Discrimination between leucine-rich glioma-inactivated 1 antibody encephalitis and gamma-aminobutyric acid B receptor antibody encephalitis based on ResNet18
作者机构:School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijing 100044China Department of NeurologyBeijing Tiantan HospitalCapital Medical UniversityChina National Clinical Research Center for Neurological DiseasesBeijing 100070China Department of Nuclear MedicineBeijing Tiantan HospitalCapital Medical UniversityBeijing 100070China School of Engineering MedicineBeihang UniversityBeijing 100191China Key Laboratory of Big Data-Based Precision Medicine(Beihang University)Ministry of Industry and Information Technology of the People’s Republic of ChinaBeijing 100191China.
出 版 物:《Visual Computing for Industry,Biomedicine,and Art》 (工医艺的可视计算(英文))
年 卷 期:2023年第6卷第1期
页 面:245-256页
核心收录:
学科分类:1002[医学-临床医学] 08[工学] 080203[工学-机械设计及理论] 100214[医学-肿瘤学] 0802[工学-机械工程] 10[医学]
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
主 题:ResNet18 Fluorodeoxyglucose-positron emission tomography GABAB receptor antibody encephalitis Deep learning LGI1 antibody encephalitis
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 ***18,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 *** receiver operating characteristic(ROC)curve and the area under the ROC curve(AUC)were generated to evaluate the CNN *** on the prediction results at slice level,a decision strategy was employed to evaluate the CNN models’performance at patient *** 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 *** 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 *** of the image slices extracted using gradient-weighted class activation mapping indicated that the model focused on the MTL and BG for *** general,the ResNet18 model is a potential approach for discriminating between LGI1 and GABAB receptor antibody *** in the MTL and BG is important for discriminating between these two encephalitis subtypes.