人工智能在根尖周囊肿病理诊断中的初步应用  

Preliminary application of artificial intelligence in the pathological diagnosis of periapical cysts

作　　者：郝逸航 黄美畅 李茂 汤亚玲 梁新华[1] HAO Yi-hang;HUANG Meichang;LI Mao;TANG Yaling;LIANG Xinhua(State Key Laboratory of Oral Diseases&Na-tional Clinical Research Center for Oral Diseases,Department of Oral and Maxillofacial Surgery,West China Hospital of Stomatology,Sichuan University,Chengdu 610041,China;State Key Laboratory of Oral Diseases&National Clinical Research Center for Oral Diseases,Department of Pathology,West China Hospital of Stomatology,Sichuan Uni-versity,Chengdu 610041,China)

机构地区：[1]口腔疾病研究国家重点实验室、国家口腔疾病临床医学研究中心、四川大学华西口腔医院口腔颌面外科,四川成都610041 [2]口腔疾病研究国家重点实验室、国家口腔疾病临床医学研究中心、四川大学华西口腔医院病理科,四川成都610041

出　　处：《口腔疾病防治》2023年第9期641-646,共6页Journal of Prevention and Treatment for Stomatological Diseases

基　　金：四川省科技计划项目(2022YFS0289);四川大学华西口腔医院临床研究项目(LCYJ2019⁃8)。

摘　　要：目的研究人工智能应用于根尖周囊肿病理诊断的效果,初步探索人工智能在口腔病理学领域中的应用。方法以87例根尖周囊肿的病理图像作为研究对象,构建U-net型结构的神经网络,将87幅根尖周囊肿的HE图像和标注图像分为训练集72幅图和测试集15幅图,分别用于训练模型和测试模型,最后利用目标级指标F1分数和像素级指标Dice系数以及受试者工作特征(receiver operating characteristic,ROC)曲线评价U-net网络模型在根尖周囊肿上皮识别中的能力。结果U-net网络模型识别根尖周囊肿上皮的性能:F1分数为0.75,Dice系数为0.685,ROC曲线下面积为0.878。结论通过人工智能构建的U-net网络模型在识别根尖周囊肿上皮时具有较好的分割结果,能够初步应用于根尖周囊肿的病理诊断,有望进一步大样本验证后逐步推广于临床。Objective To study the effect of artificial intelligence in the pathological diagnosis of periapical cysts and to explore the application of artificial intelligence in the field of oral pathology.Methods Pathological images of eighty-seven periapical cysts were selected as subjects to read,and a neural network with a U-net structure was constructed.The 87 HE images and labeled images of periapical cysts were divided into a training set(72 images)and a test set(15 images),which were used in the training model and test model,respectively.Finally,the target level index F1 score,pixel level index Dice coefficient and receiver operating characteristic(ROC)curve were used to evaluate the ability of the U-net model to recognize periapical cyst epithelium.Results The F1 score of the U-net network model for recognizing periapical cyst epithelium was 0.75,and the Dice index and the areas under the ROC curve were 0.685 and 0.878,respectively.Conclusion The U-net network model constructed by artificial intelligence has a good segmentation result in identifying periapical cyst epithelium,which can be preliminarily applied in the pathological diagnosis of periapical cysts and is expected to be gradually popularized in clinical practice after further verification with large samples.

关 键 词：根尖周囊肿 病理诊断 影像诊断 人工智能 口腔病理学 U⁃net网络 Dice系数 受试者工作特征曲线 

分 类 号：R78[医药卫生—口腔医学]