裴昌军, 孙雪丽, 王鑫, 黄玮, 李梅, 沈志梅. 人工智能结合多层螺旋CT检查在机关体检人群肺结节筛查中的应用[J]. 实用临床医药杂志, 2023, 27(24): 89-92. DOI: 10.7619/jcmp.20232282
引用本文: 裴昌军, 孙雪丽, 王鑫, 黄玮, 李梅, 沈志梅. 人工智能结合多层螺旋CT检查在机关体检人群肺结节筛查中的应用[J]. 实用临床医药杂志, 2023, 27(24): 89-92. DOI: 10.7619/jcmp.20232282
PEI Changjun, SUN Xueli, WANG Xin, HUANG Wei, LI Mei, SHEN Zhimei. Application of artificial intelligence combined with multi-slice spiral CT scanning in the screening of pulmonary nodules in health examination population in government departments[J]. Journal of Clinical Medicine in Practice, 2023, 27(24): 89-92. DOI: 10.7619/jcmp.20232282
Citation: PEI Changjun, SUN Xueli, WANG Xin, HUANG Wei, LI Mei, SHEN Zhimei. Application of artificial intelligence combined with multi-slice spiral CT scanning in the screening of pulmonary nodules in health examination population in government departments[J]. Journal of Clinical Medicine in Practice, 2023, 27(24): 89-92. DOI: 10.7619/jcmp.20232282

人工智能结合多层螺旋CT检查在机关体检人群肺结节筛查中的应用

Application of artificial intelligence combined with multi-slice spiral CT scanning in the screening of pulmonary nodules in health examination population in government departments

  • 摘要:
    目的 探讨人工智能(AI)技术结合多层螺旋CT检查在机关体检人群肺结节筛查中的应用价值。
    方法 回顾性分析在本院健康体检的机关人群胸部CT筛查至少有1个直径≥3 mm的患者资料,所有数据均由AI技术结合人工阅片的方式进行分析。记录肺结节检出率,依据AI技术预测的风险值将结节患者分成不同风险组,分析和比较结节的基本特征。
    结果 肺结节检出率为60.4%。高风险患者更容易表现为纯磨玻璃样结节、混合磨玻璃样结节。有肺结节人群的平均年龄大于无肺结节人群,差异有统计学意义(P<0.05)。不同风险组肺结节性质、形态、内部征象及外部征象等基本特征比较,差异有统计学意义(P<0.05)。
    结论 多层螺旋CT检查胸部扫描有助于大规模体检人群肺结节的筛查, AI技术结合人工阅片的方式能够提高肺结节筛查的准确性。

     

    Abstract:
    Objective To explore the application value of artificial intelligence (AI) combined with multi-slice spiral CT in screening pulmonary nodules in health examination population in government departments.
    Methods A retrospective analysis was conducted on the chest CT screening data of health examination population in government departments who had at least diameter of one nodule ≥3 mm. All data were analyzed using AI technology combined with manual film reading. The detection rate of pulmonary nodules was recorded. Based on the risk values predicted by AI technology, the nodules were divided into different risk groups, and the basic characteristics of the nodules were analyzed and compared.
    Results The detection rate of pulmonary nodules was 60.4%. High-risk patients were more likely to present as pure ground glass nodules or mixed ground glass nodules. The average age of individuals with pulmonary nodules was higher than those without(P<0.05). There were significant differences in the basic characteristics of pulmonary nodules, including nature, morphology, internal signs, and external signs among different risk groups (P<0.05).
    Conclusion  Multi-slice spiral CT examination of the chest scan is helpful for screening pulmonary nodules in large-scale physical examination populations. The combination of AI technology and manual film reading can improve the accuracy of pulmonary nodule screening.

     

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