The Emerging Role of Artificial Intelligence in Identifying Epileptogenic Zone: A Systematic Literature Review
DOI:
https://doi.org/10.18196/jrc.v6i5.27281Keywords:
Epileptogenic Zone, Artificial Intelligence, Deep Learning, Machine Learning, Stereo-EEGAbstract
Identifying epileptogenic zones (EZs) is a crucial step in the pre-surgical evaluation of drug-resistant epilepsy patients. Conventional methods, including EEG/SEEG visual inspection and neurofunctional imaging, often face challenges in accuracy, reproducibility, and subjectivity. The rapid development of artificial intelligence (AI) technologies in signal processing and neuroscience has enabled their growing use in detecting epileptogenic zones. This systematic review aims to explore recent developments in AI applications for localizing epileptogenic zones, focusing on algorithm types, dataset characteristics, and performance outcomes. A comprehensive literature search was conducted in 2025 across databases such as ScienceDirect, Springer Nature, and IEEE Xplore using relevant keyword combinations. The study selection followed PRISMA guidelines, resulting in 34 scientific articles published between 2020 and 2024. Extracted data included AI methods, algorithm types, dataset modalities, and performance metrics (accuracy, AUC, sensitivity, and F1-score). Results showed that deep learning was the most used approach (44%), followed by machine learning (35%), multi-methods (18%), and knowledge-based systems (3%). CNN and ANN were the most commonly applied algorithms, particularly in scalp EEG and SEEG-based studies. Datasets ranged from public sources (Bonn, CHB-MIT) to high-resolution clinical SEEG recordings. Multimodal and hybrid models demonstrated superior performance, with several studies achieving accuracy rates above 98%. This review confirms that AI (especially deep learning with SEEG and multimodal integration) has strong potential to improve the precision, efficiency, and scalability of EZ detection. To facilitate clinical adoption, future research should focus on standardizing data pipelines, validating AI models in real-world settings, and developing explainable, ethically responsible AI systems.
References
C. Papadelis and M. S. Perry, “Localizing the Epileptogenic Zone with Novel Biomarkers,” Seminars in Pediatric Neurology, vol. 39, p. 100919, Oct. 2021, doi: 10.1016/j.spen.2021.100919.
W. Löscher, H. Potschka, S. M. Sisodiya, and A. Vezzani, “Drug Resistance in Epilepsy: Clinical Impact, Potential Mechanisms, and New Innovative Treatment Options,” Pharmacological Reviews, vol. 72, no. 3, pp. 606–638, Jul. 2020, doi: 10.1124/pr.120.019539.
D. Villamizar-Torres, A. C. Cepeda Trillos, and A. Vargas-Moreno, “Mesial temporal sclerosis and epilepsy: a narrative review,” Acta Epileptologica, vol. 6, no. 1, 2024, doi: 10.1186/s42494-024-00172-5.
F. Anzellotti et al., “Psychogenic Non-epileptic Seizures and Pseudo-Refractory Epilepsy, a Management Challenge,” Frontiers in Neurology, vol. 11, Jun. 2020, doi: 10.3389/fneur.2020.00461.
S. Roy et al., “Eigenvector biomarker for prediction of epileptogenic zones and surgical success from interictal data,” Frontiers in Network Physiology, vol. 5, May 2025, doi: 10.3389/fnetp.2025.1565882.
A. Fattorusso et al., “The Pharmacoresistant Epilepsy: An Overview on Existent and New Emerging Therapies,” Frontiers in Neurology, vol. 12, Jun. 2021, doi: 10.3389/fneur.2021.674483.
E. Ben-Menachem, B. Schmitz, R. Kälviäinen, R. H. Thomas, and P. Klein, “The burden of chronic drug-refractory focal onset epilepsy: Can it be prevented?,” Epilepsy & Behavior, vol. 148, p. 109435, Nov. 2023, doi: 10.1016/j.yebeh.2023.109435.
F. A. Nascimento et al., “Focal epilepsies: Update on diagnosis and classification,” Epileptic Disorders, vol. 25, no. 1, pp. 1–17, 2023, doi: 10.1002/epd2.20045.
J. Liu et al., “Status of epilepsy in the tropics: An overlooked perspective,” Epilepsia Open, vol. 8, no. 1, pp. 32–45, 2023, doi: 10.1002/epi4.12686.
