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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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Kronlage C, Heide EC, Hagberg GE, Bender B, Scheffler K, Martin P, Focke N. MP2RAGE vs. MPRAGE surface-based morphometry in focal epilepsy. PLoS One 2024; 19:e0296843. [PMID: 38330027 PMCID: PMC10852321 DOI: 10.1371/journal.pone.0296843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 12/19/2023] [Indexed: 02/10/2024] Open
Abstract
In drug-resistant focal epilepsy, detecting epileptogenic lesions using MRI poses a critical diagnostic challenge. Here, we assessed the utility of MP2RAGE-a T1-weighted sequence with self-bias correcting properties commonly utilized in ultra-high field MRI-for the detection of epileptogenic lesions using a surface-based morphometry pipeline based on FreeSurfer, and compared it to the common approach using T1w MPRAGE, both at 3T. We included data from 32 patients with focal epilepsy (5 MRI-positive, 27 MRI-negative with lobar seizure onset hypotheses) and 94 healthy controls from two epilepsy centres. Surface-based morphological measures and intensities were extracted and evaluated in univariate GLM analyses as well as multivariate unsupervised 'novelty detection' machine learning procedures. The resulting prediction maps were analyzed over a range of possible thresholds using alternative free-response receiver operating characteristic (AFROC) methodology with respect to the concordance with predefined lesion labels or hypotheses on epileptogenic zone location. We found that MP2RAGE performs at least comparable to MPRAGE and that especially analysis of MP2RAGE image intensities may provide additional diagnostic information. Secondly, we demonstrate that unsupervised novelty-detection machine learning approaches may be useful for the detection of epileptogenic lesions (maximum AFROC AUC 0.58) when there is only a limited lesional training set available. Third, we propose a statistical method of assessing lesion localization performance in MRI-negative patients with lobar hypotheses of the epileptogenic zone based on simulation of a random guessing process as null hypothesis. Based on our findings, it appears worthwhile to study similar surface-based morphometry approaches in ultra-high field MRI (≥ 7 T).
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Affiliation(s)
- Cornelius Kronlage
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tuebingen, Tuebingen, Germany
| | - Ev-Christin Heide
- Clinic of Neurology, University Medical Center Goettingen, Goettingen, Germany
| | - Gisela E. Hagberg
- High-Field MR Centre, Max-Planck-Institute for Biological Cybernetics, Tuebingen, Germany
- Department for Biomedical Magnetic Resonances, University of Tuebingen, Tuebingen, Germany
| | - Benjamin Bender
- Department of Neuroradiology, University of Tuebingen, Tuebingen, Germany
| | - Klaus Scheffler
- High-Field MR Centre, Max-Planck-Institute for Biological Cybernetics, Tuebingen, Germany
- Department for Biomedical Magnetic Resonances, University of Tuebingen, Tuebingen, Germany
| | - Pascal Martin
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tuebingen, Tuebingen, Germany
| | - Niels Focke
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tuebingen, Tuebingen, Germany
- Clinic of Neurology, University Medical Center Goettingen, Goettingen, Germany
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3
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Jiménez-Murillo D, Castro-Ospina AE, Duque-Muñoz L, Martínez-Vargas JD, Suárez-Revelo JX, Vélez-Arango JM, de la Iglesia-Vayá M. Automatic Detection of Focal Cortical Dysplasia Using MRI: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:7072. [PMID: 37631608 PMCID: PMC10458261 DOI: 10.3390/s23167072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 08/02/2023] [Accepted: 08/07/2023] [Indexed: 08/27/2023]
Abstract
Focal cortical dysplasia (FCD) is a congenital brain malformation that is closely associated with epilepsy. Early and accurate diagnosis is essential for effectively treating and managing FCD. Magnetic resonance imaging (MRI)-one of the most commonly used non-invasive neuroimaging methods for evaluating the structure of the brain-is often implemented along with automatic methods to diagnose FCD. In this review, we define three categories for FCD identification based on MRI: visual, semi-automatic, and fully automatic methods. By conducting a systematic review following the PRISMA statement, we identified 65 relevant papers that have contributed to our understanding of automatic FCD identification techniques. The results of this review present a comprehensive overview of the current state-of-the-art in the field of automatic FCD identification and highlight the progress made and challenges ahead in developing reliable, efficient methods for automatic FCD diagnosis using MRI images. Future developments in this area will most likely lead to the integration of these automatic identification tools into medical image-viewing software, providing neurologists and radiologists with enhanced diagnostic capabilities. Moreover, new MRI sequences and higher-field-strength scanners will offer improved resolution and anatomical detail for precise FCD characterization. This review summarizes the current state of automatic FCD identification, thereby contributing to a deeper understanding and the advancement of FCD diagnosis and management.
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Affiliation(s)
- David Jiménez-Murillo
- Grupo de investigación Máquinas Inteligentes y Reconocimiento de Patrones, Instituto Tecnológico Metropolitano, Medellín 050013, Colombia; (D.J.-M.); (L.D.-M.)
| | - Andrés Eduardo Castro-Ospina
- Grupo de investigación Máquinas Inteligentes y Reconocimiento de Patrones, Instituto Tecnológico Metropolitano, Medellín 050013, Colombia; (D.J.-M.); (L.D.-M.)
| | - Leonardo Duque-Muñoz
- Grupo de investigación Máquinas Inteligentes y Reconocimiento de Patrones, Instituto Tecnológico Metropolitano, Medellín 050013, Colombia; (D.J.-M.); (L.D.-M.)
| | | | - Jazmín Ximena Suárez-Revelo
- Grupo de Investigación en Imágenes Médicas SURA, Ayudas Diagnósticas SURA, Carrera 48 # 26-50, Piso 2, Medellín 050021, Colombia; (J.X.S.-R.); (J.M.V.-A.)
| | - Jorge Mario Vélez-Arango
- Grupo de Investigación en Imágenes Médicas SURA, Ayudas Diagnósticas SURA, Carrera 48 # 26-50, Piso 2, Medellín 050021, Colombia; (J.X.S.-R.); (J.M.V.-A.)
