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Lucas A, Revell A, Davis KA. Artificial intelligence in epilepsy - applications and pathways to the clinic. Nat Rev Neurol 2024; 20:319-336. [PMID: 38720105 DOI: 10.1038/s41582-024-00965-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/16/2024] [Indexed: 06/06/2024]
Abstract
Artificial intelligence (AI) is rapidly transforming health care, and its applications in epilepsy have increased exponentially over the past decade. Integration of AI into epilepsy management promises to revolutionize the diagnosis and treatment of this complex disorder. However, translation of AI into neurology clinical practice has not yet been successful, emphasizing the need to consider progress to date and assess challenges and limitations of AI. In this Review, we provide an overview of AI applications that have been developed in epilepsy using a variety of data modalities: neuroimaging, electroencephalography, electronic health records, medical devices and multimodal data integration. For each, we consider potential applications, including seizure detection and prediction, seizure lateralization, localization of the seizure-onset zone and assessment for surgical or neurostimulation interventions, and review the performance of AI tools developed to date. We also discuss methodological considerations and challenges that must be addressed to successfully integrate AI into clinical practice. Our goal is to provide an overview of the current state of the field and provide guidance for leveraging AI in future to improve management of epilepsy.
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Affiliation(s)
- Alfredo Lucas
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Andrew Revell
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kathryn A Davis
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA.
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2
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Traub-Weidinger T, Arbizu J, Barthel H, Boellaard R, Borgwardt L, Brendel M, Cecchin D, Chassoux F, Fraioli F, Garibotto V, Guedj E, Hammers A, Law I, Morbelli S, Tolboom N, Van Weehaeghe D, Verger A, Van Paesschen W, von Oertzen TJ, Zucchetta P, Semah F. EANM practice guidelines for an appropriate use of PET and SPECT for patients with epilepsy. Eur J Nucl Med Mol Imaging 2024; 51:1891-1908. [PMID: 38393374 PMCID: PMC11139752 DOI: 10.1007/s00259-024-06656-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 02/13/2024] [Indexed: 02/25/2024]
Abstract
Epilepsy is one of the most frequent neurological conditions with an estimated prevalence of more than 50 million people worldwide and an annual incidence of two million. Although pharmacotherapy with anti-seizure medication (ASM) is the treatment of choice, ~30% of patients with epilepsy do not respond to ASM and become drug resistant. Focal epilepsy is the most frequent form of epilepsy. In patients with drug-resistant focal epilepsy, epilepsy surgery is a treatment option depending on the localisation of the seizure focus for seizure relief or seizure freedom with consecutive improvement in quality of life. Beside examinations such as scalp video/electroencephalography (EEG) telemetry, structural, and functional magnetic resonance imaging (MRI), which are primary standard tools for the diagnostic work-up and therapy management of epilepsy patients, molecular neuroimaging using different radiopharmaceuticals with single-photon emission computed tomography (SPECT) and positron emission tomography (PET) influences and impacts on therapy decisions. To date, there are no literature-based praxis recommendations for the use of Nuclear Medicine (NM) imaging procedures in epilepsy. The aims of these guidelines are to assist in understanding the role and challenges of radiotracer imaging for epilepsy; to provide practical information for performing different molecular imaging procedures for epilepsy; and to provide an algorithm for selecting the most appropriate imaging procedures in specific clinical situations based on current literature. These guidelines are written and authorized by the European Association of Nuclear Medicine (EANM) to promote optimal epilepsy imaging, especially in the presurgical setting in children, adolescents, and adults with focal epilepsy. They will assist NM healthcare professionals and also specialists such as Neurologists, Neurophysiologists, Neurosurgeons, Psychiatrists, Psychologists, and others involved in epilepsy management in the detection and interpretation of epileptic seizure onset zone (SOZ) for further treatment decision. The information provided should be applied according to local laws and regulations as well as the availability of various radiopharmaceuticals and imaging modalities.