M. Alghamdi, N. Alomari, A. F. Alamri, R. Ghamdi, R. Nazer, and S. Albloshi, “Drug-resistant epilepsy in Saudi Arabia: prevalence, predictive factors, and treatment outcomes,” BMC Neurology, vol. 25, no. 1, 2025, doi: 10.1186/s12883-025-04149-w.
A. Beydoun, S. DuPont, D. Zhou, M. Matta, V. Nagire, and L. Lagae, “Current role of carbamazepine and oxcarbazepine in the management of epilepsy,” Seizure, vol. 83, pp. 251–263, Dec. 2020, doi: 10.1016/j.seizure.2020.10.018.
A. Manole et al., “State of the Art and Challenges in Epilepsy—A Narrative Review,” Journal of Personalized Medicine, vol. 13, no. 4, p. 623, Apr. 2023, doi: 10.3390/jpm13040623.
K. M. C. Moalong, A. I. Espiritu, M. L. L. Fernandez, and R. D. G. Jamora, “Treatment gaps and challenges in epilepsy care in the Philippines,” Epilepsy & Behavior, vol. 115, p. 107491, Feb. 2021, doi: 10.1016/j.yebeh.2020.107491.
N. Koirala et al., “Assistive Artificial Intelligence in Epilepsy and Its Impact on Epilepsy Care in Low- and Middle-Income Countries,” Brain Sciences, vol. 15, no. 5, 2025, doi: 10.3390/brainsci15050481.
S. Ghosh, J. K. Sinha, S. Ghosh, H. Sharma, R. Bhaskar, and K. B. Narayanan, “A Comprehensive Review of Emerging Trends and Innovative Therapies in Epilepsy Management,” Brain Sciences, vol. 13, no. 9, p. 1305, Sep. 2023, doi: 10.3390/brainsci13091305.
Y. Heydari, Y. Bozzi, and L. Pavesi, “Decoding epileptic seizures: Exploring in vitro approaches to unravel pathophysiology and propel future therapeutic breakthroughs,” Biomedical Materials and Devices, vol. 2, no. 2, pp. 905–917, 2024, doi: 10.1007/s44174-024-00158-4.
J. Yuan et al., “Machine learning applications on neuroimaging for diagnosis and prognosis of epilepsy: A review,” Journal of Neuroscience Methods, vol. 368, p. 109441, Feb. 2022, doi: 10.1016/j.jneumeth.2021.109441.
C. Collins, D. Dennehy, K. Conboy, and P. Mikalef, “Artificial intelligence in information systems research: A systematic literature review and research agenda,” International Journal of Information Management, vol. 60, 2021, doi: 10.1016/j.ijinfomgt.2021.102383.
H. Hassani, E. S. Silva, S. Unger, M. TajMazinani, and S. Mac Feely, “Artificial Intelligence (AI) or Intelligence Augmentation (IA): What Is the Future?,” AI (Switzerland), vol. 1, no. 2, 2020, doi: 10.3390/ai1020008.
I. H. Sarker, “AI-based modeling: Techniques, applications and research issues towards automation, intelligent and smart systems,” SN Computer Science, vol. 3, no. 2, 2022, doi: 10.1007/s42979-022-01043-x.
M. M. Taye, “Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions,” Computers, vol. 12, no. 5, p. 91, Apr. 2023, doi: 10.3390/computers12050091.
M. Khalifa and M. Albadawy, “AI in diagnostic imaging: Revolutionising accuracy and efficiency,” Computer Methods and Programs in Biomedicine Update, vol. 5, 2024, doi: 10.1016/j.cmpbup.2024.100146.
S. A. Alowais et al., “Revolutionizing healthcare: the role of artificial intelligence in clinical practice,” BMC Medical Education, vol. 23, no. 1, 2023, doi: 10.1186/s12909-023-04698-z.
G. Krishnan et al., “Artificial intelligence in clinical medicine: catalyzing a sustainable global healthcare paradigm,” Frontiers in Artificial Intelligence, vol. 6, Aug. 2023, doi: 10.3389/frai.2023.1227091.
J. Bajwa, U. Munir, A. Nori, and B. Williams, “Artificial intelligence in healthcare: transforming the practice of medicine,” Future Healthcare Journal, vol. 8, no. 2, pp. e188–e194, 2021, doi: 10.7861/fhj.2021-0095.
