Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 17 appraisals
Neurological research
Federated deep learning model for epilepsy seizure detection using electroencephalogram (EEG) signal
OBJECTIVES: Epilepsy is a chronic neurological disorder characterized by recurrent seizures due to abnormal brain activity, which affects individuals' health and quality of life. Traditional seizure detection methods face challenges related to data privacy and security as well as difficulty in fully capturing both temporal and spatial relationships within the Electroencephalography signal. To address these limitations, a Federated Learning Enabled Unified Transformer model is proposed. METHODS: The Federated Learning with Paillier Homomorphic Encryption is deployed for preserving data privacy and enabling collaborative model training. Adaptive noise filtering and Independent Component Analysis are deployed to remove the noise and artifacts. The Multi-Scale Wavelet Coefficient is applied for signal decomposition that effectively extracts seizure-related features by decomposing Electroencephalography signals into multiple sub-bands. The Hybrid Graph-Based Attention Framework integrated Edge-Enhanced Graph Convolutional Networks for spatial feature extraction and Spectral Graph Attention for frequency-based feature selection, and refining feature representation for improving the classification accuracy. Further, the proposed technique utilizes a Unified Transformer model for seizure classification that efficiently captures temporal and spatial dependencies in Electroencephalography signals. RESULTS: The proposed model is validated on three datasets and attains 98.91% accuracy, 98.93% security, and 98.82% precision. The simulation outcomes indicate that the Federated Learning Enabled Unified Transformer model achieved outstanding performance when compared to existing models. DISCUSSION: The Federated Learning Enabled Unified Transformer model provided a superior performance by integrating Federated learning and hybrid Deep Learning models. It ensures that the model is more suitable for healthcare users.
2 Aug 2026
Read appraisal →Cell reports methods
AmygdalaGo-BOLT for boundary-aware segmentation of the human amygdala
Tracing the boundaries of the amygdala from brain images remains a major challenge in human neuroscience. Although large-scale neuroimaging studies increasingly collect thousands of scans to investigate structural development in children and adolescents, reliable segmentation of the amygdala is difficult due to its small size and complex morphology-particularly in pediatric populations. To address this, we developed AmygdalaGo-BOLT, a boundary-aware deep learning model specifically designed for amygdala segmentation. The model was trained and validated on 1,086 manually labeled pediatric MRI scans, with independent datasets used to assess generalizability. It integrates multiscale feature extraction, spatial priors, and self-attention mechanisms within a compact encoder-decoder architecture to enhance boundary detection. Across imaging centers and age groups, AmygdalaGo-BOLT demonstrates strong agreement with expert manual annotations, while substantially improving efficiency and accuracy relative to existing tools. This enables robust and scalable analysis of amygdala morphology in population neuroscience studies where manual tracing is impractical.
