Cargando…
Clinical Value of Machine Learning in the Automated Detection of Focal Cortical Dysplasia Using Quantitative Multimodal Surface-Based Features
Objective: To automatically detect focal cortical dysplasia (FCD) lesion by combining quantitative multimodal surface-based features with machine learning and to assess its clinical value. Methods: Neuroimaging data and clinical information for 74 participants (40 with histologically proven FCD type...
Autores principales: | Mo, Jia-Jie, Zhang, Jian-Guo, Li, Wen-Ling, Chen, Chao, Zhou, Na-Jing, Hu, Wen-Han, Zhang, Chao, Wang, Yao, Wang, Xiu, Liu, Chang, Zhao, Bao-Tian, Zhou, Jun-Jian, Zhang, Kai |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6336916/ https://www.ncbi.nlm.nih.gov/pubmed/30686974 http://dx.doi.org/10.3389/fnins.2018.01008 |
Ejemplares similares
-
Superior Frontal Sulcus Focal Cortical Dysplasia Type II: An MRI, PET, and Quantified SEEG Study
por: Zhang, Chao, et al.
Publicado: (2019) -
Interictal pattern on scalp electroencephalogram predicts excellent surgical outcome of epilepsy caused by focal cortical dysplasia
por: Wan, Hui‐juan, et al.
Publicado: (2022) -
Multimodality Image Post-processing in Detection of Extratemporal MRI-Negative Cortical Dysplasia
por: Hu, Wen-han, et al.
Publicado: (2018) -
Novel surface features for automated detection of focal cortical dysplasias in paediatric epilepsy
por: Adler, Sophie, et al.
Publicado: (2016) -
Electroclinical and Multimodality Neuroimaging Characteristics and Predictors of Post-Surgical Outcome in Focal Cortical Dysplasia Type IIIa
por: Zhang, Lingling, et al.
Publicado: (2022)