M. A. AbuAlrob, A. Itbaisha, and B. Mesraoua, “Unlocking new frontiers in epilepsy through AI: From seizure prediction to personalized medicine,” Epilepsy & Behavior, vol. 166, p. 110327, May 2025, doi: 10.1016/j.yebeh.2025.110327.
R. Onciul et al., “Artificial Intelligence and Neuroscience: Transformative Synergies in Brain Research and Clinical Applications,” Journal of Clinical Medicine, vol. 14, no. 2, 2025, doi: 10.3390/jcm14020550.
G. B. Dell’Isola, A. Fattorusso, G. Villano, P. Ferrara, and A. Verrotti, “Innovating pediatric epilepsy: transforming diagnosis and treatment with AI,” World Journal of Pediatrics, vol. 21, no. 4, pp. 333–337, 2025, doi: 10.1007/s12519-025-00904-8.
W. T. Kerr, K. N. McFarlane, and G. Figueiredo Pucci, “The present and future of seizure detection, prediction, and forecasting with machine learning, including the future impact on clinical trials,” Frontiers in Neurology, vol. 15, 2024, doi: 10.3389/fneur.2024.1425490.
L. K. Avberšek and G. Repovš, “Deep learning in neuroimaging data analysis: Applications, challenges, and solutions,” Frontiers in Neuroimaging, vol. 1, Oct. 2022, doi: 10.3389/fnimg.2022.981642.
X. Xu et al., “A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis,” Bioengineering, vol. 11, no. 3, 2024, doi: 10.3390/bioengineering11030219.
S. T. Jonna and K. Natarajan, “EEG signal processing in neurological conditions using machine learning and deep learning methods: a comprehensive review,” The European Physical Journal Special Topics, Apr. 2025, doi: 10.1140/epjs/s11734-025-01606-y.
K. M. Alalayah, E. M. Senan, H. F. Atlam, I. A. Ahmed, and H. S. A. Shatnawi, “Effective Early Detection of Epileptic Seizures through EEG Signals Using Classification Algorithms Based on t-Distributed Stochastic Neighbor Embedding and K-Means,” Diagnostics, vol. 13, no. 11, 2023, doi: 10.3390/diagnostics13111957.
S. Abirami et al., “Automated Multi-Class Seizure-Type Classification System Using EEG Signals and Machine Learning Algorithms,” IEEE Access, vol. 12, pp. 136524–136541, 2024, doi: 10.1109/ACCESS.2024.3462772.
J. A. Tangsrivimol et al., “Artificial Intelligence in Neurosurgery: A State-of-the-Art Review from Past to Future,” Diagnostics, vol. 13, no. 14, 2023, doi: 10.3390/diagnostics13142429.
E. Roger et al., “A machine learning approach to explore cognitive signatures in patients with temporo-mesial epilepsy,” Neuropsychologia, vol. 142, p. 107455, May 2020, doi: 10.1016/j.neuropsychologia.2020.107455.
M. Hashemi et al., “The Bayesian Virtual Epileptic Patient: A probabilistic framework designed to infer the spatial map of epileptogenicity in a personalized large-scale brain model of epilepsy spread,” NeuroImage, vol. 217, 2020, doi: 10.1016/j.neuroimage.2020.116839.
G. J. et al., “Automatic and Accurate Epilepsy Ripple and Fast Ripple Detection via Virtual Sample Generation and Attention Neural Networks,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 8, pp. 1710–1719, 2020.
Z. L. et al., “EMS-Net: A Deep Learning Method for Autodetecting Epileptic Magnetoencephalography Spikes,” IEEE Transactions on Medical Imaging, vol. 39, no. 6, pp. 1833–1844, 2020.
L. C. Djoufack Nkengfack, D. Tchiotsop, R. Atangana, B. S. Tchinda, V. Louis-Door, and D. Wolf, “A comparison study of polynomial-based PCA, KPCA, LDA and GDA feature extraction methods for epileptic and eye states EEG signals detection using kernel machines,” Informatics in Medicine Unlocked, vol. 26, 2021, doi: 10.1016/j.imu.2021.100721.
