Automated Detection of Juvenile Myoclonic Epilepsy using CNN based Transfer Learning in Diffusion MRI

Si, Xiaopeng; Zhang, Xingjian; Yu, Zhou; Sun, Yulin; Jin, Wei; Yin, Shaoya; Zhao, Xin; Li, Qiang; Ming, Dong

doi:10.1109/embc44109.2020.9175467

Cited by 15 publications

(22 citation statements)

References 15 publications

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“…The most common approach to apply a prior-sharing strategy-and, in general, transfer learning-was fine-tuning all the parameters of a pretrained CNN [29,[31][32][33]35,39,71, (80% of all prior-sharing methods). Other approaches utilized Bayesian graphical models [37,38,120,121], graph neural networks [122], kernel methods [64,123], multilayer perceptrons [124], and Pearson-correlation methods [125].…”

Section: Parameter-based Approachesmentioning

confidence: 99%

Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review

et al. 2021

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(1) Background: Transfer learning refers to machine learning techniques that focus on acquiring knowledge from related tasks to improve generalization in the tasks of interest. In magnetic resonance imaging (MRI), transfer learning is important for developing strategies that address the variation in MR images from different imaging protocols or scanners. Additionally, transfer learning is beneficial for reutilizing machine learning models that were trained to solve different (but related) tasks to the task of interest. The aim of this review is to identify research directions, gaps in knowledge, applications, and widely used strategies among the transfer learning approaches applied in MR brain imaging; (2) Methods: We performed a systematic literature search for articles that applied transfer learning to MR brain imaging tasks. We screened 433 studies for their relevance, and we categorized and extracted relevant information, including task type, application, availability of labels, and machine learning methods. Furthermore, we closely examined brain MRI-specific transfer learning approaches and other methods that tackled issues relevant to medical imaging, including privacy, unseen target domains, and unlabeled data; (3) Results: We found 129 articles that applied transfer learning to MR brain imaging tasks. The most frequent applications were dementia-related classification tasks and brain tumor segmentation. The majority of articles utilized transfer learning techniques based on convolutional neural networks (CNNs). Only a few approaches utilized clearly brain MRI-specific methodology, and considered privacy issues, unseen target domains, or unlabeled data. We proposed a new categorization to group specific, widely-used approaches such as pretraining and fine-tuning CNNs; (4) Discussion: There is increasing interest in transfer learning for brain MRI. Well-known public datasets have clearly contributed to the popularity of Alzheimer’s diagnostics/prognostics and tumor segmentation as applications. Likewise, the availability of pretrained CNNs has promoted their utilization. Finally, the majority of the surveyed studies did not examine in detail the interpretation of their strategies after applying transfer learning, and did not compare their approach with other transfer learning approaches.

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Section: Parameter-based Approachesmentioning

confidence: 99%

Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review

et al. 2021

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“…While most studies have used a region-ofinterest (ROI) to extract imaging features, the choice of atlases for ROIs varies. For example, some investigations used traditional automated anatomical labeling (AAL) (Fallahi et al, 2020;Si et al, 2020;Kini et al, 2021), and a different atlas was used in other studies (Gleichgerrcht et al, 2018(Gleichgerrcht et al, , 2020. Zhang et al ( , 2021 used radiomics as a novel method to extract imaging data, and this might provide greater usefulness than conventional methods (Gillies et al, 2016).…”

Section: Methodological Aspects and Future Directionsmentioning

confidence: 99%

“…Hosseini et al (2020) used a CNN deep learning structure for the localization and prediction of epileptogenicity based on EEG and rs-fMRI data. In an investigation by Si et al (2020), a CNN-wise transfer learning technique combined with high angular resolved diffusion imaging (HARDI) and NODDI data were used for the detection of juvenile myoclonic epilepsy. A CNN model based on rs-fMRI data was trained for the classification of pediatric refractory epilepsy (Nguyen et al, 2021).…”

Section: Classification Modelsmentioning

confidence: 99%

“…The leave-one-out strategy is a subtype of the k-fold cross-validation strategy in which the number of folds is equal to the number of samples; it is usually used for a small dataset. The k-fold cross-validation strategy has been widely used in epilepsy studies (Bharath et al, 2019;Beheshti et al, 2020a;Zhou et al, 2020;Sone et al, 2021).…”

