ReviewFrontiers in neurology2024
The present and future of seizure detection, prediction, and forecasting with machine learning, including the future impact on clinical trials.
Review in Frontiers in neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled 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.
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.
Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic performance of neuroimaging modalities for epileptogenic focus localization: A systematic review.Epilepsia open · 2026Pooled it
- Application of machine learning approaches to predict seizure-onset zones in patients with drug-resistant epilepsy: a systematic review.Frontiers in neurology · 2025Pooled it
- Artificial Intelligence in Veterinary Neurology: Comparative Insights From Human Medicine and Cross-Species Technology Transfer.Veterinary medicine and science · 2026Review
- Clinical application of artificial intelligence technology in epilepsy.Acta epileptologica · 2026Review
- An edge-AI enabled wearable platform for real-time epileptic seizure detection with geolocated alerting.BMC biomedical engineering · 2026Article
- Article
- Review
- Vision-Based Artificial Intelligence Technologies for Epilepsy Monitoring: Scoping Review and Taxonomy Development Study.Journal of medical Internet research · 2026Article
- Single-channel EEG-based seizure prediction using deep learning.Scientific reports · 2026Article
- PSD-LW-DCN: a generalizable power spectral density based lightweight deep convolutional neural network for seizure detection.Scientific reports · 2026Article
- Revolutionizing non-traumatic acute care: a review of the role of artificial intelligence and machine learning in triaging and diagnosis.Acute and critical care · 2026Article
- Bayesian Uncertainty-aware Deep Learning with noisy labels: Tackling annotation ambiguity in EEG seizure detection.PloS one · 2026Article
- An overview of the benefits of animal-assisted interventions in medical and therapeutic contexts for human health: cognitive mechanisms, sensory perception and welfare considerations.Frontiers in veterinary science · 2026Review
- Early detection of antiseizure medication inefficacy using an implantable continuous EEG system and a personalized model: a case study.Epilepsy & behavior reports · 2025Article
- Development and Validation of an IMU Sensor-Based Behaviour-Alert Detection Collar for Assistance Dogs: A Proof-of-Concept Study.Animals : an open access journal from MDPI · 2025Article
- Article
- The use of AI in epilepsy and its applications for people with intellectual disabilities: commentary.Acta epileptologica · 2025Article
- The hidden rhythms of epilepsy: exploring biological clocks and epileptic seizure dynamics.Acta epileptologica · 2025Review
- The prophet's rite of passage - pitfalls in evaluating real-time prediction in medicine.Frontiers in physiology · 2025Article
- Deep learning in intracranial EEG for seizure detection: advances, challenges, and clinical applications.Frontiers in neuroscience · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What Socratic holds
Registered trials
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 Socratic graph.