| | - Maria de la Iglesia-Vayá
- Biomedical Imaging Unit FISABIO-CIPF, Foundation for the Promotion of the Research in Healthcare and Biomedicine (FISABIO), Avda. de Catalunya, 21, 46020 Valencia, Spain;
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM-G23), 28029 Madrid, Spain
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Azzony S, Moria K, Alghamdi J. Detecting Cortical Thickness Changes in Epileptogenic Lesions Using Machine Learning. Brain Sci 2023; 13:brainsci13030487. [PMID: 36979297 PMCID: PMC10046408 DOI: 10.3390/brainsci13030487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 02/25/2023] [Accepted: 03/07/2023] [Indexed: 03/16/2023] Open
Abstract
Epilepsy is a neurological disorder characterized by abnormal brain activity. Epileptic patients suffer from unpredictable seizures, which may cause a loss of awareness. Seizures are considered drug resistant if treatment does not affect success. This leads practitioners to calculate the cortical thickness to measure the distance between the brain’s white and grey matter surfaces at various locations to perform a surgical intervention. In this study, we introduce using machine learning as an approach to classify extracted measurements from T1-weighted magnetic resonance imaging. Data were collected from the epilepsy unit at King Abdulaziz University Hospital. We applied two trials to classify the extracted measurements from T1-weighted MRI for drug-resistant epilepsy and healthy control subjects. The preprocessing sequence on T1-weighted MRI images was performed using C++ through BrainSuite’s pipeline. The first trial was performed on seven different combinations of four commonly selected measurements. The best performance was achieved in Exp6 and Exp7, with 80.00% accuracy, 83.00% recall score, and 83.88% precision. It is noticeable that grey matter volume and white matter volume measurements are more significant than the cortical thickness measurement. The second trial applied four different machine learning classifiers after applying 10-fold cross-validation and principal component analysis on all extracted measurements as in the first trial based on the mentioned previous works. The K-nearest neighbours model outperformed the other machine learning classifiers with 97.11% accuracy, 75.00% recall score, and 75.00% precision.
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Affiliation(s)
- Sumayya Azzony
- Department of Computer Sciences, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Correspondence:
| | - Kawthar Moria
- Department of Computer Sciences, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Jamaan Alghamdi
- Diagnostic Radiology Department, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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Ganji Z, Aghaee Hakak M, Zare H. Comparison of machine learning methods for the detection of focal cortical dysplasia lesions: decision tree, support vector machine and artificial neural network. Neurol Res 2022; 44:1142-1149. [PMID: 35981138 DOI: 10.1080/01616412.2022.2112381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
BACKGROUND Accurate classification of focal cortical dysplasia (FCD) has been challenging due to the problematic visual detection in magnetic resonance imaging (MRI). Hence, recently, there has been a necessity for employing new techniques to solve the problem. Among the new techniques for FCD lesion diagnosis, classification techniques can be of great help in FCD patient's detection from healthy individuals. METHODS MRI data were collected from 58 participants (30 subjects with FCD type II and 28 normal subjects). Morphological and intensity-based characteristics were calculated for each cortical level and then the performance of the three classifiers: decision tree (DT), support vector machine (SVM) and artificial neural network (ANN) was evaluated. RESULTS Metrics for evaluating classification methods, sensitivity, specificity and accuracy for the DT were 90%, 100% and 95.8%, respectively; it was 95%, 100% and 97.9% for the SVM and 96.7%, 100% and 98.6% for the ANN. CONCLUSION Comparison of the performance of the three classifications used in this study showed that all three have excellent performance in specificity, but in terms of classification sensitivity and accuracy, the artificial neural network method has worked better.
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Affiliation(s)
- Zohreh Ganji
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mohsen Aghaee Hakak
- Epilepsy Monitoring Unit, Research and Education Department, Razavi Hospital, Mashhad, Iran
| | - Hoda Zare
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.,Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
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Jeong JW, Lee MH, Kuroda N, Sakakura K, O'Hara N, Juhasz C, Asano E. Multi-Scale Deep Learning of Clinically Acquired Multi-Modal MRI Improves the Localization of Seizure Onset Zone in Children With Drug-Resistant Epilepsy. IEEE J Biomed Health Inform 2022; 26:5529-5539. [PMID: 35925854 PMCID: PMC9710730 DOI: 10.1109/jbhi.2022.3196330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The present study investigates the effectiveness of a deep learning neural network for non-invasively localizing the seizure onset zone (SOZ) using multi-modal MRI data that are clinically acquired from children with drug-resistant epilepsy. A cortical parcellation was applied to localize the SOZ in cortical nodes of the epileptogenic hemisphere. At each node, the laminar surface analysis was followed to sample 1) the relative intensity of gray matter and white matter in multi-modal MRI and 2) the neighboring white matter connectivity using diffusion tractography edge strengths. A cross-validation was employed to train and test all layers of a multi-scale residual neural network (msResNet) that can classify SOZ node in an end-to-end fashion. A prediction probability of a given node belonging to the SOZ class was proposed as a non-invasive MRI marker of seizure onset likelihood. In an independent validation cohort, the proposed MRI marker provided a very large effect size of Cohen's d = 1.21 between SOZ and non-SOZ, and classified SOZ with a balanced accuracy of 0.75 in lesional and 0.67 in non-lesional MRI groups. The subsequent multi-variate logistic regression found the incorporation of the proposed MRI marker into interictal intracranial EEG (iEEG) markers further improves the differentiation between the epileptogenic focus (defined as SOZ resected during surgery) and non-epileptogenic sites (i.e., non-SOZ sites preserved during surgery) up to 15 % in non-lesional MRI group, suggesting that the proposed MRI marker could improve the localization of epileptogenic foci for successful pediatric epilepsy surgery.
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Nandakumar N, Hsu D, Ahmed R, Venkataraman A. DeepEZ: A Graph Convolutional Network for Automated Epileptogenic Zone Localization from Resting-State fMRI Connectivity. IEEE Trans Biomed Eng 2022; 70:216-227. [PMID: 35776823 PMCID: PMC9841829 DOI: 10.1109/tbme.2022.3187942] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
OBJECTIVE Epileptogenic zone (EZ) localization is a crucial step during diagnostic work up and therapeutic planning in medication refractory epilepsy. In this paper, we present the first deep learning approach to localize the EZ based on resting-state fMRI (rs-fMRI) data. METHODS Our network, called DeepEZ, uses a cascade of graph convolutions that emphasize signal propagation along expected anatomical pathways. We also integrate domain-specific information, such as an asymmetry term on the predicted EZ and a learned subject-specific bias to mitigate environmental confounds. RESULTS We validate DeepEZ on rs-fMRI collected from 14 patients with focal epilepsy at the University of Wisconsin Madison. Using cross validation, we demonstrate that DeepEZ achieves consistently high EZ localization performance (Accuracy: 0.88 ± 0.03; AUC: 0.73 ± 0.03) that far outstripped any of the baseline methods. This performance is notable given the variability in EZ locations and scanner type across the cohort. CONCLUSION Our results highlight the promise of using DeepEZ as an accurate and noninvasive therapeutic planning tool for medication refractory epilepsy. SIGNIFICANCE While prior work in EZ localization focused on identifying localized aberrant signatures, there is growing evidence that epileptic seizures affect inter-regional connectivity in the brain. DeepEZ allows clinicians to harness this information from noninvasive imaging that can easily be integrated into the existing clinical workflow.
