Artificial Intelligence and Computational Approaches for Epilepsy

Abstract

Studies on treatment of epilepsy have been actively conducted in multiple avenues, but there are limitations in improving its efficacy due to between-subject variability in which treatment outcomes vary from patient to patient. Accordingly, there is a growing interest in precision medicine that provides accurate diagnosis for seizure types and optimal treatment for an individual epilepsy patient. Among these approaches, computational studies making this feasible are rapidly progressing in particular and have been widely applied in epilepsy. These computational studies are being conducted in two main streams: 1) artificial intelligence-based studies implementing computational machines with specific functions, such as automatic diagnosis and prognosis prediction for an individual patient, using machine learning techniques based on large amounts of data obtained from multiple patients and 2) patient-specific modeling-based studies implementing biophysical in-silico platforms to understand pathological mechanisms and derive the optimal treatment for each patient by reproducing the brain network dynamics of the particular patient per se based on individual patient’s data. These computational approaches are important as it can integrate multiple types of data acquired from patients and analysis results into a single platform. If these kinds of methods are efficiently operated, it would suggest a novel paradigm for precision medicine.

Neuroimaging in the Era of Artificial Intelligence: Current Applications

“…[37][38][39]43,[45][46][47] Downstream applications of AI in neuroradiology and neurology include using CT to aid in detecting hemorrhage or ischemic stroke; using MRI to automatically segment lesions, such as tumors or MS lesions; assisting in early diagnosis and predicting prognosis in MS; assisting in treating paralysis, including from spinal cord injury; determining seizure type and localizing area of seizure onset; and using cameras, wearable devices, and smartphone applications to diagnose and assess treatment response in neurodegenerative disorders, such as Parkinson or Alzheimer diseases (Figure). 37,[48][49][50][51][52][53][54][55][56] Several AI tools have been deployed in the clinical setting, particularly triaging intracranial hemorrhage and moving these studies to the top of the radiologist's worklist. In 2020 the Centers for Medicare and Medicaid Services (CMS) began reimbursing Viz.ai software's AI-based Viz ContaCT (Viz LVO) with a new International Statistical Classification of Diseases, Tenth Revision procedure code.…”

Sage Against the Machine: Promise and Challenge of Artificial Intelligence in Epilepsy

“…Beyond biomedical imaging, AI and computational approaches have been used in epilepsy to analyze clinical, neurophysiologic (eg electromyographic kinetic data, transcranial magnetic stimulation/magnetoencephalographic or scalp/intracranial video-electroencephalographic signals) and genomic/proteomic data for similar purposes. 7 - 9 The same limitations apply to these studies compared to their neuroimaging counterparts.…”

Temporal lobe epilepsy lateralisation and surgical outcome prediction using diffusion imaging

“…Many groups have leveraged machine learning (a.k.a. artificial intelligence) to maximise the information gleaned from non-invasive modalities to address existing problems in epileptology 12. In general, machine learning is the process of inputting large amounts of raw data into a fine-tuned algorithm to provide a specific clinically relevant result.…”