22 July 2026
Read appraisal →Journal of medical Internet research
Vision-Based Artificial Intelligence Technologies for Epilepsy Monitoring: Scoping Review and Taxonomy Development Study
BACKGROUND: Artificial intelligence (AI) technologies for vision-based epilepsy monitoring are advancing rapidly in health care. Despite growing research using various video data sources and analytical approaches, no comprehensive framework exists to classify these technologies. OBJECTIVE: This scoping review aimed to develop and validate a taxonomy for AI technologies in vision-based epilepsy monitoring and to characterize visual AI approaches in epilepsy care. METHODS: Using an extended taxonomy development framework, we developed the taxonomy in 5 iterative cycles, drawing on theory and practice. We conducted a scoping review, market analysis, and applicability evaluation with market-ready solutions. We searched Scopus, Web of Science, and PubMed, including MeSH (Medical Subject Headings) terms; the final search was completed on January 16, 2026. We included primary studies from 2013 onward on AI-based or machine learning-based monitoring or prediction of epileptic seizures in humans using visual data. We excluded reviews, non-English publications, nonepilepsy studies, studies focused only on electroencephalography or wearables, animal studies, and pre-2013 publications. Evidence was charted through narrative and tabular synthesis and descriptive frequency analysis. In line with scoping review guidance, we did not conduct a meta-analysis or critical appraisal. To assess validity and practical relevance, 9 domain experts evaluated the taxonomy using a Delphi technique. RESULTS: We included 40 original studies. Study analysis yielded 16 dimensions, including data acquisition source, tracking target, image processing, classifier type, performance metrics, environment, seizure classification, data privacy, and user interface. Expert feedback added 4 further dimensions, including communication mode and information purpose. The final taxonomy comprises 23 dimensions with 102 characteristics. The review identified structural evidence gaps across settings, evaluation maturity, and reporting practices. Detection and classification in stationary settings predominated, whereas predictive approaches and real-time feedback were limited. Deep learning detection methods were common, but performance reporting was inconsistent, and patient-facing functionalities were limited. Privacy safeguards and standardized metrics were often incompletely reported, reducing comparability and maturity assessment. The taxonomy translates these patterns into guidance for benchmarking, procurement evaluation, user interface, and explainable AI design. We synthesized 5 main findings and 10 implications for research and practice. Key challenges concern standardization, seizure prediction, and real-time applicability. CONCLUSIONS: Vision-based AI technologies for epilepsy monitoring are still dominated by proof-of-concept and pilot evaluations, indicating a gap between technical feasibility and deployment-ready systems. This scoping review presents an implementation-oriented taxonomy integrating application context, system architecture, visual analysis, AI models, performance reporting, and feedback design into a single classification framework. Unlike prior work that mainly maps methods or data sources, the taxonomy provides a shared structure for consistent system-level characterization and comparison across studies and emerging solutions. It may support benchmarking, implementation-focused evaluation, procurement, and translation into clinical and home settings.
25 June 2026
Read appraisal →Journal of neural engineering
Computational neuroelectrophysiology and artificial intelligence for drug-resistant epilepsy: recent advances, current challenges, and future directions
Drug-resistant epilepsy (DRE) affects approximately 30% of epilepsy patients, with surgical cure rates below 70%. This challenge drives a fundamental paradigm shift from localizing a discrete epileptogenic zone toward characterizing and modulating the dysfunctional brain networks that initiate and propagate seizures. This review critically synthesizes how computational neuroelectrophysiology and artificial intelligence (AI) converge to propel this shift. We chart the trajectory from quantifying local biomarkers, including high-frequency oscillations and excitation-inhibition balance metrics, to mapping the topological properties of epileptic networks through functional and effective connectivity. The integration of these network features with AI techniques, particularly spatiotemporal deep learning architectures, has demonstrated significant potential for enhancing both the localization of the epileptogenic network and the prediction of postoperative outcomes. However, the translation of this network-centric paradigm into clinical practice remains constrained by several challenges, including spatial sampling bias, the inherent label instability of surgical outcomes, and the 'black-box' interpretability crisis. Future directions must emphasize EEG foundation models, causal AI, and the development of interactive multimodal AI agents. The convergence of mechanism-driven network models and data-driven AI frameworks promises a new era of personalized, network-guided therapy for DRE.
11 June 2026
Read appraisal →Brain structure & function
Automated interictal epileptic spike detection from simple and noisy annotations in MEG data
In drug-resistant epilepsy, presurgical evaluation can be considered for suitable candidates. Magnetoencephalography (MEG) has been shown to be an effective exam to inform the localization of the epileptogenic zone through the localization of interictal epileptic spikes. Manual detection of these pathological biomarkers remains a fastidious task due to the high dimensionality of MEG recordings, and inter-rater agreement has been reported to be only moderate. Current automated methods are unsuitable for clinical practice, either requiring extensively annotated data or lacking robustness on non-typical data. Considering a database of 82 patients, we demonstrate that deep learning models can be used for detecting interictal spikes in MEG recordings, even when only temporal and single-expert annotations are available, which represents real-world clinical practice. We propose two model architectures: a feature-based artificial neural network (ANN) and a convolutional neural network (CNN), evaluated against a state-of-the-art model to classify short time windows of signal. In addition, we employ an interactive machine learning strategy to iteratively improve our data annotation quality using intermediary model outputs. Both proposed models outperform the state-of-the-art model (F1-scores: CNN = 0.46, ANN = 0.44) when tested on 10 holdout test patients. The interactive machine learning strategy demonstrates that our models are robust to noisy annotations. Overall, results highlight the ability of our models to analyze complex and imperfectly annotated data. Our method of interactive machine learning offers great potential for faster data annotation, while our models represent useful and efficient tools for automated interictal spikes detection.
