@article {Kaestner10.1212 / WNL。0000000000207411,作者= {Erik Kaestner小君饶和艾伦·j . Chang钟王艾琳和罗宾·M布施和西蒙·S·凯勒和西奥多·R {\ " u}数量和丹尼尔·L Drane和特拉维斯Stoub Ezequiel Gleichgerrcht和莱昂纳多Bonilha凯尔Hasenstab和凯莉麦克唐纳},title ={卷积神经网络算法来确定癫痫患者癫痫发作的偏侧性:理论水平研究},elocation-id = {10.1212 / WNL。={2023}0000000000207411},年,doi = {10.1212 / WNL。出版商0000000000207411}= {Wolters Kluwer健康,公司代表美国神经病学学会},文摘={背景和目标:诊断放射学的新边疆的包容machine-assisted支持工具,首页促进细微病变的识别通常人眼不可见。结构神经影像中扮演着重要的角色在癫痫患者病变的鉴别,通常配合癫痫的焦点。这里我们探索潜在的卷积神经网络(CNN)来确定偏侧性癫痫发作的癫痫患者使用t1加权结构核磁共振扫描作为输入。方法:使用一个数据集的359例颞叶癫痫(框架)从7外科中心,我们测试了基于t1影像的CNN能否发作一侧进行分类整合与临床团队共识。CNN与随机模型(相比机会)和海马体积逻辑回归(目前clinically-available措施的比较)。此外,我们利用一个CNN特性可视化技术来识别区域用于分类的病人。结果:在100个运行,CNN模型整合了临床医生偏侧性平均78 \ % (SD = 5.1 \ %)表现最佳的运行模型实现89 \ %的一致性。CNN优于随机模型(51.7的平均一致性\ %)100 \ %的运行平均提高26.2 \ %和表现海马体积模型(71.7的平均一致性\ %)85 \ %的运行平均提高6.25 \ %。 Feature visualization maps revealed that in addition to the medial temporal lobe, regions in the lateral temporal lobe, cingulate, and precentral gyrus aided in classification.Discussion: These extra-temporal lobe features underscore the importance of whole-brain models to highlight areas worthy of clinician scrutiny during temporal lobe epilepsy lateralization. This proof-of-concept study illustrates that a CNN applied to structural MRI data can visually aid clinician-led localization of epileptogenic zone and identify extra-hippocampal regions that may require additional radiological attention.Classification of Evidence: This study provides Class II evidence that in patients with drug-resistant unilateral temporal lobe epilepsy, a convolutional neural network algorithm derived from T1-weighted MR images can correctly classify seizure laterality.}, issn = {0028-3878}, URL = {//www.ez-admanager.com/content/early/2023/05/18/WNL.0000000000207411}, eprint = {//www.ez-admanager.com/content/early/2023/05/18/WNL.0000000000207411.full.pdf}, journal = {Neurology} }