ArticleScientific reports2024
Machine learning algorithm for predicting seizure control after temporal lobe resection using peri-ictal electroencephalography.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled 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
- The use of quantitative electroencephalography in evaluating epilepsy treatment.Acta epileptologica · 2026Review
- Identification of EEG features during status epilepticus for prediction of emergent epilepsy phenotype in the mouse intra-amygdala kainic acid model using supervised learning.IBRO neuroscience reports · 2026Article
- Machine-learning methods for epilepsy diagnosis and therapeutic prevention: advances, setbacks, and opportunities.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Review
- Review
- Role of Pentacyclic Triterpenes in the Management of Neurological Disorders: An Insight into Molecular Mechanisms and Therapeutic Approaches.Molecular neurobiology · 2026Review
- Obstructive sleep apnea and seizures: pathophysiological mechanisms and clinical implications: a narrative review.Sleep & breathing = Schlaf & Atmung · 2026Review
- Multimodal neuroimaging fusion in presurgical evaluation of drug-resistant epilepsy: current evidence, clinical integration, and future directions.Frontiers in neuroscience · 2026Review
- Non-invasive preoperative prediction of surgical outcomes in drug-resistant epilepsy using seizure-related EEG network dynamics.Frontiers in neuroscience · 2026Article
- Multimodal artificial intelligence in epilepsy: antiseizure medication response, epileptogenic zone localization, and surgical outcome prediction.Frontiers in neurology · 2026Review
- Stronger pre-surgical functional connectivity networks are associated with improved surgical outcome in temporal lobe epilepsy.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2025Article
- Geometric representations of brain networks can predict the surgery outcome in temporal lobe epilepsy.NPJ systems biology and applications · 2025Article
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8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Brain resection is curative for a subset of patients with drug resistant epilepsy but up to half will fail to achieve sustained seizure freedom in the long term. There is a critical need for accurate prediction tools to identify patients likely to have recurrent postoperative seizures. Results from preclinical models and intracranial EEG in humans suggest that the window of time immediately before and after a seizure ("peri-ictal") represents a unique brain state with implications for clinical outcome prediction. Using a dataset of 294 patients who underwent temporal lobe resection for seizures, we show that machine learning classifiers can make accurate predictions of postoperative seizure outcome using 5 min of peri-ictal scalp EEG data that is part of universal presurgical evaluation (AUC 0.98, out-of-group testing accuracy > 90%). This is the first approach to seizure outcome prediction that employs a routine non-invasive preoperative study (scalp EEG) with accuracy range likely to translate into a clinical tool. Decision curve analysis (DCA) shows that compared to the prevalent clinical-variable based nomogram, use of the EEG-augmented approach could decrease the rate of unsuccessful brain resections by 20%.
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