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Affiliation(s)
- Naresh Nandakumar
- Department of Electrical and Computer Engineering, Johns Hopkins University, USA
| | - David Hsu
- Department of Neurology, University of Wisconsin, USA
| | - Raheel Ahmed
- Department of Neurosurgery, University of Wisconsin, USA
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218 USA
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Luckett PH, Maccotta L, Lee JJ, Park KY, Dosenbach NU, Ances BM, Hogan RE, Shimony JS, Leuthardt EC. Deep learning resting state fMRI lateralization of temporal lobe epilepsy. Epilepsia 2022; 63:1542-1552. [PMID: 35320587 DOI: 10.1111/epi.17233] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 03/21/2022] [Accepted: 03/21/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVE Localization of focal epilepsy is critical for surgical treatment of refractory seizures. There remains a great need for non-invasive techniques to localize seizures for surgical decision-making. We investigate the use of deep learning using resting state functional MRI (RS-fMRI) to identify the hemisphere of seizure onset in temporal lobe epilepsy (TLE) patients. METHODS 2132 healthy controls and 32 pre-operative TLE patients were studied. All participants underwent structural MRI and RS-fMRI. Healthy control data was used to generate training samples for a 3D convolutional neural network (3DCNN). RS-fMRI was synthetically altered in randomly lateralized regions in the healthy control participants. The model was then trained to classify the hemisphere containing synthetic noise. Finally, the model was tested on TLE patients to assess its performance for detecting biological seizure-onset zones, and gradient-weighted class activation mapping (Grad-CAM) identified the strongest predictive regions. RESULTS The 3DCNN classified healthy control hemispheres known to contain synthetic noise with 96% accuracy, and TLE hemispheres clinically identified to be seizure onset zones with 90.6% accuracy. Grad-CAM identified a range of temporal, frontal, parietal, and subcortical regions that were strong anatomical predictors of the seizure onset zone, while the resting state networks which colocalized with Grad-CAM results included default mode, medial temporal, and dorsal attention networks. Lastly, in an analysis of a subset of patients with post-surgical outcomes, the 3DCNN leveraged a more focal set of regions to achieve classification in patients with Engel class > 1 compared to Engel class 1. SIGNIFICANCE Non-invasive techniques capable of localizing the seizure-onset zone could improve pre-surgical planning in patients with intractable epilepsy. We have demonstrated the ability of deep learning to identify the correct hemisphere of the seizure onset zone in TLE patients using RS-fMRI with high accuracy. This approach represents a novel technique of seizure lateralization that could improve preoperative surgical planning.
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Affiliation(s)
- Patrick H Luckett
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis
| | - Luigi Maccotta
- Department of Neurology, Washington University School of Medicine, St. Louis
| | - John J Lee
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis
| | - Ki Yun Park
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis
| | - Nico Uf Dosenbach
- Department of Neurology, Washington University School of Medicine, St. Louis
| | - Beau M Ances
- Department of Neurology, Washington University School of Medicine, St. Louis
| | - R Edward Hogan
- Department of Neurology, Washington University School of Medicine, St. Louis
| | - Joshua S Shimony
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis
| | - Eric C Leuthardt
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis
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AIM in Clinical Neurophysiology and Electroencephalography (EEG). Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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10
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Yuan J, Ran X, Liu K, Yao C, Yao Y, Wu H, Liu Q. Machine learning applications on neuroimaging for diagnosis and prognosis of epilepsy: A review. J Neurosci Methods 2021; 368:109441. [PMID: 34942271 DOI: 10.1016/j.jneumeth.2021.109441] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 10/23/2021] [Accepted: 12/11/2021] [Indexed: 02/07/2023]
Abstract
Machine learning is playing an increasingly important role in medical image analysis, spawning new advances in the clinical application of neuroimaging. There have been some reviews on machine learning and epilepsy before, and they mainly focused on electrophysiological signals such as electroencephalography (EEG) and stereo electroencephalography (SEEG), while neglecting the potential of neuroimaging in epilepsy research. Neuroimaging has its important advantages in confirming the range of the epileptic region, which is essential in presurgical evaluation and assessment after surgery. However, it is difficult for EEG to locate the accurate epilepsy lesion region in the brain. In this review, we emphasize the interaction between neuroimaging and machine learning in the context of epilepsy diagnosis and prognosis. We start with an overview of epilepsy and typical neuroimaging modalities used in epilepsy clinics, MRI, DWI, fMRI, and PET. Then, we elaborate two approaches in applying machine learning methods to neuroimaging data: (i) the conventional machine learning approach combining manual feature engineering and classifiers, (ii) the deep learning approach, such as the convolutional neural networks and autoencoders. Subsequently, the application of machine learning on epilepsy neuroimaging, such as segmentation, localization, and lateralization tasks, as well as tasks directly related to diagnosis and prognosis are looked into in detail. Finally, we discuss the current achievements, challenges, and potential future directions in this field, hoping to pave the way for computer-aided diagnosis and prognosis of epilepsy.
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Affiliation(s)
- Jie Yuan
- Shenzhen Key Laboratory of Smart Healthcare Engineering, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, PR China
| | - Xuming Ran
- Shenzhen Key Laboratory of Smart Healthcare Engineering, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, PR China
| | - Keyin Liu
- Shenzhen Key Laboratory of Smart Healthcare Engineering, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, PR China
| | - Chen Yao
- Shenzhen Second People's Hospital, Shenzhen 518035, PR China
| | - Yi Yao
- Shenzhen Children's Hospital, Shenzhen 518017, PR China
| | - Haiyan Wu
- Centre for Cognitive and Brain Sciences and Department of Psychology, University of Macau, Taipa, Macau
| | - Quanying Liu
- Shenzhen Key Laboratory of Smart Healthcare Engineering, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, PR China.