M. Xia, L. Sui, X. Zhao, T. Tanaka, and J. Cao, “Convolution Neural Network recognition of epileptic foci based on composite signal processing of electroencephalograph data,” Procedia Computer Science, vol. 192, pp. 688–696, 2021, doi: 10.1016/j.procs.2021.08.071.
I. Aliyu and C. G. Lim, “Selection of optimal wavelet features for epileptic EEG signal classification with LSTM,” Neural Computing and Applications, vol. 35, no. 2, pp. 1077–1097, 2023, doi: 10.1007/s00521-020-05666-0.
F. Pérez-García et al., “A self-supervised learning strategy for postoperative brain cavity segmentation simulating resections,” International Journal of Computer Assisted Radiology and Surgery, vol. 16, no. 10, pp. 1653–1661, 2021, doi: 10.1007/s11548-021-02420-2.
L. C. Djoufack Nkengfack, D. Tchiotsop, R. Atangana, V. Louis-Door, and D. Wolf, “Classification of EEG signals for epileptic seizures detection and eye states identification using Jacobi polynomial transforms-based measures of complexity and least-square support vector machine,” Informatics in Medicine Unlocked, vol. 23, p. 100536, 2021, doi: 10.1016/j.imu.2021.100536.
A. Torabi and M. R. Daliri, “Applying nonlinear measures to the brain rhythms: an effective method for epilepsy diagnosis,” BMC Medical Informatics and Decision Making, vol. 21, no. 1, 2021, doi: 10.1186/s12911-021-01631-6.
S. A. Saeedinia, M. R. Jahed-Motlagh, A. Tafakhori, and N. Kasabov, “Design of MRI structured spiking neural networks and learning algorithms for personalized modelling, analysis, and prediction of EEG signals,” Scientific Reports, vol. 11, no. 1, 2021, doi: 10.1038/s41598-021-90029-5.
J. Guo et al., “Detecting high frequency oscillations for stereoelectroencephalography in epilepsy via hypergraph learning,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 587–596, 2021, doi: 10.1109/TNSRE.2021.3056685.
A. N. Vattikonda, M. Hashemi, V. Sip, M. M. Woodman, F. Bartolomei, and V. K. Jirsa, “Identifying spatio-temporal seizure propagation patterns in epilepsy using Bayesian inference,” Communications Biology, vol. 4, no. 1, 2021, doi: 10.1038/s42003-021-02751-5.
Z. Wang and P. Mengoni, “Seizure classification with selected frequency bands and EEG montages: a Natural Language Processing approach,” Brain Informatics, vol. 9, no. 1, 2022, doi: 10.1186/s40708-022-00159-3.
J. Liu, Y. Du, X. Wang, W. Yue, and J. Feng, “Automated Machine Learning for Epileptic Seizure Detection Based on EEG Signals,” Computers, Materials and Continua, vol. 73, no. 1, pp. 1995–2011, 2022, doi: 10.32604/cmc.2022.029073.
D. Sunaryono, R. Sarno, and J. Siswantoro, “Gradient boosting machines fusion for automatic epilepsy detection from EEG signals based on wavelet features,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 10, pp. 9591–9607, 2022, doi: 10.1016/j.jksuci.2021.11.015.
Y. Miao, Y. Iimura, H. Sugano, K. Fukumori, and T. Tanaka, “Seizure onset zone identification using phase-amplitude coupling and multiple machine learning approaches for interictal electrocorticogram,” Cognitive Neurodynamics, vol. 17, no. 6, pp. 1591–1607, 2023, doi: 10.1007/s11571-022-09915-x.
A. H. Mohammed et al., “Dynamics of Electrical Activity in Epileptic Brain and Induced Changes Due to Interictal Epileptiform Discharges,” IEEE Access, vol. 10, pp. 1276–1288, 2022, doi: 10.1109/ACCESS.2021.3138385.
Y. Wang et al., “SEEG-Net: An explainable and deep learning-based cross-subject pathological activity detection method for drug-resistant epilepsy,” Computers in Biology and Medicine, vol. 148, p. 105703, Sep. 2022, doi: 10.1016/j.compbiomed.2022.105703.
