We evaluated whether integration of expert guidance on seizure onset zone (SOZ) identification from resting state functional MRI (rs-fMRI) connectomics combined with deep learning (DL) techniques enhances the SOZ delineation in patients with refractory epilepsy (RE), compared to utilizing DL alone. Rs-fMRI were collected from 52 children with RE who had subsequently undergone ic-EEG and then, if indicated, surgery for seizure control (n = 25). The resting state functional connectomics data were previously independently classified by two expert epileptologists, as indicative of measurement noise, typical resting state network connectivity, or SOZ. An expert knowledge integrated deep network was trained on functional connectomics data to identify SOZ. Expert knowledge integrated with DL showed a SOZ localization accuracy of 84.8& and F1 score, harmonic mean of positive predictive value and sensitivity, of 91.7%. Conversely, a DL only model yielded an accuracy of less than 50% (F1 score 63%). Activations that initiate in gray matter, extend through white matter and end in vascular regions are seen as the most discriminative expert identified SOZ characteristics. Integration of expert knowledge of functional connectomics can not only enhance the performance of DL in localizing SOZ in RE, but also lead toward potentially useful explanations of prevalent co-activation patterns in SOZ. RE with surgical outcomes and pre-operative rs-fMRI studies can yield expert knowledge most salient for SOZ identification.

结合专家指导和深度学习技术，通过静息态功能磁共振成像（rs-fMRI）连接组学对难治性癫痫患者的发作起始区（SOZ）进行评估，结果显示，集成了专家知识的深度网络可以提高SOZ定位的准确性，并且这种集成能够对SOZ中普遍存在的协同激活模式提供有用的解释。

基于专家知识与人工智能的结合在使用静息状态功能性磁共振成像中优于仅人工智能的癫痫起始区定位