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Varotto G, Susi G, Tassi L, Gozzo F, Franceschetti S, Panzica F. Comparison of Resampling Techniques for Imbalanced Datasets in Machine Learning: Application to Epileptogenic Zone Localization From Interictal Intracranial EEG Recordings in Patients With Focal Epilepsy. Front Neuroinform 2021; 15:715421. [PMID: 34867255 PMCID: PMC8641296 DOI: 10.3389/fninf.2021.715421] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 09/23/2021] [Indexed: 11/30/2022] Open
Abstract
Aim: In neuroscience research, data are quite often characterized by an imbalanced distribution between the majority and minority classes, an issue that can limit or even worsen the prediction performance of machine learning methods. Different resampling procedures have been developed to face this problem and a lot of work has been done in comparing their effectiveness in different scenarios. Notably, the robustness of such techniques has been tested among a wide variety of different datasets, without considering the performance of each specific dataset. In this study, we compare the performances of different resampling procedures for the imbalanced domain in stereo-electroencephalography (SEEG) recordings of the patients with focal epilepsies who underwent surgery. Methods: We considered data obtained by network analysis of interictal SEEG recorded from 10 patients with drug-resistant focal epilepsies, for a supervised classification problem aimed at distinguishing between the epileptogenic and non-epileptogenic brain regions in interictal conditions. We investigated the effectiveness of five oversampling and five undersampling procedures, using 10 different machine learning classifiers. Moreover, six specific ensemble methods for the imbalanced domain were also tested. To compare the performances, Area under the ROC curve (AUC), F-measure, Geometric Mean, and Balanced Accuracy were considered. Results: Both the resampling procedures showed improved performances with respect to the original dataset. The oversampling procedure was found to be more sensitive to the type of classification method employed, with Adaptive Synthetic Sampling (ADASYN) exhibiting the best performances. All the undersampling approaches were more robust than the oversampling among the different classifiers, with Random Undersampling (RUS) exhibiting the best performance despite being the simplest and most basic classification method. Conclusions: The application of machine learning techniques that take into consideration the balance of features by resampling is beneficial and leads to more accurate localization of the epileptogenic zone from interictal periods. In addition, our results highlight the importance of the type of classification method that must be used together with the resampling to maximize the benefit to the outcome.
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Affiliation(s)
- Giulia Varotto
- Epilepsy Unit, Bioengineering Group, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy.,Neurophysiopathology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Gianluca Susi
- Universidad Complutense de Madrid-Universidad Politécnica de Madrid (UPM-UCM) Laboratory of Cognitive and Computational Neuroscience, Center of Biomedical Technology, Technical University of Madrid, Madrid, Spain.,Department of Experimental Psychology, Cognitive Processes and Logopedy, Complutense University of Madrid, Madrid, Spain
| | - Laura Tassi
- "Claudio Munari" Epilepsy Surgery Centre, Niguarda Hospital, Milan, Italy
| | - Francesca Gozzo
- "Claudio Munari" Epilepsy Surgery Centre, Niguarda Hospital, Milan, Italy
| | - Silvana Franceschetti
- Neurophysiopathology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Ferruccio Panzica
- Clinical Engineering, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
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12
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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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Zoli M, Staartjes VE, Guaraldi F, Friso F, Rustici A, Asioli S, Sollini G, Pasquini E, Regli L, Serra C, Mazzatenta D. Machine learning-based prediction of outcomes of the endoscopic endonasal approach in Cushing disease: is the future coming? Neurosurg Focus 2021; 48:E5. [PMID: 32480364 DOI: 10.3171/2020.3.focus2060] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 03/04/2020] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Machine learning (ML) is an innovative method to analyze large and complex data sets. The aim of this study was to evaluate the use of ML to identify predictors of early postsurgical and long-term outcomes in patients treated for Cushing disease (CD). METHODS All consecutive patients in our center who underwent surgery for CD through the endoscopic endonasal approach were retrospectively reviewed. Study endpoints were gross-tumor removal (GTR), postsurgical remission, and long-term control of disease. Several demographic, radiological, and histological factors were assessed as potential predictors. For ML-based modeling, data were randomly divided into 2 sets with an 80% to 20% ratio for bootstrapped training and testing, respectively. Several algorithms were tested and tuned for the area under the curve (AUC). RESULTS The study included 151 patients. GTR was achieved in 137 patients (91%), and postsurgical hypersecretion remission was achieved in 133 patients (88%). At last follow-up, 116 patients (77%) were still in remission after surgery and in 21 patients (14%), CD was controlled with complementary treatment (overall, of 131 cases, 87% were under control at follow-up). At internal validation, the endpoints were predicted with AUCs of 0.81-1.00, accuracy of 81%-100%, and Brier scores of 0.035-0.151. Tumor size and invasiveness and histological confirmation of adrenocorticotropic hormone (ACTH)-secreting cells were the main predictors for the 3 endpoints of interest. CONCLUSIONS ML algorithms were used to train and internally validate robust models for all the endpoints, giving accurate outcome predictions in CD cases. This analytical method seems promising for potentially improving future patient care and counseling; however, careful clinical interpretation of the results remains necessary before any clinical adoption of ML. Moreover, further studies and increased sample sizes are definitely required before the widespread adoption of ML to the study of CD.
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Affiliation(s)
- Matteo Zoli
- 1Pituitary Unit, Center for the Diagnosis and Treatment of Hypothalamic-Pituitary Diseases, IRCCS Institute of Neurological Sciences of Bologna.,2Department of Biomedical and Motor Sciences (DIBINEM), University of Bologna, Italy
| | - Victor E Staartjes
- 3Department of Neurosurgery, Clinical Neuroscience Center, University Hospital of Zurich, University of Zurich, Switzerland.,4Neurosurgery, Amsterdam Movement Sciences, Amsterdam UMC, Vrije Universiteit Amsterdam, The Netherlands
| | - Federica Guaraldi
- 1Pituitary Unit, Center for the Diagnosis and Treatment of Hypothalamic-Pituitary Diseases, IRCCS Institute of Neurological Sciences of Bologna.,2Department of Biomedical and Motor Sciences (DIBINEM), University of Bologna, Italy
| | - Filippo Friso
- 1Pituitary Unit, Center for the Diagnosis and Treatment of Hypothalamic-Pituitary Diseases, IRCCS Institute of Neurological Sciences of Bologna
| | - Arianna Rustici
- 5Department of Neuroradiology, IRCCS Istitute of Neurological Sciences of Bologna.,6Department of Experimental, Diagnostic and Specialty Medicine (DIMES), University of Bologna
| | - Sofia Asioli
- 1Pituitary Unit, Center for the Diagnosis and Treatment of Hypothalamic-Pituitary Diseases, IRCCS Institute of Neurological Sciences of Bologna.,2Department of Biomedical and Motor Sciences (DIBINEM), University of Bologna, Italy.,7Section of Anatomic Pathology 'M. Malpighi' at Bellaria Hospital, Bologna; and
| | - Giacomo Sollini
- 1Pituitary Unit, Center for the Diagnosis and Treatment of Hypothalamic-Pituitary Diseases, IRCCS Institute of Neurological Sciences of Bologna.,8ENT Department, Bellaria Hospital, Bologna, Italy
| | - Ernesto Pasquini
- 1Pituitary Unit, Center for the Diagnosis and Treatment of Hypothalamic-Pituitary Diseases, IRCCS Institute of Neurological Sciences of Bologna.,8ENT Department, Bellaria Hospital, Bologna, Italy
| | - Luca Regli
- 3Department of Neurosurgery, Clinical Neuroscience Center, University Hospital of Zurich, University of Zurich, Switzerland
| | - Carlo Serra
- 3Department of Neurosurgery, Clinical Neuroscience Center, University Hospital of Zurich, University of Zurich, Switzerland
| | - Diego Mazzatenta
- 1Pituitary Unit, Center for the Diagnosis and Treatment of Hypothalamic-Pituitary Diseases, IRCCS Institute of Neurological Sciences of Bologna.,2Department of Biomedical and Motor Sciences (DIBINEM), University of Bologna, Italy
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14
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Consales A, Casciato S, Asioli S, Barba C, Caulo M, Colicchio G, Cossu M, de Palma L, Morano A, Vatti G, Villani F, Zamponi N, Tassi L, Di Gennaro G, Marras CE. The surgical treatment of epilepsy. Neurol Sci 2021; 42:2249-2260. [PMID: 33797619 DOI: 10.1007/s10072-021-05198-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Accepted: 03/16/2021] [Indexed: 01/07/2023]
Abstract
In 2009, the Commission for Epilepsy Surgery of the Italian League Against Epilepsy (LICE) conducted an overview about the techniques used for the pre-surgical evaluation and the surgical treatment of epilepsies. The recognition that, in selected cases, surgery can be considered the first-line approach, suggested that the experience gained by the main Italian referral centers should be pooled in order to provide a handy source of reference. In light of the progress made over these past years, some parts of that first report have accordingly been updated. The present revision aims to harmonize the general principles regulating the patient selection and the pre-surgical work-up, as well as to expand the use of epilepsy surgery, that still represents an underutilized resource, regrettably. The objective of this contribution is drawing up a methodological framework within which to integrate the experiences of each group in this complex and dynamic sector of the neurosciences.
