Evidence map›Paper›PMID 39554271›Full record

ReviewEpilepsy currents2024

Artificial Intelligence: Fundamentals and Breakthrough Applications in Epilepsy.

Wesley Kerr, Sandra Acosta, Patrick Kwan, Gregory Worrell, Mohamad A Mikati

Abstract readReview
In one paragraph

Review in Epilepsy currents, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Wesley KerrDepartment of Neurology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
Sandra AcostaDepartment of Pathology and Experimental Therapeutics, Institute of Neurosciences, University of Barcelona, Barcelona, Catalonia, Spain.
Patrick KwanDepartment of Neuroscience, Monash Institute of Medical Engineering at Monash University, and Epilepsy Unit of Alfred Hospital, Melbourne, Victoria, Australia.
Gregory WorrellDepartment of Neurology, Mayo Clinic, Rochester, MN, USA.
Mohamad A MikatiDepartment of Pediatrics, Duke University, Durham, NC, USA.ORCID https://orcid.org/0000-0003-0363-8715

Funding

Reliable Seizure Prediction Using Physiological Signals and Machine LearningR01NS092882 · NINDS · MAYO CLINIC ROCHESTER · PI Gregory A Worrell · 2015 to 2026
$5.7M
The glial mechanism for electrical brain stimulationR01NS112144 · NINDS · MAYO CLINIC ROCHESTER · PI WANG, HAI-LONG, WORRELL, GREGORY A · 2019 to 2023
$3.8M
Optimizing Pharmacotherapy with Noninvasive Wearable Sensors and Subscalp EEGUG3NS123066 · NINDS · MAYO CLINIC ROCHESTER · PI BRINKMANN, BENJAMIN H, WORRELL, GREGORY A · 2022 to 2024
$1.2M
NINDS NIH HHS R01 NS092882NINDS NIH HHS R01 NS112144NINDS NIH HHS UG3 NS123066
6 · The paper itself

Abstract

Artificial intelligence, machine learning, and deep learning are increasingly being used in all medical fields including for epilepsy research and clinical care. Already there have been resultant cutting-edge applications in both the clinical and research arenas of epileptology. Because there is a need to disseminate knowledge about these approaches, how to use them, their advantages, and their potential limitations, the goal of the 2023 Merritt-Putnam Symposium and of this synopsis review of that symposium has been to present the background and state of the art and then to draw conclusions on current and future applications of these approaches through the following: (1) Initially provide an explanation of the fundamental principles of artificial intelligence, machine learning, and deep learning. These are presented in the first section of this review by Dr Wesley Kerr. (2) Provide insights into their cutting-edge applications in screening for medications in neural organoids, in general, and for epilepsy in particular. These are presented by Dr Sandra Acosta. (3) Provide insights into how artificial intelligence approaches can predict clinical response to medication treatments. These are presented by Dr Patrick Kwan. (4) Finally, provide insights into the expanding applications to the detection and analysis of EEG signals in intensive care, epilepsy monitoring unit, and intracranial monitoring situations, as presented below by Dr Gregory Worrell. The expectation is that, in the coming decade and beyond, the increasing use of the above approaches will transform epilepsy research and care and supplement, but not replace, the diligent work of epilepsy clinicians and researchers.

Identifiers

PMID39554271
PMCPMC11562289

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.