1 June 2026
Read appraisal →Neurology
Efficacy of Levetiracetam in Patients With Pediatric Epilepsy: A Systematic Review and Meta-Analysis
BACKGROUND AND OBJECTIVES: Levetiracetam (LEV) is widely used in pediatric epilepsies because of its favorable pharmacokinetics, ease of administration, and perceived tolerability. However, its comparative efficacy relative to established antiseizure medications (ASMs) in children remains uncertain. We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) to evaluate LEV efficacy in pediatric epilepsies and compare outcomes vs placebo and active comparators. METHODS: We systematically searched PubMed/MEDLINE and Embase (2000-6 August 2025) for RCTs enrolling patients 16 years or younger with epilepsy and reporting seizure freedom and/or ≥50% responder rate. Trials including both pediatric and adult patients were eligible if pediatric participants were represented. Comparisons included LEV vs placebo or active ASMs as monotherapy or adjunctive therapy. Primary outcomes were seizure freedom and responder rate at the trial's primary endpoint or, if not specified, longest reported follow-up. We assessed risk of bias using Cochrane Risk of Bias 2. We pooled risk differences (RDs) with 95% CIs using random-effects models, stratified by comparator and epilepsy subtype. RESULTS: We included 25 RCTs (4,070 participants): 23 contributed to pooled meta-analyses. Across 25 trials, the mean age ranged from 0.4 to 39.3 years, reflecting pediatric-only and mixed-age RCTs; 43.8% were female. In placebo/no-therapy-controlled trials (mainly add-on studies), LEV was associated with higher seizure freedom (RD 11.0%; 95% CI 5.3%-16.7%) and responder rates (RD 24.3%; 95% CI 19.1%-29.4%). In active-comparator-controlled trials (mainly monotherapy head-to-head studies), LEV showed no overall advantage vs active comparators for seizure freedom (RD -2.4%; 95% CI -5.6% to 0.7%) or responder rate (RD -7.4%; 95% CI -23.0% to 8.1%). Fourteen trials were at high risk of bias. Sensitivity analyses confirmed benefit vs placebo but showed significant disadvantage vs active comparators in low risk-of-bias trials. Findings in pediatric-only trials (16 RCTs; 1,380 participants) were consistent with the overall results. DISCUSSION: LEV confers benefit vs placebo, mostly as adjunctive therapy, but does not consistently outperform established ASMs in pediatric epilepsies and may be inferior in some subgroups when higher-quality evidence is considered. Limitations include substantial heterogeneity, frequent high risk of bias, variable follow-up durations, publication bias, and limited pediatric-only comparative data.
28 May 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Prediction and diagnosis of post-stroke epilepsy using artificial intelligence approaches: a systematic review and meta-analysis
INTRODUCTION: Post-stroke epilepsy (PSE) is a common complication following a stroke and is a major cause of epilepsy in the elderly. Artificial intelligence (AI) is currently developing rapidly in the medical field and has a promising outlook in disease diagnosis, treatment, and prognosis. METHODS: We screened five studies that fully met the requirements from the PubMed, Web of Science, and EMBASE databases using relevant search terms such as PSE and AI, and analyzed the role of AI in predicting and diagnosing PSE in these studies. RESULTS: The results showed that the sensitivity of AI in predicting and diagnosing PSE was 88% (95% CI 0.78-0.94), and the specificity was 83% (95% CI 0.79-0.86). The area under the summary receiver operating characteristic (SROC) curve was 0.90 (95% CI 0.87-0.92). CONCLUSION: These results indicate that using AI to predict and assist in diagnosing PSE demonstrates high specificity and sensitivity, and has certain prospects in the future auxiliary diagnosis of PSE.
