medical images using deep learning techniques[J]. Scientific Reports, 2021, 11(1): 19638. DOI: 10.1038/s41598-021-99015-3.
    [27] CHEN J, WU L, ZHANG J, et al. Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: A prospective study[J]. Scientific Reports, 2020, 10(1): 19196. DOI: 10.1038/s41598-020-76282-0.
    [28] WANG S, KANG B, MA J, et al. A deep learning algorithm using CT images to screen for corona virus disease (COVID-19)[J]. European Radiology, 2021, 31(8): 6096-6104.
    [29] LI Z, ZHONG Z, LI Y, et al. From community-acquired pneumonia to COVID-19: A deep learning-based method for quantitative analysis of COVID-19 on thick-section CT scans[J]. European Radiology, 2020, 30(12): 6828−6837. DOI: 10.1007/s00330-020-07042-x.
    [30] CHOUAT I, ECHTIOUI A, KHEMAKHEM R, et al. COVID-19 detection in CT and CXR images using deep learning models[J]. Biogerontology, 2022, 23(1): 65−84. DOI: 10.1007/s10522-021-09946-7.
  • 加载中
图(6) / 表(3)
计量
  • 文章访问数:  55
  • HTML全文浏览量:  29
  • PDF下载量:  4
  • 被引次数: 0
出版历程
  • 收稿日期:  2023-04-04
  • 修回日期:  2023-04-27
  • 录用日期:  2023-05-16
  • 网络出版日期:  2023-08-09
  • 刊出日期:  2023-09-22

目录

    /

    返回文章
    返回