S. Mohsen, S. S. M. Ghoneim, M. S. Alzaidi, A. Alzahrani, and A. M. Ali Hassan, “Classification of Electroencephalogram Signals Using LSTM and SVM Based on Fast Walsh-Hadamard Transform,” Computers, Materials and Continua, vol. 75, no. 3, pp. 5271–5286, 2023, doi: 10.32604/cmc.2023.038758.
R. Sun, W. Zhang, A. Bagić, and B. He, “Deep learning based source imaging provides strong sublobar localization of epileptogenic zone from MEG interictal spikes,” NeuroImage, vol. 281, p. 120366, Nov. 2023, doi: 10.1016/j.neuroimage.2023.120366.
Y. Dou, J. Xia, M. Fu, Y. Cai, X. Meng, and Y. Zhan, “Identification of epileptic networks with graph convolutional network incorporating oscillatory activities and evoked synaptic responses,” NeuroImage, vol. 284, p. 120439, Dec. 2023, doi: 10.1016/j.neuroimage.2023.120439.
Z. Li et al., “Machine learning-based classification of physiological and pathological high-frequency oscillations recorded by stereoelectroencephalography,” Seizure, vol. 113, pp. 58–65, 2023, doi: 10.1016/j.seizure.2023.11.005.
L. Ilias, D. Askounis, and J. Psarras, “Multimodal detection of epilepsy with deep neural networks,” Expert Systems with Applications, vol. 213, 2023, doi: 10.1016/j.eswa.2022.119010.
S. A. Weiss et al., “Graph theoretical measures of fast ripple networks improve the accuracy of post-operative seizure outcome prediction,” Scientific Reports, vol. 13, no. 1, 2023, doi: 10.1038/s41598-022-27248-x.
Y.-T. Kim et al., “Differentiating loss of consciousness causes through artificial intelligence-enabled decoding of functional connectivity,” NeuroImage, vol. 297, p. 120749, Aug. 2024, doi: 10.1016/j.neuroimage.2024.120749.
T. K. Murugan and A. Kameswaran, “Employing convolutional neural networks and explainable artificial intelligence for the detection of seizures from electroencephalogram signal,” Results in Engineering, vol. 24, p. 103378, Dec. 2024, doi: 10.1016/j.rineng.2024.103378.
C. Stergiadis, D. Kazis, and M. A. Klados, “Epileptic tissue localization using graph-based networks in the high frequency oscillation range of intracranial electroencephalography,” Seizure, vol. 117, pp. 28–35, 2024, doi: 10.1016/j.seizure.2024.01.015.
A. A. Payman, I. El-Sayed, and R. R. Rubio, “Exploring the Combination of Computer Vision and Surgical Neuroanatomy: A Workflow Involving Artificial Intelligence for the Identification of Skull Base Foramina,” World Neurosurgery, vol. 191, pp. e403–e410, 2024, doi: 10.1016/j.wneu.2024.08.137.
L. Huang, K. Zhou, S. Chen, Y. Chen, and J. Zhang, “Automatic detection of epilepsy from EEGs using a temporal convolutional network with a self-attention layer,” BioMedical Engineering Online, vol. 23, no. 1, 2024, doi: 10.1186/s12938-024-01244-w.
M. V. V. P. Kantipudi, N. S. P. Kumar, R. Aluvalu, S. Selvarajan, and K. Kotecha, “An improved GBSO-TAENN-based EEG signal classification model for epileptic seizure detection,” Scientific reports, vol. 14, no. 1, p. 843, 2024, doi: 10.1038/s41598-024-51337-8.
S. Mora et al., “NLP-based tools for localization of the epileptogenic zone in patients with drug-resistant focal epilepsy,” Scientific Reports, vol. 14, no. 1, 2024, doi: 10.1038/s41598-024-51846-6.
M. Mercier et al., “The value of linear and non-linear quantitative EEG analysis in paediatric epilepsy surgery: a machine learning approach,” Scientific Reports, vol. 14, no. 1, 2024, doi: 10.1038/s41598-024-60622-5.
U. Krishnamoorthy, S. Jagan, M. Zakariah, A. S. Almazyad, and K. Gurunathan, “A Novel Optimized Deep Convolutional Neural Network for Efficient Seizure Stage Classification,” Computers, Materials and Continua, vol. 81, no. 3, pp. 3903–3926, 2024, doi: 10.32604/cmc.2024.055910.
