王 敏

肺炎支原体感染是儿童及青少年社区社区获得性肺炎的重要病因。其早期症状、炎症反应和影像表现与病毒性肺炎及其他细菌性肺炎存在较多重叠，单靠某一项指标往往难以及时判断病原。血清学、核酸扩增和胸部影像检查各有优势，也受到病程、采样质量及结果判读等因素影响。人工智能可整合症状、实验室指标和影像信息，为早期筛查、病原辅助鉴别及风险判断提供新的技术支持。本文结合现有文献，分析常规临床数据、胸部X线与CT影像在快速诊断中的应用价值，并讨论其在门急诊初筛、病原学确认及重症、难治性肺炎预警中的应用路径。现阶段，人工智能更适合作为临床决策辅助工具，后续仍需通过规范数据采集、多中心验证和真实世界评价提高模型的临床适用性。

肺炎支原体；人工智能；快速诊断；机器学习；胸部影像；临床决策

[1] Subspecialty Group of Respiratory,the Society of Pediatrics,Chinese Medical Association,et al.Evidence-based guideline for the diagnosis and treatment of Mycoplasma pneumoniae pneumonia in children(2023).Pediatr Investig,2025,9(1):1-11. [2] Yun KW.Community-acquired pneumonia in children:updated perspectives on its etiology,diagnosis,and treatment.Clin Exp Pediatr, 2024,67(2):80-89. [3] Peng X,Liu Y,Zhang B,et al.A preliminary prediction model of pediatric Mycoplasma pneumoniae pneumonia based on routine blood parameters by using machine learning method.BMC Infect Dis,2024,24:707. [4] Lan XH,Zhang YX,Yuan WH,et al.Image-based deep learning in diagnosing mycoplasma pneumonia on pediatric chest X-rays.BMC Pediatr,2024,24:720. [5] Wang D,Zhao J,Zhang R,et al.The value of CT radiomic in differentiating Mycoplasma pneumoniae pneumonia from Streptococcus pneumoniae pneumonia with similar consolidation in children under 5 years.Front Pediatr,2022,10:953399. [6] Ye Y,Gao Z,Zhang Z,et al.A machine learning model for predicting severe Mycoplasma pneumoniae pneumonia in school-aged children.BMC Infect Dis,2025,25:570. [7] Qian Y,Tao Y,Wu L,et al.Model based on the automated AI-driven CT quantification is effective for the diagnosis of refractory Mycoplasma pneumoniae pneumonia.Sci Rep,2024,14:16172. [8] Wang YS,Zhou YL,Bai GN,et al.Expert consensus on the diagnosis and treatment of macrolide-resistant Mycoplasma pneumoniae pneumonia in children.World J Pediatr,2024,20:901-914.

点击下载PDF