人工智能辅助慢性鼻窦炎嗅觉障碍预测  

Artificial intelligence-assisted prediction of olfactory disorders in patients with chronic rhinosinusitis

作　　者：陈靖媛 陈雯仪 罗新 黄雪琨[1,2] 张雅娜 杨钦泰[1,2] CHEN Jingyuan;CHEN Wenyi;LUO Xin;HUANG Xuekun;ZHANG Yana;YANG Qintai(Department of Otorhinolaryngology Head and Neck Surgery,the Third Affiliated Hospital of Sun Yat-sen University,Guangzhou,510630,China;Department of Allergy,the Third Affiliated Hospital of Sun Yat-sen University)

机构地区：[1]中山大学附属第三医院耳鼻咽喉头颈外科,广州510630 [2]中山大学附属第三医院变态反应科

出　　处：《临床耳鼻咽喉头颈外科杂志》2023年第11期871-877,885,共8页Journal of Clinical Otorhinolaryngology Head And Neck Surgery

基　　金：科技部重点研发计划(No:2022YFC2504105);国家自然科学基金(No:U20A20399、82171114、82271148、82371121);广东省重点领域研发计划(No:2020B0101130015);广东省自然科学基金(No:2021A1515011764、2022A1515011787);中山大学临床研究5010计划项目(No:2019006);高校基本科研业务费青年教授培育项目(No:23qnpy141)。

摘　　要：目的:基于人工智能病理诊断及临床特征分析慢性鼻窦炎(chronic rhinosinusitis,CRS)患者嗅觉障碍的影响因素并进行预测。方法:回顾性分析2021年10月至2023年2月于中山大学附属第三医院耳鼻咽喉头颈外科住院并接受鼻内镜手术的CRS患者75例,其中男53例,女22例,中位年龄42.0岁。通过CRS智能显微镜判读系统计算每例患者组织病理切片中腺体、血管面积占比及嗜酸性粒细胞、淋巴细胞、浆细胞和中性粒细胞的绝对值及比例。此外,根据Sniffin'Sticks嗅觉测试结果将患者分为嗅觉正常和嗅觉异常组,比较组间患者基本资料、鼻黏膜组织病理学特征差异、实验室检测指标及鼻窦CT,分析嗅觉障碍的独立影响因素并进一步描绘受试者工作特征(receiveroperating characteristic,ROC)曲线评价相关模型临床预测效能。采用SPSS25.0软件进行统计学分析。结果:75例CRS患者中,其中嗅觉正常25例(33.3%)、嗅觉障碍50例(66.7%)。多因素logistic回归模型发现,组织嗜酸性粒细胞百分比(OR=1.032,95%CI1.002~1.064,P=0.036)、嗅觉障碍生活质量调查问卷(questionnaire of olfactory disorders-negative statement,QOD-NS)(OR=1.079,95%CI1.004~1.160,P=0.040)和嗅裂前区评分(anteriorol factory cleftscore,AOCS)(OR=2.672,95%CI1.480~4.827,P=0.001)均为CRS嗅觉障碍的独立危险因素。进一步研究发现,由组织嗜酸性粒细胞百分比、QOD-NS和AOCS建立的联合预测模型ROC曲线下面积为0.836(95%CI0.748~0.924,P<0.001),较上述单因素预测模型对CRS嗅觉障碍预测效果较好。结论:基于人工智能病理诊断组织嗜酸性粒细胞百分比、QOD-NS和AOCS为CRS患者嗅觉障碍的独立危险因素,三者联合对CRS嗅觉障碍有较好的预测效果。Objective:To analyze the influencing factors and perform the prediction of olfactory disorders in patients with chronic rhinosinusitis(CRS)based on artificial intelligence.Methods:The data of 75 patients with CRS who underwent nasal endoscopic surgery from October 2021 to February 2023 in the Department of Otorhinolaryngology Head and Neck Surgery,the Third Affiliated Hospital of Sun Yat-sen University were analyzed retrospectively.There were 53 males and 22 females enrolled in the study,with a median age of 42.0 years old.The CRS intelligent microscope interpretation system was used to calculate the proportion of area glands and blood vessels occupy in the pathological sections of each patient,and the absolute value and proportion of eosinophils,lymphocytes,plasma cells and neutrophils.The patients were grouped according to the results of the Sniffin'Sticks smell test,and the clinical baseline data,differences in nasal mucosal histopathological characteristics,laboratory test indicators and sinus CT were compared between the groups.Determine the independent influencing factors of olfactory disorders and receiver operating characteristic curves(ROC)were used to evaluate the performance of the prediction model.Statistical analysis was performed using SPSS 25.0 software.Results:Among the 75 CRS patients,25 cases(33.3%)had normal olfaction and 50 cases(66.7%)had olfactory disorders.Multivariate Logistic regression analysis showed that tissue eosinophils percentage(OR=1.032,95%CI 1.002-1.064,P=0.036),Questionnaire of olfactory disorders-Negative statement(QOD-NS)(OR=1.079,95%CI 1.004-1.160,P=0.040)and Anterior olfactory cleft score(AOCS)(OR=2.672,95%CI 1.480-4.827,P=0.001)were independent risk factors for olfactory disorders in CRS patients.Further research found that the area under the ROC curve(AUC)of the combined prediction model established by the tissue eosinophil percentage,QOD-NS and AOCS was 0.836(95%CI 0.748-0.924,P<0.001),which is better than the above single factor prediction model in predicting olfactory

关 键 词：慢性鼻窦炎 嗅觉障碍 人工智能 嗜酸性粒细胞 显微镜判读系统 

分 类 号：R765.4[医药卫生—耳鼻咽喉科]