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Affiliation(s)
- Tatjana Traub-Weidinger
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Javier Arbizu
- Department of Nuclear Medicine, University of Navarra Clinic, Pamplona, Spain
| | - Henryk Barthel
- Department of Nuclear Medicine, Leipzig University Medical Centre, Leipzig, Germany
| | - Ronald Boellaard
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands
| | - Lise Borgwardt
- Department of Clinical Physiology and Nuclear Medicine, University of Copenhagen, Blegdamsvej 9, DK-2100, RigshospitaletCopenhagen, Denmark
| | - Matthias Brendel
- Department of Nuclear Medicine, Ludwig Maximilian-University of Munich, Munich, Germany
- DZNE-German Center for Neurodegenerative Diseases, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Diego Cecchin
- Nuclear Medicine Unit, Department of Medicine-DIMED, University-Hospital of Padova, Padova, Italy
| | - Francine Chassoux
- Université Paris-Saclay, CEA, CNRS, Inserm, BioMaps, 91401, Orsay, France
| | - Francesco Fraioli
- Institute of Nuclear Medicine, University College London (UCL), London, UK
| | - Valentina Garibotto
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland
- NIMTLab, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Center for Biomedical Imaging (CIBM), Geneva, Switzerland
| | - Eric Guedj
- APHM, CNRS, Centrale Marseille, Institut Fresnel, Timone Hospital, CERIMED, Nuclear Medicine Department, Aix Marseille Univ, Marseille, France
| | - Alexander Hammers
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London & Guy's and St Thomas' PET Centre, King's College London, London, UK
| | - Ian Law
- Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, DK-2100, Copenhagen, Denmark
| | - Silvia Morbelli
- Nuclear Medicine Unit, IRCCS Ospedale Policlinico San Martino, Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Nelleke Tolboom
- Department of Radiology and Nuclear Medicine, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | | | - Antoine Verger
- Department of Nuclear Medicine and Nancyclotep Imaging Platform, CHRU Nancy, Université de Lorraine, IADI, INSERM U1254, Nancy, France
| | - Wim Van Paesschen
- Laboratory for Epilepsy Research, KU Leuven and Department of Neurology, University Hospitals, Leuven, Belgium
| | - Tim J von Oertzen
- Depts of Neurology 1&2, Kepler University Hospital, Johannes Kepler University, Linz, Austria
| | - Pietro Zucchetta
- Nuclear Medicine Unit, Department of Medicine-DIMED, University-Hospital of Padova, Padova, Italy
| | - Franck Semah
- Nuclear Medicine Department, University Hospital, Inserm, CHU Lille, U1172-LilNCog-Lille, F-59000, Lille, France.
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Yao L, Cheng N, Chen AQ, Wang X, Gao M, Kong QX, Kong Y. Advances in Neuroimaging and Multiple Post-Processing Techniques for Epileptogenic Zone Detection of Drug-Resistant Epilepsy. J Magn Reson Imaging 2023. [PMID: 38014782 DOI: 10.1002/jmri.29157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 11/14/2023] [Accepted: 11/15/2023] [Indexed: 11/29/2023] Open
Abstract
Among the approximately 20 million patients with drug-resistant epilepsy (DRE) worldwide, the vast majority can benefit from surgery to minimize seizure reduction and neurological impairment. Precise preoperative localization of epileptogenic zone (EZ) and complete resection of the lesions can influence the postoperative prognosis. However, precise localization of EZ is difficult, and the structural and functional alterations in the brain caused by DRE vary by etiology. Neuroimaging has emerged as an approach to identify the seizure-inducing structural and functional changes in the brain, and magnetic resonance imaging (MRI) and positron emission tomography (PET) have become routine noninvasive imaging tools for preoperative evaluation of DRE in many epilepsy treatment centers. Multimodal neuroimaging offers unique advantages in detecting EZ, especially in improving the detection rate of patients with negative MRI or PET findings. This approach can characterize the brain imaging characteristics of patients with DRE caused by