Discrimination between leucine-rich glioma-inactivated 1 antibody encephalitis and gamma-aminobutyric acid B receptor antibody encephalitis based on ResNet18  

作　　者：Jian Pan Ruijuan Lv Qun Wang Xiaobin Zhao Jiangang Liu Lin Ai 

机构地区：[1]School of Computer and Information Technology,Beijing Jiaotong University,Beijing 100044,China [2]Department of Neurology,Beijing Tiantan Hospital,Capital Medical University,China National Clinical Research Center for Neurological Diseases,Beijing 100070,China [3]Department of Nuclear Medicine,Beijing Tiantan Hospital,Capital Medical University,Beijing 100070,China [4]School of Engineering Medicine,Beihang University,Beijing 100191,China [5]Key Laboratory of Big Data-Based Precision Medicine(Beihang University),Ministry of Industry and Information Technology of the People’s Republic of China,Beijing 100191,China.

出　　处：《Visual Computing for Industry,Biomedicine,and Art》2023年第1期245-256,共12页工医艺的可视计算（英文）

基　　金：grants from the Beijing Natural Science Foundation-Haidian Original Innovation Joint Foundation,No.L222033;the National Key Research and Development Program of China“Common Disease Prevention and Control Research”Key Project,No.2022YFC2503800;the National Natural Science Foundation of China,No.81771143;the Beijing Natural Science Foundation,No.7192054;and the National Key Research and Development Program of China,No.2018YFC1315201.

摘　　要：This study aims to discriminate between leucine-rich glioma-inactivated 1(LGI1)antibody encephalitis and gammaaminobutyric acid B(GABAB)receptor antibody encephalitis using a convolutional neural network(CNN)model.A total of 81 patients were recruited for this study.ResNet18,VGG16,and ResNet50 were trained and tested separately using 3828 positron emission tomography image slices that contained the medial temporal lobe(MTL)or basal ganglia(BG).Leave-one-out cross-validation at the patient level was used to evaluate the CNN models.The receiver operating characteristic(ROC)curve and the area under the ROC curve(AUC)were generated to evaluate the CNN models.Based on the prediction results at slice level,a decision strategy was employed to evaluate the CNN models’performance at patient level.The ResNet18 model achieved the best performance at the slice(AUC=0.86,accuracy=80.28%)and patient levels(AUC=0.98,accuracy=96.30%).Specifically,at the slice level,73.28%(1445/1972)of image slices with GABAB receptor antibody encephalitis and 87.72%(1628/1856)of image slices with LGI1 antibody encephalitis were accurately detected.At the patient level,94.12%(16/17)of patients with GABAB receptor antibody encephalitis and 96.88%(62/64)of patients with LGI1 antibody encephalitis were accurately detected.Heatmaps of the image slices extracted using gradient-weighted class activation mapping indicated that the model focused on the MTL and BG for classification.In general,the ResNet18 model is a potential approach for discriminating between LGI1 and GABAB receptor antibody encephalitis.Metabolism in the MTL and BG is important for discriminating between these two encephalitis subtypes.

关 键 词：ResNet18 Fluorodeoxyglucose-positron emission tomography GABAB receptor antibody encephalitis Deep learning LGI1 antibody encephalitis 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] R73[自动化与计算机技术—计算机科学与技术]