D. J. Doss, G. W. Johnson, and D. J. Englot, “Imaging and Stereotactic Electroencephalography Functional Networks to Guide Epilepsy Surgery,” Neurosurgery Clinics of North America, vol. 35, no. 1, pp. 61–72, 2024, doi: 10.1016/j.nec.2023.09.001.
E. D. Smolyansky, H. Hakeem, Z. Ge, Z. Chen, and P. Kwan, “Machine learning models for decision support in epilepsy management: A critical review,” Epilepsy and Behavior, vol. 123, 2021, doi: 10.1016/j.yebeh.2021.108273.
J. L. Evans et al., “SEEG4D: a tool for 4D visualization of stereoelectroencephalography data,” Frontiers in Neuroinformatics, vol. 18, 2024, doi: 10.3389/fninf.2024.1465231.
M. Milne-Ives et al., “The use of AI in epilepsy and its applications for people with intellectual disabilities: commentary,” Acta Epileptologica, vol. 7, no. 1, 2025, doi: 10.1186/s42494-025-00205-7.
J. A. Yeung, Y. Y. Wang, Z. Kraljevic, and J. T. H. Teo, “Artificial intelligence (AI) for neurologists: do digital neurones dream of electric sheep?,” Practical Neurology, vol. 23, no. 6, pp. 476–488, 2023, doi: 10.1136/pn-2023-003757.
M. Pedersen, K. Verspoor, M. Jenkinson, M. Law, D. F. Abbott, and G. D. Jackson, “Artificial intelligence for clinical decision support in neurology,” Brain Communications, vol. 2, no. 2, 2020, doi: 10.1093/braincomms/fcaa096.
H. Torkey, S. Hashish, S. Souissi, E. E. D. Hemdan, and A. Sayed, “Seizure Detection in Medical IoT: Hybrid CNN-LSTM-GRU Model with Data Balancing and XAI Integration,” Algorithms, vol. 18, no. 2, 2025, doi: 10.3390/a18020077.
R. Bouderhem, “A Comprehensive Framework for Transparent and Explainable AI Sensors in Healthcare,” in ECSA-11, Nov. p. 49, 2024, doi: 10.3390/ecsa-11-20524.
Z. Sadeghi et al., “A review of Explainable Artificial Intelligence in healthcare,” Computers and Electrical Engineering, vol. 118, 2024, doi: 10.1016/j.compeleceng.2024.109370.
G. Abgrall, A. L. Holder, Z. Chelly Dagdia, K. Zeitouni, and X. Monnet, “Should AI models be explainable to clinicians?,” Critical Care, vol. 28, no. 1, 2024, doi: 10.1186/s13054-024-05005-y.
A. Gerdes, “The role of explainability in AI-supported medical decision-making,” Discover Artificial Intelligence, vol. 4, no. 1, 2024, doi: 10.1007/s44163-024-00119-2.
C. Metta, A. Beretta, R. Pellungrini, S. Rinzivillo, and F. Giannotti, “Towards Transparent Healthcare: Advancing Local Explanation Methods in Explainable Artificial Intelligence,” Bioengineering, vol. 11, no. 4, 2024, doi: 10.3390/bioengineering11040369.
H. Uyanik, A. Sengur, M. Salvi, R. S. Tan, J. H. Tan, and U. R. Acharya, “Automated Detection of Neurological and Mental Health Disorders Using EEG Signals and Artificial Intelligence: A Systematic Review,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 15, no. 1, 2025, doi: 10.1002/widm.70002.
J. Bösel, R. Mathur, L. Cheng, M. S. Varelas, M. A. Hobert, and J. I. Suarez, “AI and Neurology,” Neurological Research and Practice, vol. 7, no. 1, p. 11, Feb. 2025, doi: 10.1186/s42466-025-00367-2.
S. R. Sheikh et al., “Machine learning algorithm for predicting seizure control after temporal lobe resection using peri-ictal electroencephalography,” Scientific Reports, vol. 14, no. 1, p. 21771, Sep. 2024, doi: 10.1038/s41598-024-72249-7.
W. Kerr, S. Acosta, P. Kwan, G. Worrell, and M. A. Mikati, “Artificial Intelligence: Fundamentals and Breakthrough Applications in Epilepsy,” Epilepsy Currents, Mar. 2024, doi: 10.1177/15357597241238526.
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