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Affiliation(s)
- Alessandro Consales
- Division of Neurosurgery, IRCCS Giannina Gaslini Children's Hospital, Genoa, Italy
| | - Sara Casciato
- Epilepsy Surgery Centre, IRCCS Neuromed, Via Atinense, 18, 86170, Pozzilli, IS, Italy
| | - Sofia Asioli
- Department of Biomedical and Neuromotor Sciences, Section of Anatomic Pathology "M. Malpighi", Bellaria Hospital, Bologna, Italy
| | - Carmen Barba
- Neuroscience Department, Meyer Children's Hospital-University of Florence, Florence, Italy
| | - Massimo Caulo
- Department of Neuroscience, Imaging and Clinical Sciences, "G. D'Annunzio" University, Chieti, Italy
| | | | - Massimo Cossu
- "C. Munari" Epilepsy Surgery Center, Niguarda Hospital, Milan, Italy
| | - Luca de Palma
- Department of Neuroscience and Neurorehabilitation, Bambino Gesù Children Hospital, Rome, Italy
| | - Alessandra Morano
- Department of Human Neurosciences, "Sapienza" University, Rome, Italy
| | - Giampaolo Vatti
- Department of Neurological and Sensorial Sciences, University of Siena, Siena, Italy
| | - Flavio Villani
- Division of Neurophysiology and Epilepsy Centre, IRCCS San Martino Policlinic Hospital, Genoa, Italy
| | - Nelia Zamponi
- Child Neuropsychiatric Unit, University of Ancona, Ancona, Italy
| | - Laura Tassi
- "C. Munari" Epilepsy Surgery Center, Niguarda Hospital, Milan, Italy
| | - Giancarlo Di Gennaro
- Epilepsy Surgery Centre, IRCCS Neuromed, Via Atinense, 18, 86170, Pozzilli, IS, Italy.
| | - Carlo Efisio Marras
- Department of Neuroscience and Neurorehabilitation, Bambino Gesù Children Hospital, Rome, Italy
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15
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Ganji Z, Hakak MA, Zamanpour SA, Zare H. Automatic Detection of Focal Cortical Dysplasia Type II in MRI: Is the Application of Surface-Based Morphometry and Machine Learning Promising? Front Hum Neurosci 2021; 15:608285. [PMID: 33679343 PMCID: PMC7933541 DOI: 10.3389/fnhum.2021.608285] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Accepted: 01/20/2021] [Indexed: 11/24/2022] Open
Abstract
Background and Objectives Focal cortical dysplasia (FCD) is a type of malformations of cortical development and one of the leading causes of drug-resistant epilepsy. Postoperative results improve the diagnosis of lesions on structural MRIs. Advances in quantitative algorithms have increased the identification of FCD lesions. However, due to significant differences in size, shape, and location of the lesion in different patients and a big deal of time for the objective diagnosis of lesion as well as the dependence of individual interpretation, sensitive approaches are required to address the challenge of lesion diagnosis. In this research, a FCD computer-aided diagnostic system to improve existing methods is presented. Methods Magnetic resonance imaging (MRI) data were collected from 58 participants (30 with histologically confirmed FCD type II and 28 without a record of any neurological prognosis). Morphological and intensity-based features were calculated for each cortical surface and inserted into an artificial neural network. Statistical examinations evaluated classifier efficiency. Results Neural network evaluation metrics—sensitivity, specificity, and accuracy—were 96.7, 100, and 98.6%, respectively. Furthermore, the accuracy of the classifier for the detection of the lobe and hemisphere of the brain, where the FCD lesion is located, was 84.2 and 77.3%, respectively. Conclusion Analyzing surface-based features by automated machine learning can give a quantitative and objective diagnosis of FCD lesions in presurgical assessment and improve postsurgical outcomes.