23 May 2026
Read appraisal →Neurosurgical review
Preoperative predictors of seizure outcomes after epilepsy surgery for mesial temporal sclerosis: A systematic review
Mesial temporal lobe epilepsy (MTLE) due to hippocampal sclerosis (HS) is a leading cause of drug-resistant epilepsy. Surgical resection, particularly anterior temporal lobectomy (ATL), achieves high rates of seizure freedom, but outcomes vary. Identifying reliable preoperative predictors may optimize patient selection and surgical success. To systematically evaluate preoperative clinical, neuroimaging, and neuropsychological factors associated with postoperative seizure outcomes in MTLE. A systematic review was conducted according to PRISMA 2020 guidelines. PubMed, Scopus, and Web of Science were searched for cohort studies (2010-2025) reporting predictors of seizure freedom after MTLE-HS surgery. Eligible studies assessed one or more preoperative factors (clinical, EEG, imaging, neuropsychological) in relation to postoperative outcomes. Data were extracted for qualitative synthesis, and methodological quality was assessed with the Newcastle-Ottawa Scale (NOS). From 615 records, 24 studies were included, encompassing patients treated with ATL, selective amygdalohippocampectomy (SAHE), or stereotactic laser ablation. Seizure freedom rates (Engel I/ILAE 1) ranged from 50% to 75%, with ATL achieving ~ 70% at 1-2 years. Multimodal concordance-MRI-visible HS, unilateral seizure onset on EEG, and concordant PET hypometabolism-was the strongest predictor of seizure freedom. Bilateral EEG/PET abnormalities consistently predicted poorer outcomes, whereas structural measures, such as hippocampal subfield volumes and HS subtypes, showed little prognostic value. Multimodal presurgical concordance is a robust predictor of favorable outcomes in MTLE-HS surgery, whereas bilateral or discordant findings indicate a higher risk of failure. Comprehensive presurgical evaluation remains essential to optimize surgical decision-making.
21 May 2026
Read appraisal →Neurology. Clinical practice
Optimizing Antiseizure Medication Taper in the Epilepsy Monitoring Unit
BACKGROUND AND OBJECTIVES: Inpatient video-EEG epilepsy monitoring aims to provide a safe environment for antiseizure medication (ASM) tapering to precipitate seizures, which is often required for diagnosis and treatment. However, there is no standardized taper protocol across institutions, which may contribute to increased morbidity through longer hospitalizations if the taper is too mild and bilateral tonic-clonic seizures (BTCSs) if too aggressive. This study investigates how ASM taper influences length of stay (LOS) and patient outcomes in the epilepsy monitoring unit (EMU), aiming to develop guidelines for medication taper strategies to maximize diagnostic yield while minimizing patient risk. METHODS: We used natural language processing to extract data from electronic medical records of 639 patients admitted to the University of Pennsylvania EMU between 2017 and 2023. We estimated ASM load, the total normalized sum of all ASM serum concentrations based on dose-dependent pharmacokinetic models, throughout each hospitalization. We applied LASSO (Least Absolute Shrinkage and Selection Operator) regression and mixed-effects models to identify predictors of LOS and BTCS risk as a function of taper speed and percentage of baseline ASM load in patients with (hBTCS) and without (nBTCS) a history of BTCS. RESULTS: Taper speed did not correlate with BTCS risk, although absolute ASM load did. Median ASM loads at seizure occurrence were 51% of baseline in the hBTCS group, 41% in the nBTCS group, and 39% in the unclear group. BTCS occurred at higher ASM loads in patients with hBTCS vs nBTCS. Median ASM loads at first EMU seizure were 33% for both hBTCS and nBTCS groups, while the unclear group had first seizures at lower loads (25% of baseline). Time to first seizure and number of baseline ASMs were positively correlated with longer hospital stays, suggesting that sequential medication taper delayed seizures and prolonged LOS without altering BTCS risk. DISCUSSION: Patient history and ASM load are critical factors in predicting BTCS. Rapid taper to approximately 50% baseline ASM load may more efficiently produce seizures without increased BTCS risk in patients without BTCS history. Individualized taper protocols remain advised, particularly for severe epilepsies. This study provides a foundation for quantitative, evidence-based guidelines to optimize ASM reduction strategies for safely precipitating seizures in EMUs.