Section: Validation Strategiesmentioning

confidence: 99%

“…As summarized in Table 1, various ML classifiers have achieved over 70-80% accuracy to successfully discriminate between individuals with epilepsy and healthy controls, using T1 images (Vasta et al, 2018;Chen et al, 2020;Park et al, 2020), diffusion MRI (Cantor-Rivera et al, 2015Del Gaizo et al, 2017;Park and Ohn, 2019;Huang et al, 2020;Si et al, 2020), and functional MRI (Pedersen et al, 2015;Torlay et al, 2017;Wang et al, 2018a;Bharath et al, 2019;Hwang et al, 2019a,b;Zhou et al, 2020;Nguyen et al, 2021). Studies targeting TLE achieved ∼90% accuracy (Cantor-Rivera et al, 2015;Bharath et al, 2019;Chen et al, 2020;Huang et al, 2020), but it has been more challenging to identify idiopathic generalized epilepsy (IGE), and only ∼75% accuracy has been obtained for this task (Wang et al, 2018a;Si et al, 2020). Though these impressive investigations yielded evidence of the potential of machine learning in epilepsy, the clinical usefulness of the findings might be limited, since a differentiation between individuals with epilepsy and healthy subjects is not a major role of neuroimaging.…”

Section: The Differentiation Of Individuals With Epilepsy From Healthy Controlsmentioning

confidence: 99%

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Clinical Application of Machine Learning Models for Brain Imaging in Epilepsy: A Review

Sone

Beheshti

2021

Front. Neurosci.

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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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MRI‐Based Radiomics Approach for Differentiating Juvenile Myoclonic Epilepsy from Epilepsy with Generalized Tonic–Clonic Seizures Alone

Sim,

Lee,

Chu

et al. 2023

Magnetic Resonance Imaging

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BackgroundThe clinical presentation of juvenile myoclonic epilepsy (JME) and epilepsy with generalized tonic–clonic seizures alone (GTCA) is similar, and MRI scans are often perceptually normal in both conditions making them challenging to differentiate.PurposeTo develop and validate an MRI‐based radiomics model to accurately diagnose JME and GTCA, as well as to classify prognostic groups.Study TypeRetrospective.Population164 patients (127 with JME and 37 with GTCA) patients (age 24.0 ± 9.6; 50% male), divided into training (n = 114) and test (n = 50) sets in a 7:3 ratio with the same proportion of JME and GTCA patients kept in both sets.Field Strength/Sequence3T; 3D T1‐weighted spoiled gradient‐echo.AssessmentA total of 17 region‐of‐interest in the brain were identified as having clinical evidence of association with JME and GTCA, from where 1581 radiomics features were extracted for each subject. Forty‐eight machine‐learning combinations of oversampling, feature selection, and classification algorithms were explored to develop an optimal radiomics model. The performance of the best radiomics models for diagnosis and for classification of the favorable outcome group were evaluated in the test set.Statistical TestsModel performance measured using area under the curve (AUC) of receiver operating characteristic (ROC) curve. Shapley additive explanations (SHAP) analysis to estimate the contribution of each radiomics feature.ResultsThe AUC (95% confidence interval) of the best radiomics models for diagnosis and for classification of favorable outcome group were 0.767 (0.591–0.943) and 0.717 (0.563–0.871), respectively. SHAP analysis revealed that the first‐order and textural features of the caudate, cerebral white matter, thalamus proper, and putamen had the highest importance in the best radiomics model.ConclusionThe proposed MRI‐based radiomics model demonstrated the potential to diagnose JME and GTCA, as well as to classify prognostic groups. MRI regions associated with JME, such as the basal ganglia, thalamus, and cerebral white matter, appeared to be important for constructing radiomics models.Level of Evidence3Technical EfficacyStage 3

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Automated Detection of Juvenile Myoclonic Epilepsy using CNN based Transfer Learning in Diffusion MRI

Cited by 15 publications

References 15 publications

Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review

Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review

Clinical Application of Machine Learning Models for Brain Imaging in Epilepsy: A Review

MRI‐Based Radiomics Approach for Differentiating Juvenile Myoclonic Epilepsy from Epilepsy with Generalized Tonic–Clonic Seizures Alone

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