Machine learning applications on neuroimaging for diagnosis and prognosis of epilepsy: A review

Yuan，Jie1; Ran，Xuming1; Liu，Keyin1; Yao，Chen2; Yao，Yi3; Wu，Haiyan4; Liu，Quanying1

2022-02-15

10.1016/j.jneumeth.2021.109441

JOURNAL OF NEUROSCIENCE METHODS   影响因子和分区

0165-0270

1872-678X

Machine learning is playing an increasingly important role in medical image analysis, spawning new advances in the clinical application of neuroimaging. There have been some reviews on machine learning and epilepsy before, and they mainly focused on electrophysiological signals such as electroencephalography (EEG) and stereo electroencephalography (SEEG), while neglecting the potential of neuroimaging in epilepsy research. Neuroimaging has its important advantages in confirming the range of the epileptic region, which is essential in presurgical evaluation and assessment after surgery. However, it is difficult for EEG to locate the accurate epilepsy lesion region in the brain. In this review, we emphasize the interaction between neuroimaging and machine learning in the context of epilepsy diagnosis and prognosis. We start with an overview of epilepsy and typical neuroimaging modalities used in epilepsy clinics, MRI, DWI, fMRI, and PET. Then, we elaborate two approaches in applying machine learning methods to neuroimaging data: (i) the conventional machine learning approach combining manual feature engineering and classifiers, (ii) the deep learning approach, such as the convolutional neural networks and autoencoders. Subsequently, the application of machine learning on epilepsy neuroimaging, such as segmentation, localization, and lateralization tasks, as well as tasks directly related to diagnosis and prognosis are looked into in detail. Finally, we discuss the current achievements, challenges, and potential future directions in this field, hoping to pave the way for computer-aided diagnosis and prognosis of epilepsy.

Diagnosis and prognosis of epilepsy Lateralization Localization Machine learning Neuroimaging

SCI

英语

第一 ; 通讯

National Natural Science Foundation of China[62001205] ; Guangdong Natural Science Founda-tion Joint Fund[2019A1515111038] ; Shenzhen Science and Technol-ogy Innovation Committee[20200925155957004,"KCXFZ2020122117340001","SGDX2020110309280100"] ; Shenzhen Key Laboratory of Smart Healthcare Engineering[ZDSYS20200811144003009]

Biochemistry & Molecular Biology ; Neurosciences & Neurology

Biochemical Research Methods ; Neurosciences

WOS:000788155700006

ELSEVIER

NEUROSCIENCE & BEHAVIOR

2-s2.0-85121915671

Scopus

1.Shenzhen Key Laboratory of Smart Healthcare Engineering,Department of Biomedical Engineering,Southern University of Science and Technology,Shenzhen,518055,China
2.Shenzhen Second People's Hospital,Shenzhen,518035,China
3.Shenzhen Children's Hospital,Shenzhen,518017,China
4.Centre for Cognitive and Brain Sciences and Department of Psychology,University of Macau,Taipa,Macao

Yuan，Jie,Ran，Xuming,Liu，Keyin,et al. Machine learning applications on neuroimaging for diagnosis and prognosis of epilepsy: A review[J]. JOURNAL OF NEUROSCIENCE METHODS,2022,368.

Yuan，Jie.,Ran，Xuming.,Liu，Keyin.,Yao，Chen.,Yao，Yi.,...&Liu，Quanying.(2022).Machine learning applications on neuroimaging for diagnosis and prognosis of epilepsy: A review.JOURNAL OF NEUROSCIENCE METHODS,368.

Yuan，Jie,et al."Machine learning applications on neuroimaging for diagnosis and prognosis of epilepsy: A review".JOURNAL OF NEUROSCIENCE METHODS 368(2022).