different etiologies, serving as a bridge between clinical and pathological findings and providing a basis for individualized clinical treatment plans. In addition to the integration of multimodal imaging modalities and the development of special scanning sequences and image post-processing techniques for early and precise localization of EZ, the application of deep machine learning for extracting image features and deep learning-based artificial intelligence have gradually improved diagnostic efficiency and accuracy. These improvements can provide clinical assistance for precisely outlining the scope of EZ and indicating the relationship between EZ and functional brain areas, thereby enabling standardized and precise surgery and ensuring good prognosis. However, most existing studies have limitations imposed by factors such as their small sample sizes or hypothesis-based study designs. Therefore, we believe that the application of neuroimaging and post-processing techniques in DRE requires further development and that more efficient and accurate imaging techniques are urgently needed in clinical practice. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Lei Yao
- Clinical Medical College, Jining Medical University, Jining, China
| | - Nan Cheng
- Medical Imaging Department, Affiliated Hospital of Jining Medical University, Jining, China
| | - An-Qiang Chen
- Medical Imaging Department, Affiliated Hospital of Jining Medical University, Jining, China
| | - Xun Wang
- Medical Imaging Department, Affiliated Hospital of Jining Medical University, Jining, China
| | - Ming Gao
- Medical Imaging Department, Affiliated Hospital of Jining Medical University, Jining, China
| | - Qing-Xia Kong
- Department of Neurology, Affiliated Hospital of Jining Medical University, Jining, China
| | - Yu Kong
- Medical Imaging Department, Affiliated Hospital of Jining Medical University, Jining, China
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Sukprakun C, Tepmongkol S. Nuclear imaging for localization and surgical outcome prediction in epilepsy: A review of latest discoveries and future perspectives. Front Neurol 2022; 13:1083775. [PMID: 36588897 PMCID: PMC9800996 DOI: 10.3389/fneur.2022.1083775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Accepted: 11/25/2022] [Indexed: 12/23/2022] Open
Abstract
Background Epilepsy is one of the most common neurological disorders. Approximately, one-third of patients with epilepsy have seizures refractory to antiepileptic drugs and further require surgical removal of the epileptogenic region. In the last decade, there have been many recent developments in radiopharmaceuticals, novel image analysis techniques, and new software for an epileptogenic zone (EZ) localization. Objectives Recently, we provided the latest discoveries, current challenges, and future perspectives in the field of positron emission tomography (PET) and single-photon emission computed tomography (SPECT) in epilepsy. Methods We searched for relevant articles published in MEDLINE and CENTRAL from July 2012 to July 2022. A systematic literature review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis was conducted using the keywords "Epilepsy" and "PET or SPECT." We included both prospective and retrospective studies. Studies with preclinical subjects or not focusing on EZ localization or surgical outcome prediction using recently developed PET radiopharmaceuticals, novel image analysis techniques, and new software were excluded from the review. The remaining 162 articles were reviewed. Results We first present recent findings and developments in PET radiopharmaceuticals. Second, we present novel image analysis techniques and new software in the last decade for EZ localization. Finally, we summarize the overall findings and discuss future perspectives in the field of PET and SPECT in epilepsy. Conclusion Combining new radiopharmaceutical development, new indications, new techniques, and software improves EZ localization and provides a better understanding of epilepsy. These have proven not to only predict prognosis but also to improve the outcome of epilepsy surgery.