15 May 2026
Read appraisal →Journal of molecular modeling
Exploring human GABA transporter 3 binders for epilepsy through quantum reactivity analysis, molecular dynamics, machine learning prediction, and network pharmacology
CONTEXT: Drug-resistant epilepsy (DRE) remains a major therapeutic challenge, affecting millions of patients globally who do not respond to conventional antiepileptic medications. Recent evidence highlights GABA transporter 3 (GAT3), a glial subtype responsible for regulating extracellular GABA levels, as a promising molecular target for seizure control. Despite its therapeutic potential, selective and potent GAT3 inhibitors remain largely unexplored. This study aims to identify novel GAT3 inhibitors through an integrative computational strategy combining quantum chemical, molecular dynamics, and machine learning approaches to accelerate antiepileptic drug discovery. The cryo-EM-resolved human GAT3-SNAP-5114 complex structure was used for virtual screening, leading to the identification of three highest-ranking compounds, viz. Comp_1, Comp_2, and Comp_3, from the diverse chemically optimized screening library. Analysis of their quantum chemistry profiles indicated HOMO-LUMO energy gap values of 4.14 eV, 4.21 eV, and 3.26 eV, respectively, suggesting them to be maximally reactive with favorable electronic properties for interacting with biological molecules. Molecular dynamics simulations were performed in a fully hydrated POPC lipid bilayer using the AMBER force field, providing a physiologically relevant environment for assessing ligand binding and transporter dynamic, revealed stable binding modes, with the highest structural integrity or least deviations in the structure with Comp_2, having RMSD values ranging from 2.5 to 3.0 Å, with strong hydrogen bonding, while the binding mode was more flexible with strong anchorage for Comp_3, with its mode involving diverse interactions in the complex. Principal component and free energy landscape analyses identified energetically favorable, ligand-induced conformational states. Machine learning-based pIC₅₀ predictions using the AdaBoost regressor yielded values of 7.95 (Comp_1), 7.98 (Comp_2), and 7.63 (Comp_3), all comparable or superior to the reference inhibitor (7.89). A novel approach for network pharmacology integrating detailed drug-gene interaction profiling was additionally used to improve systems-level understanding of the discovered compound. These findings validate the potential of Comp_1-3 as lead GAT3-targeting antiepileptic agents. METHODS: A docking-based virtual screening of a structurally diverse compound library was performed against the cryo-EM structure of human GAT3 in complex with the reference inhibitor SNAP-5114. The top-scoring ligands were subjected to density functional theory (DFT) analysis to assess electronic properties such as HOMO-LUMO energy gaps, reflecting potential binding reactivity. Lead candidates were refined, docked, and evaluated through 500 ns molecular dynamics (MD) simulations, analyzing structural stability via RMSD, RMSF, hydrogen bonding, and interaction network profiles. Principal component analysis and free energy landscape evaluations were used to explore ligand-induced conformational dynamics. Furthermore, machine learning (ML) models-particularly the AdaBoost algorithm-were employed to predict pIC₅₀ values, confirming bioactivity trends. Finally, network pharmacology analysis was conducted to elucidate gene targets and pathway interconnections, revealing the multi-pathway involvement of top candidates in seizure regulation. This integrative computational framework underscores the potential of GAT3-focused modelling and ML-guided screening for rational antiepileptic drug design.