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Affiliation(s)
- Zohreh Ganji
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mohsen Aghaee Hakak
- Epilepsy Monitoring Unit, Research and Education Department, Razavi Hospital, Mashhad, Iran
| | - Seyed Amir Zamanpour
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hoda Zare
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.,Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
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16
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Kanber B, Vos SB, de Tisi J, Wood TC, Barker GJ, Rodionov R, Chowdhury FA, Thom M, Alexander DC, Duncan JS, Winston GP. Detection of covert lesions in focal epilepsy using computational analysis of multimodal magnetic resonance imaging data. Epilepsia 2021; 62:807-816. [PMID: 33567113 PMCID: PMC8436754 DOI: 10.1111/epi.16836] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 12/24/2020] [Accepted: 01/21/2021] [Indexed: 02/01/2023]
Abstract
Objective To compare the location of suspect lesions detected by computational analysis of multimodal magnetic resonance imaging data with areas of seizure onset, early propagation, and interictal epileptiform discharges (IEDs) identified with stereoelectroencephalography (SEEG) in a cohort of patients with medically refractory focal epilepsy and radiologically normal magnetic resonance imaging (MRI) scans. Methods We developed a method of lesion detection using computational analysis of multimodal MRI data in a cohort of 62 control subjects, and 42 patients with focal epilepsy and MRI‐visible lesions. We then applied it to detect covert lesions in 27 focal epilepsy patients with radiologically normal MRI scans, comparing our findings with the areas of seizure onset, early propagation, and IEDs identified at SEEG. Results Seizure‐onset zones (SoZs) were identified at SEEG in 18 of the 27 patients (67%) with radiologically normal MRI scans. In 11 of these 18 cases (61%), concordant abnormalities were detected by our method. In the remaining seven cases, either early seizure propagation or IEDs were observed within the abnormalities detected, or there were additional areas of imaging abnormalities found by our method that were not sampled at SEEG. In one of the nine patients (11%) in whom SEEG was inconclusive, an abnormality, which may have been involved in seizures, was identified by our method and was not sampled at SEEG. Significance Computational analysis of multimodal MRI data revealed covert abnormalities in the majority of patients with refractory focal epilepsy and radiologically normal MRI that co‐located with SEEG defined zones of seizure onset. The method could help identify areas that should be targeted with SEEG when considering epilepsy surgery.
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Affiliation(s)
- Baris Kanber
- Centre for Medical Image Computing, University College London, London, UK.,Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK.,MRI Unit, Epilepsy Society, Chalfont St Peter, UK.,National Institute for Health Research Biomedical Research Centre at University College London and University College London NHS Foundation Trust, London, UK
| | - Sjoerd B Vos
- Centre for Medical Image Computing, University College London, London, UK.,Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK.,MRI Unit, Epilepsy Society, Chalfont St Peter, UK.,National Institute for Health Research Biomedical Research Centre at University College London and University College London NHS Foundation Trust, London, UK.,Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, London, UK
| | - Jane de Tisi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
| | - Tobias C Wood
- Department of Neuroimaging, King's College London, London, UK
| | - Gareth J Barker
- Department of Neuroimaging, King's College London, London, UK
| | - Roman Rodionov
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK.,MRI Unit, Epilepsy Society, Chalfont St Peter, UK
| | - Fahmida Amin Chowdhury
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
| | - Maria Thom
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK.,Division of Neuropathology, The National Hospital for Neurology and Neurosurgery, London, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, University College London, London, UK.,National Institute for Health Research Biomedical Research Centre at University College London and University College London NHS Foundation Trust, London, UK
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK.,MRI Unit, Epilepsy Society, Chalfont St Peter, UK.,National Institute for Health Research Biomedical Research Centre at University College London and University College London NHS Foundation Trust, London, UK
| | - Gavin P Winston
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK.,MRI Unit, Epilepsy Society, Chalfont St Peter, UK.,Department of Medicine, Division of Neurology, Queen's University, Kingston, Canada
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17
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AIM in Clinical Neurophysiology and Electroencephalography (EEG). Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_257-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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18
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Rofatto VF, Matsuoka MT, Klein I, Veronez MR, da Silveira LG. On the effects of hard and soft equality constraints in the iterative outlier elimination procedure. PLoS One 2020; 15:e0238145. [PMID: 32845919 PMCID: PMC7449505 DOI: 10.1371/journal.pone.0238145] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 08/10/2020] [Indexed: 11/18/2022] Open
Abstract
Reliability analysis allows for the estimation of a system’s probability of detecting and identifying outliers. Failure to identify an outlier can jeopardize the reliability level of a system. Due to its importance, outliers must be appropriately treated to ensure the normal operation of a system. System models are usually developed from certain constraints. Constraints play a central role in model precision and validity. In this work, we present a detailed investigation of the effects of the hard and soft constraints on the reliability of a measurement system model. Hard constraints represent a case in which there exist known functional relations between the unknown model parameters, whereas the soft constraints are employed where such functional relations can be slightly violated depending on their uncertainty. The results highlighted that the success rate of identifying an outlier for the case of hard constraints is larger than soft constraints. This suggested that hard constraints be used in the stage of pre-processing data for the purpose of identifying and removing possible outlying measurements. After identifying and removing possible outliers, one should set up the soft constraints to propagate their uncertainties to the model parameters during the data processing.
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Affiliation(s)
- Vinicius Francisco Rofatto
- Graduate Program in Remote Sensing, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
- Institute of Geography, Federal University of Uberlandia, Monte Carmelo, MG, Brazil
- * E-mail:
| | - Marcelo Tomio Matsuoka
- Graduate Program in Remote Sensing, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
- Institute of Geography, Federal University of Uberlandia, Monte Carmelo, MG, Brazil
- Graduate Program in Agriculture and Geospatial Information, Federal University of Uberlandia, Monte Carmelo, MG, Brazil
- Graduate Program in Applied Computing, Unisinos University, São Leopoldo, RS, Brazil
| | - Ivandro Klein
- Department of Civil Construction, Federal Institute of Santa Catarina, Florianópolis, SC, Brazil
- Graduate Program in Geodetic Sciences, Federal University of Paraná, Curitiba, PR, Brazil
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19
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Kanber B, Duncan JS, Rodionov R, Chowdhury FA, Winston GP. Validation of computational lesion detection methods in magnetic resonance imaging-negative, focal epilepsy. Epilepsia 2020; 61:828-830. [PMID: 32100283 DOI: 10.1111/epi.16461] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Accepted: 02/07/2020] [Indexed: 11/26/2022]
Affiliation(s)
- Baris Kanber
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing, University College London, London, UK
- Department of Clinical and Experimental Epilepsy, Queen Square Institute of Neurology, University College London, London, UK
- Magnetic Resonance Imaging Unit, Epilepsy Society, Chalfont Saint Peter, UK
- National Institute for Health Research University College London National Health Service Foundation Trust Biomedical Research Centre, London, UK
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, Queen Square Institute of Neurology, University College London, London, UK
- Magnetic Resonance Imaging Unit, Epilepsy Society, Chalfont Saint Peter, UK
- National Institute for Health Research University College London National Health Service Foundation Trust Biomedical Research Centre, London, UK
| | - Roman Rodionov
- Department of Clinical and Experimental Epilepsy, Queen Square Institute of Neurology, University College London, London, UK
- Magnetic Resonance Imaging Unit, Epilepsy Society, Chalfont Saint Peter, UK
| | - Fahmida Amin Chowdhury
- Department of Clinical and Experimental Epilepsy, Queen Square Institute of Neurology, University College London, London, UK
- Magnetic Resonance Imaging Unit, Epilepsy Society, Chalfont Saint Peter, UK
| | - Gavin P Winston
- Department of Clinical and Experimental Epilepsy, Queen Square Institute of Neurology, University College London, London, UK
- Magnetic Resonance Imaging Unit, Epilepsy Society, Chalfont Saint Peter, UK
- Division of Neurology, Department of Medicine, Queen's University, Kingston, Ontario, Canada
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20
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Alaverdyan Z, Jung J, Bouet R, Lartizien C. Regularized siamese neural network for unsupervised outlier detection on brain multiparametric magnetic resonance imaging: Application to epilepsy lesion screening. Med Image Anal 2019; 60:101618. [PMID: 31841950 DOI: 10.1016/j.media.2019.101618] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Revised: 11/12/2019] [Accepted: 11/13/2019] [Indexed: 10/25/2022]
Abstract
In this study, we propose a novel anomaly detection model targeting subtle brain lesions in multiparametric MRI. To compensate for the lack of annotated data adequately sampling the heterogeneity of such pathologies, we cast this problem as an outlier detection problem and introduce a novel configuration of unsupervised deep siamese networks to learn normal brain representations using a series of non-pathological brain scans. The proposed siamese network, composed of stacked convolutional autoencoders as subnetworks is designed to map patches extracted from healthy control scans only and centered at the same spatial localization to 'close' representations with respect to the chosen metric in a latent space. It is based on a novel loss function combining a similarity term and a regularization term compensating for the lack of dissimilar pairs. These latent representations are then fed into oc-SVM models at voxel-level to produce anomaly score maps. We evaluate the performance of our brain anomaly detection model to detect subtle epilepsy lesions in multiparametric (T1-weighted, FLAIR) MRI exams considered as normal (MRI-negative). Our detection model trained on 75 healthy subjects and validated on 21 epilepsy patients (with 18 MRI-negatives) achieves a maximum sensitivity of 61% on the MRI-negative lesions, identified among the 5 most suspicious detections on average. It is shown to outperform detection models based on the same architecture but with stacked convolutional or Wasserstein autoencoders as unsupervised feature extraction mechanisms.