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Affiliation(s)
- Chanan Sukprakun
- Division of Nuclear Medicine, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Supatporn Tepmongkol
- Division of Nuclear Medicine, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand,Chulalongkorn University Biomedical Imaging Group (CUBIG), Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand,Chula Neuroscience Center, King Chulalongkorn Memorial Hospital, Bangkok, Thailand,Cognitive Impairment and Dementia Research Unit, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand,*Correspondence: Supatporn Tepmongkol ✉
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Escobar-Ipuz F, Torres A, García-Jiménez M, Basar C, Cascón J, Mateo J. Prediction of patients with idiopathic generalized epilepsy from healthy controls using machine learning from scalp EEG recordings. Brain Res 2022; 1798:148131. [DOI: 10.1016/j.brainres.2022.148131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 10/14/2022] [Accepted: 10/23/2022] [Indexed: 11/05/2022]
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Sollee J, Tang L, Igiraneza AB, Xiao B, Bai HX, Yang L. Artificial Intelligence for Medical Image Analysis in Epilepsy. Epilepsy Res 2022; 182:106861. [DOI: 10.1016/j.eplepsyres.2022.106861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 11/18/2021] [Accepted: 01/16/2022] [Indexed: 11/16/2022]
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Beheshti I, Sone D, Maikusa N, Kimura Y, Shigemoto Y, Sato N, Matsuda H. Accurate lateralization and classification of MRI-negative 18F-FDG-PET-positive temporal lobe epilepsy using double inversion recovery and machine-learning. Comput Biol Med 2021; 137:104805. [PMID: 34464851 DOI: 10.1016/j.compbiomed.2021.104805] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 08/03/2021] [Accepted: 08/23/2021] [Indexed: 12/28/2022]
Abstract
OBJECTIVE The main objective of this study was to determine the ability of double inversion recovery (DIR) data coupled with machine-learning algorithms to distinguish normal individuals from epileptic subjects and to identify the laterality of the focus side in MRI-negative, PET-positive temporal lobe epilepsy (TLE) patients. MATERIALS AND METHODS We used whole-brain DIR data as the input features with which to train a linear support-vector machine model in 63 participants who underwent high-resolution structural MRI and DIR scans. The subjects included 20 left TLE patients, 19 right TLE patients, and 24 healthy controls (HCs). RESULTS Using the DIR data, we achieved a robust accuracy of 87.30% for discriminating among the left TLE, right TLE, and HC groups as well as 84.61%, 97.72%, and 93.02% prediction accuracies for distinguishing left TLE from right TLE, HC from right TLE, and HC from left TLE, respectively. INTERPRETATION Our experimental results suggest that DIR data coupled with machine-learning algorithms provide a promising approach to identifying MRI-negative TLE patients, especially when fluorodeoxyglucose-PET is not available.
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Affiliation(s)
- Iman Beheshti
- Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada; Cyclotron and Drug Discovery Research Center, Southern TOHOKU Research Institute for Neuroscience, 7- 61-2, Yatsuyamada, Koriyama, 963-8052, Japan.
| | - Daichi Sone
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, 4-1-1, Ogawahigashi-cho, Kodaira, Tokyo, 187-8551, Japan; Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, United Kingdom; Department of Psychiatry, The Jikei University School of Medicine, 3-25-8, Nishishimbashi, Minato, Tokyo, 105-8461, Japan
| | - Norihide Maikusa
- Department of Radiology, National Center of Neurology and Psychiatry, 4-1-1, Ogawahigashi-cho, Kodaira, Tokyo, 187-8551, Japan
| | - Yukio Kimura
- Department of Radiology, National Center of Neurology and Psychiatry, 4-1-1, Ogawahigashi-cho, Kodaira, Tokyo, 187-8551, Japan
| | - Yoko Shigemoto
- Department of Radiology, National Center of Neurology and Psychiatry, 4-1-1, Ogawahigashi-cho, Kodaira, Tokyo, 187-8551, Japan
| | - Noriko Sato
- Department of Radiology, National Center of Neurology and Psychiatry, 4-1-1, Ogawahigashi-cho, Kodaira, Tokyo, 187-8551, Japan
| | - Hiroshi Matsuda