14 May 2026
Read appraisal →Journal of neural engineering
Uncovering relationships in multi-channel EEG data using principal Hessian directions and Ricci flow
Objective.The high-dimensional nature of multi-channel EEG data poses major challenges for downstream classification. Uncovering connectivity relationships and network structure between EEG channels can improve high-dimensional EEG signal recovery by identifying redundant inputs and guiding more targeted feature selection. In this study, we aim to demonstrate a systematic framework for extracting useful interchannel structure in EEG data.Approach.We present two complementary methods for inferring network structure and connectivity in EEG data: (1) a new supervised algorithm for inferring classification-relevant community structure based on principal Hessian directions (pHds), and (2) a discrete Ricci flow-based unsupervised community detection algorithm. We demonstrate these systematic methods on high-dimensional real-world EEG datasets involving classifying imagined digits versus non-digits and detecting emotional valence.Main results.We show that our pHd and Ricci flow methods-when combined-can detect interchannel relationships that meaningfully hold on unseen test classification data. Moreover, we demonstrate that this interchannel structure extracted via pHd and Ricci flow can enable subsequent improvements in downstream EEG signal classification.Significance.Our combined pHd-Ricci flow method expands the existing EEG preprocessing toolkit by offering a systematic framework for extracting meaningful network structure from high-dimensional EEG data. By facilitating more targeted and effective feature selection, our method has the potential to improve EEG signal recovery in real-world applications.
11 May 2026
Read appraisal →Epilepsy research
Independent evaluation of deep learning models for detecting focal cortical dysplasia
The purpose of this study is to perform an independent assessment of three state-of-the-art tools for the detection of focal cortical dysplasia (FCD) from Magnetic Resonance images (MRI). These tools include DeepFCD, the Multi-center Epilepsy Lesion Detection (MELD) Classifier, and MELDGraph. T1-weighted and fluid-attenuated inversion recovery MRIs from 101 epilepsy patients with FCD and 101 epilepsy patients without FCD were retrospectively included. Classifiers were evaluated at patient-level by their ability to correctly identify the presence of any FCD lesions, and at lesion-level by their capacity to identify lesions within regions delineated by neuroradiologists in MRI reports. A calibrated threshold for DeepFCD prediction probabilities was empirically determined to improve classifier specificity. Classifier test-retest consistency was measured using the Dice coefficient on repeated MRI scans of 21 individuals. At patient-level, MELDClassifier achieved 52% accuracy (sensitivity=91%, specificity=14%), MELDGraph reached 61% accuracy (sensitivity=76%, specificity=47%) and DeepFCD performed with 56% accuracy (sensitivity=62%, specificity=50%) at an empirically determined threshold of 0.90. At lesion-level, MELDClassifier performed with a sensitivity of 70% and a positive predictive value (PPV) of 13%. MELDGraph reached 53% sensitivity and PPV of 36%, whereas the DeepFCD performed with 30% sensitivity and PPV of 19%. Test-retest reliability was low, with an average [min, max] Dice coefficient of 0.28 [0.0, 1.0] for MELDClassifier, 0.38 [0.0, 1.0] for MELDGraph, and 0.35 [0.05, 0.54] for DeepFCD. This study highlights the current limitations of using deep learning models in FCD diagnosis and emphasizes the need to enhance the tools' accuracy, reliability, and interpretability to improve clinical utility.