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Affiliation(s)
- Zaruhi Alaverdyan
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F69621, Lyon, France
| | - Julien Jung
- Lyon Neuroscience Research Center, CRNL, INSERM U1028, CNRS UMR5292, University Lyon 1, Lyon, France
| | - Romain Bouet
- Lyon Neuroscience Research Center, CRNL, INSERM U1028, CNRS UMR5292, University Lyon 1, Lyon, France
| | - Carole Lartizien
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, F69621, Lyon, France.
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21
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Abbasi B, Goldenholz DM. Machine learning applications in epilepsy. Epilepsia 2019; 60:2037-2047. [PMID: 31478577 PMCID: PMC9897263 DOI: 10.1111/epi.16333] [Citation(s) in RCA: 164] [Impact Index Per Article: 32.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Revised: 07/25/2019] [Accepted: 08/12/2019] [Indexed: 02/05/2023]
Abstract
Machine learning leverages statistical and computer science principles to develop algorithms capable of improving performance through interpretation of data rather than through explicit instructions. Alongside widespread use in image recognition, language processing, and data mining, machine learning techniques have received increasing attention in medical applications, ranging from automated imaging analysis to disease forecasting. This review examines the parallel progress made in epilepsy, highlighting applications in automated seizure detection from electroencephalography (EEG), video, and kinetic data, automated imaging analysis and pre-surgical planning, prediction of medication response, and prediction of medical and surgical outcomes using a wide variety of data sources. A brief overview of commonly used machine learning approaches, as well as challenges in further application of machine learning techniques in epilepsy, is also presented. With increasing computational capabilities, availability of effective machine learning algorithms, and accumulation of larger datasets, clinicians and researchers will increasingly benefit from familiarity with these techniques and the significant progress already made in their application in epilepsy.
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Affiliation(s)
- Bardia Abbasi
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA 02215
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22
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Gyebnár G, Klimaj Z, Entz L, Fabó D, Rudas G, Barsi P, Kozák LR. Personalized microstructural evaluation using a Mahalanobis-distance based outlier detection strategy on epilepsy patients' DTI data - Theory, simulations and example cases. PLoS One 2019; 14:e0222720. [PMID: 31545838 PMCID: PMC6756533 DOI: 10.1371/journal.pone.0222720] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Accepted: 09/05/2019] [Indexed: 11/19/2022] Open
Abstract
Quantitative MRI methods have recently gained extensive interest and are seeing substantial developments; however, their application in single patient vs control group comparisons is often limited by inherent statistical difficulties. One such application is detecting malformations of cortical development (MCDs) behind drug resistant epilepsies, a task that, especially when based solely on conventional MR images, may represent a serious challenge. We aimed to develop a novel straightforward voxel-wise evaluation method based on the Mahalanobis-distance, combining quantitative MRI data into a multidimensional parameter space and detecting lesion voxels as outliers. Simulations with standard multivariate Gaussian distribution and resampled DTI-eigenvalue data of 45 healthy control subjects determined the optimal critical value, cluster size threshold, and the expectable lesion detection performance through ROC-analyses. To reduce the effect of false positives emanating from registration artefacts and gyrification differences, an automatic classification method was applied, fine-tuned using a leave-one-out strategy based on diffusion and T1-weighted data of the controls. DWI processing, including thorough corrections and robust tensor fitting was performed with ExploreDTI, spatial coregistration was achieved with the DARTEL tools of SPM12. Additional to simulations, clusters of outlying diffusion profile, concordant with neuroradiological evaluation and independent calculations with the MAP07 toolbox were identified in 12 cases of a 13 patient example population with various types of MCDs. The multidimensional approach proved sufficiently sensitive in pinpointing regions of abnormal tissue microstructure using DTI data both in simulations and in the heterogeneous example population. Inherent limitations posed by registration artefacts, age-related differences, and the different or mixed pathologies limit the generalization of specificity estimation. Nevertheless, the proposed statistical method may aid the everyday examination of individual subjects, ever so more upon extending the framework with quantitative information from other modalities, e.g. susceptibility mapping, relaxometry, or perfusion.