- Cyclotron and Drug Discovery Research Center, Southern TOHOKU Research Institute for Neuroscience, 7- 61-2, Yatsuyamada, Koriyama, 963-8052, Japan; Department of Radiology, National Center of Neurology and Psychiatry, 4-1-1, Ogawahigashi-cho, Kodaira, Tokyo, 187-8551, Japan
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Comparison of multimodal findings on epileptogenic side in temporal lobe epilepsy using self-organizing maps. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2021; 35:249-266. [PMID: 34347200 DOI: 10.1007/s10334-021-00948-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 06/20/2021] [Accepted: 07/19/2021] [Indexed: 10/20/2022]
Abstract
OBJECTIVE To develop a decision-making tool to evaluate and compare the performance of neuroimaging markers with clinical findings and the significance of attributes for presurgical lateralization of mesial temporal lobe epilepsy (mTLE). METHODS Thirty-five unilateral mTLE patients who qualified as candidates for surgical resection were studied. Seizure semiology, ictal EEG, ictal epileptogenic zone, interictal-irritative zone, and MRI findings were used as clinical markers. Hippocampal T1 volumetry and FLAIR intensity, DTI estimated; mean diffusivity (MD) in the hippocampus and fractional anisotropy (FA) in posteroinferior cingulum and crus of fornix, and the output of logistic regression method on volumetrics of the hippocampus, amygdala, and thalamus were adopted as neuroimaging markers. The self-organizing map (SOM) method was applied to markers to provide predictive methods for mTLE lateralization. RESULTS The SOM clustered all clinical attributes correctly with 100% accuracy and sensitivity for both the left and right mTLE. Among the clinical markers, seizure semiology and interictal-irrelative zone are the most sensitive attribute for the left-mTLE group lateralization. The accuracy achieved by applying the SOM method to the neuroimaging attributes was 94%, while the sensitivity was achieved 90% for left and 100% for right mTLE. SOM evidence indicated that the hippocampal volume is the most sensitive attribute for the prediction of the laterality in left-mTLE groups. CONCLUSION The proposed SOM method showed that neuroimaging markers may not replace with clinical findings. Nevertheless, multimodal neuroimaging can play an effective role in preoperative lateralization to reduce the costs and risks of surgical resection.
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Sone D. Making the Invisible Visible: Advanced Neuroimaging Techniques in Focal Epilepsy. Front Neurosci 2021; 15:699176. [PMID: 34385902 PMCID: PMC8353251 DOI: 10.3389/fnins.2021.699176] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 06/28/2021] [Indexed: 12/30/2022] Open
Abstract
It has been a clinically important, long-standing challenge to accurately localize epileptogenic focus in drug-resistant focal epilepsy because more intensive intervention to the detected focus, including resection neurosurgery, can provide significant seizure reduction. In addition to neurophysiological examinations, neuroimaging plays a crucial role in the detection of focus by providing morphological and neuroanatomical information. On the other hand, epileptogenic lesions in the brain may sometimes show only subtle or even invisible abnormalities on conventional MRI sequences, and thus, efforts have been made for better visualization and improved detection of the focus lesions. Recent advance in neuroimaging has been attracting attention because of the potentials to better visualize the epileptogenic lesions as well as provide novel information about the pathophysiology of epilepsy. While the progress of newer neuroimaging techniques, including the non-Gaussian diffusion model and arterial spin labeling, could non-invasively detect decreased neurite parameters or hypoperfusion within the focus lesions, advances in analytic technology may also provide usefulness for both focus detection and understanding of epilepsy. There has been an increasing number of clinical and experimental applications of machine learning and network analysis in the field of epilepsy. This review article will shed light on recent advances in neuroimaging for focal epilepsy, including both technical progress of images and newer analytical methodologies and discuss about the potential usefulness in clinical practice.