9 May 2026
Read appraisal →Epilepsy research
Machine learning in epilepsy
Epilepsy is a complex neurological disorder characterized by pathological processes that unfold across multiple biological scales, from cellular excitability and synaptic integration to large-scale network dynamics observable in electroencephalographic (EEG) recordings. While traditional analytical approaches have provided valuable insights, they often fail to capture high-dimensional and nonlinear structure of contemporary electrophysiological and clinical datasets. Consequently, machine learning (ML) has emerged as a powerful analytical framework in epilepsy research, although its rapid adoption has revealed a growing gap between algorithmic performance and biological interpretability. This review examines ML methods operating across three analytically distinct yet interconnected levels: (i) unsupervised learning for cellular-level phenotyping using high-dimensional electrophysiological data; (ii) supervised learning for EEG-based seizure detection and prediction; and (iii) multiscale modeling frameworks integrating neuronal and network dynamics. Rather than providing an exhaustive catalog of algorithms, we focus on inferential assumptions underlying ML applications, the methodological pitfalls constraining generalization and clinical relevance, and how ML-derived representations can be interpreted within established neurophysiological theory. We highlight that unsupervised ML facilitates identification of latent excitability phenotypes and trajectories obscured in traditional univariate analyses, while supervised ML has substantially advanced automated seizure detection and prediction, despite persistent challenges related to data leakage, class imbalance, and ambiguous preictal labeling. We argue that the most promising direction lies in embedding ML within multiscale mechanistic models, where data-driven inference facilitates parameter estimation and hypothesis generation rather than black-box prediction. By prioritizing interpretability, rigorous validation, and cross-scale integration, ML-enhanced multiscale frameworks offer a path toward clinically actionable models of epilepsy.
9 May 2026
Read appraisal →Pediatric neurology
Electroencephalography-Based Machine Learning Models for Predicting Ketogenic Diet Outcomes in Pediatric Drug-Resistant Epilepsy
BACKGROUND: Ketogenic diet therapy (KDT) is an established treatment for drug-resistant epilepsy (DRE); however, methods for predicting its effectiveness remain underdeveloped. This study evaluated various machine learning (ML) models in predicting responses to KDT among DRE patients based on electroencephalography data. METHODS: Using leave-one-out cross-validation, this study evaluated 19 ML classifiers in predicting the outcomes of 90 DRE patients based on absolute and relative power, as well as functional connectivity measures (phase-locking value, phase lag index [PLI], and weighted PLI) across standard frequency bands. KDT significantly reduced seizure frequency at three and 6 months after initiation. RESULTS: The most effective classifier at 3 months was a Coarse Tree classifier trained on absolute power (recall = 0.933, precision = 0.767, F2 = 0.894, area under the receiver operating characteristic curve = 0.607). The most effective classifier at 6 months was a Gaussian Naive Bayes classifier trained on weighted PLI + relative power (recall = 0.759, precision = 0.854, F2 = 0.776, area under the receiver operating characteristic curve = 0.603). CONCLUSIONS: This study identified the most effective ML models for predicting KDT outcomes in DRE patients. The results highlight the potential of electroencephalography-based ML tools for guiding KDT treatment in clinical practice.
9 May 2026
Read appraisal →Neurology
Wearable Technology and Its Role in Neurologic Care: Emerging Issues in Neurology
Consumer wearable devices are commonly used by patients and consumers for several reasons with increasing application as new technologies are developed. Use of these devices is an emerging issue in Neurology because of increased adoption and the additional data reported to providers by patients. Understanding of possible functions, limitations, and effect on patients of non-US Food and Drug Administration (FDA)-cleared wearable technology to inform neurologic care is needed. A common theme in people with neurologic conditions regarding consumer wearables and associated tracking applications is that there is significant promise in these tools, but adherence (days per use/per week), continued engagement (attrition), and unintended consequences such as heightened anxiety remain important issues. Further understanding and validation of these devices is needed within the field of Neurology before full use and confidence can be achieved. Below, we provide examples of non-FDA-cleared wearable devices used in Neurology in the areas of epilepsy, headache, cardiac monitoring, and sleep.