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Affiliation(s)
- Gyula Gyebnár
- Magnetic Resonance Research Centre, Semmelweis University, Budapest, Hungary
- * E-mail:
| | - Zoltán Klimaj
- Magnetic Resonance Research Centre, Semmelweis University, Budapest, Hungary
| | - László Entz
- National Institute of Clinical Neurosciences, Budapest, Hungary
| | - Dániel Fabó
- National Institute of Clinical Neurosciences, Budapest, Hungary
| | - Gábor Rudas
- Magnetic Resonance Research Centre, Semmelweis University, Budapest, Hungary
| | - Péter Barsi
- Magnetic Resonance Research Centre, Semmelweis University, Budapest, Hungary
| | - Lajos R. Kozák
- Magnetic Resonance Research Centre, Semmelweis University, Budapest, Hungary
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23
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Automatic detection and localization of Focal Cortical Dysplasia lesions in MRI using fully convolutional neural network. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2019.04.024] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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24
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Fitsiori A, Hiremath SB, Boto J, Garibotto V, Vargas MI. Morphological and Advanced Imaging of Epilepsy: Beyond the Basics. CHILDREN (BASEL, SWITZERLAND) 2019; 6:E43. [PMID: 30862078 PMCID: PMC6462967 DOI: 10.3390/children6030043] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2018] [Revised: 02/27/2019] [Accepted: 03/05/2019] [Indexed: 12/26/2022]
Abstract
The etiology of epilepsy is variable and sometimes multifactorial. Clinical course and response to treatment largely depend on the precise etiology of the seizures. Along with the electroencephalogram (EEG), neuroimaging techniques, in particular, magnetic resonance imaging (MRI), are the most important tools for determining the possible etiology of epilepsy. Over the last few years, there have been many developments in data acquisition and analysis for both morphological and functional neuroimaging of people suffering from this condition. These innovations have increased the detection of underlying structural pathologies, which have till recently been classified as "cryptogenic" epilepsy. Cryptogenic epilepsy is often refractory to anti-epileptic drug treatment. In drug-resistant patients with structural or consistent functional lesions related to the epilepsy syndrome, surgery is the only treatment that can offer a seizure-free outcome. The pre-operative detection of the underlying structural condition increases the odds of successful surgical treatment of pharmacoresistant epilepsy. This article provides a comprehensive overview of neuroimaging techniques in epilepsy, highlighting recent advances and innovations and summarizes frequent etiologies of epilepsy in order to improve the diagnosis and management of patients suffering from seizures, especially young patients and children.
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Affiliation(s)
- Aikaterini Fitsiori
- Unit of Neurodiagnostic, Division of Neuroradiology, Geneva University Hospital, rue Gabrielle-Perret-Gentil 4, 1205 Geneva, Switzerland.
| | | | - José Boto
- Unit of Neurodiagnostic, Division of Neuroradiology, Geneva University Hospital, rue Gabrielle-Perret-Gentil 4, 1205 Geneva, Switzerland.
| | - Valentina Garibotto
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital and Faculty of Medicine, Geneva University, 1205 Geneva, Switzerland.
| | - Maria Isabel Vargas
- Unit of Neurodiagnostic, Division of Neuroradiology, Geneva University Hospital, rue Gabrielle-Perret-Gentil 4, 1205 Geneva, Switzerland.
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Semi-supervised VAE-GAN for Out-of-Sample Detection Applied to MRI Quality Control. LECTURE NOTES IN COMPUTER SCIENCE 2019. [DOI: 10.1007/978-3-030-32248-9_15] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Sakai K, Yamada K. Machine learning studies on major brain diseases: 5-year trends of 2014–2018. Jpn J Radiol 2018; 37:34-72. [DOI: 10.1007/s11604-018-0794-4] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2018] [Accepted: 11/14/2018] [Indexed: 12/17/2022]
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Senders JT, Zaki MM, Karhade AV, Chang B, Gormley WB, Broekman ML, Smith TR, Arnaout O. An introduction and overview of machine learning in neurosurgical care. Acta Neurochir (Wien) 2018; 160:29-38. [PMID: 29134342 DOI: 10.1007/s00701-017-3385-8] [Citation(s) in RCA: 87] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2017] [Accepted: 10/29/2017] [Indexed: 12/20/2022]
Abstract
BACKGROUND Machine learning (ML) is a branch of artificial intelligence that allows computers to learn from large complex datasets without being explicitly programmed. Although ML is already widely manifest in our daily lives in various forms, the considerable potential of ML has yet to find its way into mainstream medical research and day-to-day clinical care. The complex diagnostic and therapeutic modalities used in neurosurgery provide a vast amount of data that is ideally suited for ML models. This systematic review explores ML's potential to assist and improve neurosurgical care. METHOD A systematic literature search was performed in the PubMed and Embase databases to identify all potentially relevant studies up to January 1, 2017. All studies were included that evaluated ML models assisting neurosurgical treatment. RESULTS Of the 6,402 citations identified, 221 studies were selected after subsequent title/abstract and full-text screening. In these studies, ML was used to assist surgical treatment of patients with epilepsy, brain tumors, spinal lesions, neurovascular pathology, Parkinson's disease, traumatic brain injury, and hydrocephalus. Across multiple paradigms, ML was found to be a valuable tool for presurgical planning, intraoperative guidance, neurophysiological monitoring, and neurosurgical outcome prediction. CONCLUSIONS ML has started to find applications aimed at improving neurosurgical care by increasing the efficiency and precision of perioperative decision-making. A thorough validation of specific ML models is essential before implementation in clinical neurosurgical care. To bridge the gap between research and clinical care, practical and ethical issues should be considered parallel to the development of these techniques.
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Bowles C, Qin C, Guerrero R, Gunn R, Hammers A, Dickie DA, Valdés Hernández M, Wardlaw J, Rueckert D. Brain lesion segmentation through image synthesis and outlier detection. Neuroimage Clin 2017; 16:643-658. [PMID: 29868438 PMCID: PMC5984574 DOI: 10.1016/j.nicl.2017.09.003] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Revised: 08/30/2017] [Accepted: 09/04/2017] [Indexed: 11/02/2022]
Abstract
Cerebral small vessel disease (SVD) can manifest in a number of ways. Many of these result in hyperintense regions visible on T2-weighted magnetic resonance (MR) images. The automatic segmentation of these lesions has been the focus of many studies. However, previous methods tended to be limited to certain types of pathology, as a consequence of either restricting the search to the white matter, or by training on an individual pathology. Here we present an unsupervised abnormality detection method which is able to detect abnormally hyperintense regions on FLAIR regardless of the underlying pathology or location. The method uses a combination of image synthesis, Gaussian mixture models and one class support vector machines, and needs only be trained on healthy tissue. We evaluate our method by comparing segmentation results from 127 subjects with SVD with three established methods and report significantly superior performance across a number of metrics.
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Affiliation(s)
| | - Chen Qin
- Department of Computing, Imperial College London, UK
| | | | - Roger Gunn
- Imanova Ltd., London, UK
- Department of Medicine, Imperial College London, UK
| | - Alexander Hammers
- Department of Computing, Imperial College London, UK
- King's College London & Guy's and St Thomas' PET Centre, Division of Imaging Sciences and Biomedical Engineering, St Thomas' Hospital, King's College London, UK
| | | | | | - Joanna Wardlaw
- Department of Neuroimaging Sciences, University of Edinburgh, UK
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