Clinical Application of Machine Learning Models for Brain Imaging in Epilepsy: A Review

Abstract

Epilepsy is a common neurological disorder characterized by recurrent and disabling seizures. An increasing number of clinical and experimental applications of machine learning (ML) methods for epilepsy and other neurological and psychiatric disorders are available. ML methods have the potential to provide a reliable and optimal performance for clinical diagnoses, prediction, and personalized medicine by using mathematical algorithms and computational approaches. There are now several applications of ML for epilepsy, including neuroimaging analyses. For precise and reliable clinical applications in epilepsy and neuroimaging, the diverse ML methodologies should be examined and validated. We review the clinical applications of ML models for brain imaging in epilepsy obtained from a PubMed database search in February 2021. We first present an overview of typical neuroimaging modalities and ML models used in the epilepsy studies and then focus on the existing applications of ML models for brain imaging in epilepsy based on the following clinical aspects: (i) distinguishing individuals with epilepsy from healthy controls, (ii) lateralization of the temporal lobe epilepsy focus, (iii) the identification of epileptogenic foci, (iv) the prediction of clinical outcomes, and (v) brain-age prediction. We address the practical problems and challenges described in the literature and suggest some future research directions.

Impaired Functional Homotopy and Topological Properties Within the Default Mode Network of Children With Generalized Tonic-Clonic Seizures: A Resting-State fMRI Study

“…Machine learning is an emerging trend in medicine that can be used to develop and/or optimize clinically useful algorithms for clinical medicine and basic research. In recent years, an increasing number of clinical and experimental applications of machine learning methods for epilepsy have become available in the diagnosis of epilepsy, surgical management of epilepsy, and medical management of epilepsy ( Abbasi and Goldenholz, 2019 ; Sone and Beheshti, 2021 ). Machine learning techniques have enabled imaging analysis and epilepsy diagnosis from a wide range of clinical data.…”

Performance reserves in brain-imaging-based phenotype prediction

“…A majority of past and present studies on machine learning for clinical neuroimaging rely on features designed by domain experts; cf. recent reviews on machine learning in epilepsy (Sone & Beheshti, 2021), autism (Xu et al, 2021), stroke (Sirsat et al, 2020), and mild cognitive impairment (Ansart et al, 2021). The UK Biobank directly provides widely used feature representations (IDPs) for structural, functional, and diffusion tensor imaging.…”

Artificial Intelligence Applications in the Imaging of Epilepsy and Its Comorbidities: Present and Future

“…The application of AI to medical image analyses has exponentially increased over the past decade and has the potential to reshape our approach to clinical diagnosis, prediction of treatment outcomes, and management of cognitive comorbidities in epilepsy. 9 , 46 – 48 However, despite the promise of AI, the majority of studies applying ML to epilepsy have been modest in sample size, raising concerns for overfitting and limiting the application of DL models that require thousands of patient samples (e.g., convolutional neural networks). Such barriers will hopefully be lifted in the future as more powerful AI methods are developed and imaging and cognitive data are aggregated across centers and harmonized in the context of big data efforts.…”