14 Apr 2026
Read appraisal →Current opinion in neurology
AI in epilepsy neuroimaging
PURPOSE OF REVIEW: Recent advances in the capabilities and usability of artificial intelligence (AI) architectures coupled with increased availability of neuroimaging datasets has fuelled a rapid expansion in AI applications to epilepsy neuroimaging. This review summarizes the main applications of AI in epilepsy neuroimaging and suggests future directions for the field. RECENT FINDINGS: A range of different machine learning approaches, from multi-layer perceptrons to volumetric and graph-based convolutional neural networks, have been utilized for prediction of whether people will have epilepsy, detection of structural epilepsy lesions, localization of seizure onset zones, segmentation of resection cavities after epilepsy surgery as well as for image enhancement. SUMMARY: AI in epilepsy neuroimaging research has primarily focussed on lesion detection and localization, with a number of open and validated tools now available for evaluation across diverse settings. Additional applications of AI in epilepsy neuroimaging are either at earlier stages of development or emerging as new challenges. As these tools and their supporting evidence mature, further work addressing the hurdles of clinical integration is required.
4 Apr 2026
Read appraisal →Expert review of medical devices
Modalities and algorithms for generalized motor seizure detection and prediction: a scoping review
INTRODUCTION: Generalized tonic - clonic seizures (GTCS) is a major cause of sudden unexpected death in epilepsy (SUDEP); thus, continuous monitoring is essential. Electroencephalography (EEG) is the diagnostic gold standard; however, non-EEG wearables have emerged as promising, user-friendly alternatives enhancing comfort, mobility, and social acceptance. AREAS COVERED: This scoping review compared sensing modalities and computational methods used for seizure detection and prediction, identified effective sensor combinations, and highlighted current research gaps. Following PRISMA-ScR guidelines, Scopus, IEEE Xplore, and PubMed databases were searched up to 22 April 2025. Twenty-nine studies met inclusion criteria. For seizure detection, combining electrodermal activity (EDA) with motion sensors - accelerometers (ACC) and gyroscopes (GYR) - and surface electromyography (sEMG) achieved high reliability, 98.6% precision, while using ACC and EDA alone yielded superior sensitivities up to 97.2% and a lower false alarm rate (FAR) of 0.53/24 h. Regarding seizure prediction, combining EDA, blood volume pulse (BVP), ACC, and temperature showed the highest sensitivity of 75.6%. EXPERT OPINION: Wearable multimodal non-EEG seizure detection and prediction systems offer personalized care but face validation, hardware, and privacy hurdles. Future success depends on efficient AI, IoT integration, patient-centric design, and clear regulations ensuring accessible and trustworthy clinical tools. People with epilepsy experience sudden episodes of abnormal brain activity called seizures, or fits. One serious type, the generalized tonic – clonic seizure (GTCS), causes loss of consciousness, muscle stiffening, and body jerking, which can lead to injury or even sudden unexpected death in epilepsy (SUDEP).Traditionally, seizures are monitored in hospitals using electroencephalography (EEG), which records brain signals. However, EEG systems are uncomfortable for daily use and limit continuous monitoring. Recent innovations in wearable sensor technology – such as motion sensors (accelerometers), skin conductance sensors (electrodermal activity), and heart or blood flow sensors (photoplethysmography) – offer a more practical, noninvasive alternative. These devices can be worn as smartwatches or bracelets, fitting naturally into everyday life.Artificial intelligence (AI) and machine learning play an increasingly important role in these systems. By recognizing complex patterns in motion and physiological signals, AI algorithms can automatically detect seizures and, in early research, may even predict when a seizure is likely to occur. While results so far are promising, more clinical studies and larger datasets are needed to confirm accuracy and reliability across different patient groups.Advances in wearable sensors and artificial intelligence could revolutionize epilepsy management by making continuous seizure monitoring and prediction possible in daily life. Although more evidence is required before widespread clinical adoption, these technologies hold strong potential to improve safety, autonomy, and quality of life for people living with epilepsy.
2 Apr 2026
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