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Affiliation(s)
- Daichi Sone
- Department of Psychiatry, The Jikei University School of Medicine, Tokyo, Japan.,Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, United Kingdom
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Sone D, Beheshti I. Clinical Application of Machine Learning Models for Brain Imaging in Epilepsy: A Review. Front Neurosci 2021; 15:684825. [PMID: 34239413 PMCID: PMC8258163 DOI: 10.3389/fnins.2021.684825] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 05/31/2021] [Indexed: 12/13/2022] Open
Abstract
Epilepsy is a common neurological disorder characterized by recurrent and disabling seizures. An increasing number of clinical and experimental applications of machine learning (ML) methods for epilepsy and other neurological and psychiatric disorders are available. ML methods have the potential to provide a reliable and optimal performance for clinical diagnoses, prediction, and personalized medicine by using mathematical algorithms and computational approaches. There are now several applications of ML for epilepsy, including neuroimaging analyses. For precise and reliable clinical applications in epilepsy and neuroimaging, the diverse ML methodologies should be examined and validated. We review the clinical applications of ML models for brain imaging in epilepsy obtained from a PubMed database search in February 2021. We first present an overview of typical neuroimaging modalities and ML models used in the epilepsy studies and then focus on the existing applications of ML models for brain imaging in epilepsy based on the following clinical aspects: (i) distinguishing individuals with epilepsy from healthy controls, (ii) lateralization of the temporal lobe epilepsy focus, (iii) the identification of epileptogenic foci, (iv) the prediction of clinical outcomes, and (v) brain-age prediction. We address the practical problems and challenges described in the literature and suggest some future research directions.
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Affiliation(s)
- Daichi Sone
- Department of Psychiatry, The Jikei University School of Medicine, Tokyo, Japan.,Department of Clinical and Experimental Epilepsy, University College London Institute of Neurology, London, United Kingdom
| | - Iman Beheshti
- Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada
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Ebrahimi T, Tafakhori A, Hashemi H, Ali Oghabian M. An interictal measurement of cerebral oxygen extraction fraction in MRI-negative refractory epilepsy using quantitative susceptibility mapping. Phys Med 2021; 85:87-97. [PMID: 33984822 DOI: 10.1016/j.ejmp.2021.03.039] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 03/19/2021] [Accepted: 03/30/2021] [Indexed: 11/16/2022] Open
Abstract
PURPOSE Oxygen extraction fraction (OEF) can be a factor to identify brain tissue's disability in epileptic patients. This study aimed to assess the OEF's level measurement in refractory epileptic patients (REPs) using a quantitative susceptibility mapping (QSM) method and to determine whether the OEF parameters change. METHODS QSM-OEF maps of 26 REPs and 16 healthy subjects were acquired using 3T MRI with a 64-channel coil. Eighteen regions-of-interest (ROIs) were chosen around the cortex in one appropriate slice of the brain and the mean QSM-OEF for each ROI was obtained. The correlations of QSM-OEF among different clinical characteristics of the disease, as well as between the patients and normal subjects, were also investigated. RESULTS QSM-OEF was shown to be significantly higher in REPs (44.9 ± 5.8) than that in HS (41.9 ± 6.2) (p < 0.05). Mean QSM-OEF was statistically lower in the ipsilateral side (44.5 ± 6.6) compared to the contralateral side (46.4 ± 6.8) (P < 0.01). QSM-OEF was illustrated to have a strong positive correlation with the attack duration (r = 0.6), and a moderate negative correlation with the attack frequency (r = -0.3). Using an optimized support vector machine algorithm, we could predict the disease in subjects having abnormal OEF values in the brain-selected-ROIs with sensitivity, specificity, AUC, and the precision of 0.96, 1, 0.98, and 1, respectively. CONCLUSIONS The results of this study revealed that QSM-OEF of the REPs' brain is higher than that of HS, which indicates that QSM-OEF is associated with disease activity.
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Affiliation(s)
- Tayyebeh Ebrahimi
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran; Department of Neuroimaging and Analysis, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran.
| | - Abbas Tafakhori
- Iranian Center of Neurological Research (ICNR), Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran.
| | - Hassan Hashemi
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran.
| | - Mohammad Ali Oghabian
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran; Department of Neuroimaging and Analysis, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran; Research Center for Molecular and Cellular Imaging, Tehran University of Medical Science, Tehran, Iran.
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