Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist

Kerr, Wesley T.; McFarlane, Katherine N.

doi:10.1007/s11910-023-01318-7

Cited by 4 publications

(7 citation statements)

References 204 publications

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“…This allows developers to develop high performance ML/AI tools based on large datasets, as well as validate them on data that was “unseen” by the tool. This approach of cross-validation and other similar techniques allow for development of new approaches on limited datasets, but before integration into clinical practice, these tools also should be validated with external datasets on a broad population of people ( 43–45 ).…”

Section: How To Evaluate New Machine Learning Tools For Seizure Detec...mentioning

confidence: 99%

“…Machine learning (ML) tools are trained based on historical data to maximize their performance based on a single quantitative metric (e.g., accuracy of classifying seizure [ictal], pre-seizure [pre-ictal], post-seizure [post-ictal], versus between seizure [interictal] states). Artificial intelligence (AI) tools are designed to perform a broad range of tasks, including tasks for which they have not been explicitly trained, and can do so using multiple ML tools ( 43 ). We are not aware of any AI tool for seizures that both fills that definition and is approved or cleared for clinical use by the United States Food and Drug Administration (FDA).…”

Section: Introductionmentioning

confidence: 99%

“…While there is substantial hype about the power of ML/AI for clinical medicine ( 44 , 45 ), especially seizures, there are only a limited number of tools that have rigorously demonstrated their utility and have been integrated into standard clinical practice ( 43 ). This is despite decades of effort, especially focused on seizure detection and prediction using electroencephalography (EEG) ( 46 , 47 ).…”

Section: Introductionmentioning

confidence: 99%

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The present and future of seizure detection, prediction, and forecasting with machine learning, including the future impact on clinical trials

Kerr,

McFarlane,

Figueiredo Pucci

2024

Front. Neurol.

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Seizures have a profound impact on quality of life and mortality, in part because they can be challenging both to detect and forecast. Seizure detection relies upon accurately differentiating transient neurological symptoms caused by abnormal epileptiform activity from similar symptoms with different causes. Seizure forecasting aims to identify when a person has a high or low likelihood of seizure, which is related to seizure prediction. Machine learning and artificial intelligence are data-driven techniques integrated with neurodiagnostic monitoring technologies that attempt to accomplish both of those tasks. In this narrative review, we describe both the existing software and hardware approaches for seizure detection and forecasting, as well as the concepts for how to evaluate the performance of new technologies for future application in clinical practice. These technologies include long-term monitoring both with and without electroencephalography (EEG) that report very high sensitivity as well as reduced false positive detections. In addition, we describe the implications of seizure detection and forecasting upon the evaluation of novel treatments for seizures within clinical trials. Based on these existing data, long-term seizure detection and forecasting with machine learning and artificial intelligence could fundamentally change the clinical care of people with seizures, but there are multiple validation steps necessary to rigorously demonstrate their benefits and costs, relative to the current standard.

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Section: How To Evaluate New Machine Learning Tools For Seizure Detec...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The present and future of seizure detection, prediction, and forecasting with machine learning, including the future impact on clinical trials

Kerr,

McFarlane,

Figueiredo Pucci

2024

Front. Neurol.

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“…ML is a subfield of artificial intelligence (AI), which is a scientific discipline that focuses on how computers learn from data ( 7 , 8 ). ML, a data-driven technology, turns raw data into actionable and interpretable insights that support clinical decision-making ( 9 ). It tackles the challenge of developing computer models that improve autonomously with experience.…”

Section: Introductionmentioning

confidence: 99%

“…It is leveraged for a range of purposes, including automated imaging analysis and disease prediction. In the specific context of epilepsy, ML techniques are employed for detecting and testing seizures, predicting epilepsy in atrisk individuals, classifying types of epilepsy, modeling the disorder, tracking and forecasting responses to pharmacological and surgical interventions, analyzing EEG data, and enhancing neuroanatomical localization and bias regularization (5,6,9,(11)(12)(13). Consequently, ML plays an essential role in advancing epilepsy research.…”

Section: Introductionmentioning

confidence: 99%

Machine learning applied to epilepsy: bibliometric and visual analysis from 2004 to 2023

Huo,

Luo,

et al. 2024

Front. Neurol.

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BackgroundEpilepsy is one of the most common serious chronic neurological disorders, which can have a serious negative impact on individuals, families and society, and even death. With the increasing application of machine learning techniques in medicine in recent years, the integration of machine learning with epilepsy has received close attention, and machine learning has the potential to provide reliable and optimal performance for clinical diagnosis, prediction, and precision medicine in epilepsy through the use of various types of mathematical algorithms, and promises to make better parallel advances. However, no bibliometric assessment has been conducted to evaluate the scientific progress in this area. Therefore, this study aims to visually analyze the trend of the current state of research related to the application of machine learning in epilepsy through bibliometrics and visualization.MethodsRelevant articles and reviews were searched for 2004–2023 using Web of Science Core Collection database, and bibliometric analyses and visualizations were performed in VOSviewer, CiteSpace, and Bibliometrix (R-Tool of R-Studio).ResultsA total of 1,284 papers related to machine learning in epilepsy were retrieved from the Wo SCC database. The number of papers shows an increasing trend year by year. These papers were mainly from 1,957 organizations in 87 countries/regions, with the majority from the United States and China. The journal with the highest number of published papers is EPILEPSIA. Acharya, U. Rajendra (Ngee Ann Polytechnic, Singapore) is the authoritative author in the field and his paper “Deep Convolutional Neural Networks for Automated Detection and Diagnosis of Epileptic Seizures Using EEG Signals” was the most cited. Literature and keyword analysis shows that seizure prediction, epilepsy management and epilepsy neuroimaging are current research hotspots and developments.ConclusionsThis study is the first to use bibliometric methods to visualize and analyze research in areas related to the application of machine learning in epilepsy, revealing research trends and frontiers in the field. This information will provide a useful reference for epilepsy researchers focusing on machine learning.

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The promise of AI Large Language Models for Epilepsy care

Landais,

Sultan,

Thomas

2024

Epilepsy & Behavior

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Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist

Cited by 4 publications

References 204 publications

The present and future of seizure detection, prediction, and forecasting with machine learning, including the future impact on clinical trials

The present and future of seizure detection, prediction, and forecasting with machine learning, including the future impact on clinical trials

Machine learning applied to epilepsy: bibliometric and visual analysis from 2004 to 2023

The promise of AI Large Language Models for Epilepsy care

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