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Weng L, Zhu Z, Dai K, Zheng Z, Zhu J, Wu H. Reduced-Reference Learning for Target Localization in Deep Brain Stimulation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:2434-2447. [PMID: 38324428 DOI: 10.1109/tmi.2024.3363425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
This work proposes a supervised machine learning method for target localization in deep brain stimulation (DBS). DBS is a recognized treatment for essential tremor. The effects of DBS significantly depend on the precise implantation of electrodes. Recent research on diffusion tensor imaging shows that the optimal target for essential tremor is related to the dentato-rubro-thalamic tract (DRTT), thus DRTT targeting has become a promising direction. The tractography-based targeting is more accurate than conventional ones, but still too complicated for clinical scenarios, where only structural magnetic resonance imaging (sMRI) data is available. In order to improve efficiency and utility, we consider target localization as a non-linear regression problem in a reduced-reference learning framework, and solve it with convolutional neural networks (CNNs). The proposed method is an efficient two-step framework, and consists of two image-based networks: one for classification and the other for localization. We model the basic workflow as an image retrieval process and define relevant performance metrics. Using DRTT as pseudo groundtruths, we show that individualized tractography-based optimal targets can be inferred from sMRI data with high accuracy. For two datasets of 280×220/272×227 (0.7/0.8 mm slice thickness) sMRI input, our model achieves an average posterior localization error of 2.3/1.2 mm, and a median of 1.7/1.02 mm. The proposed framework is a novel application of reduced-reference learning, and a first attempt to localize DRTT from sMRI. It significantly outperforms existing methods using 3D-CNN, anatomical and DRTT atlas, and may serve as a new baseline for general target localization problems.
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2
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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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3
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Maleki Varnosfaderani S, Forouzanfar M. The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century. Bioengineering (Basel) 2024; 11:337. [PMID: 38671759 PMCID: PMC11047988 DOI: 10.3390/bioengineering11040337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 03/25/2024] [Accepted: 03/26/2024] [Indexed: 04/28/2024] Open
Abstract
As healthcare systems around the world face challenges such as escalating costs, limited access, and growing demand for personalized care, artificial intelligence (AI) is emerging as a key force for transformation. This review is motivated by the urgent need to harness AI's potential to mitigate these issues and aims to critically assess AI's integration in different healthcare domains. We explore how AI empowers clinical decision-making, optimizes hospital operation and management, refines medical image analysis, and revolutionizes patient care and monitoring through AI-powered wearables. Through several case studies, we review how AI has transformed specific healthcare domains and discuss the remaining challenges and possible solutions. Additionally, we will discuss methodologies for assessing AI healthcare solutions, ethical challenges of AI deployment, and the importance of data privacy and bias mitigation for responsible technology use. By presenting a critical assessment of AI's transformative potential, this review equips researchers with a deeper understanding of AI's current and future impact on healthcare. It encourages an interdisciplinary dialogue between researchers, clinicians, and technologists to navigate the complexities of AI implementation, fostering the development of AI-driven solutions that prioritize ethical standards, equity, and a patient-centered approach.
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Affiliation(s)
| | - Mohamad Forouzanfar
- Département de Génie des Systèmes, École de Technologie Supérieure (ÉTS), Université du Québec, Montréal, QC H3C 1K3, Canada
- Centre de Recherche de L’institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, QC H3W 1W5, Canada
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4
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Caznok Silveira AC, Antunes ASLM, Athié MCP, da Silva BF, Ribeiro dos Santos JV, Canateli C, Fontoura MA, Pinto A, Pimentel-Silva LR, Avansini SH, de Carvalho M. Between neurons and networks: investigating mesoscale brain connectivity in neurological and psychiatric disorders. Front Neurosci 2024; 18:1340345. [PMID: 38445254 PMCID: PMC10912403 DOI: 10.3389/fnins.2024.1340345] [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: 11/17/2023] [Accepted: 01/29/2024] [Indexed: 03/07/2024] Open
Abstract
The study of brain connectivity has been a cornerstone in understanding the complexities of neurological and psychiatric disorders. It has provided invaluable insights into the functional architecture of the brain and how it is perturbed in disorders. However, a persistent challenge has been achieving the proper spatial resolution, and developing computational algorithms to address biological questions at the multi-cellular level, a scale often referred to as the mesoscale. Historically, neuroimaging studies of brain connectivity have predominantly focused on the macroscale, providing insights into inter-regional brain connections but often falling short of resolving the intricacies of neural circuitry at the cellular or mesoscale level. This limitation has hindered our ability to fully comprehend the underlying mechanisms of neurological and psychiatric disorders and to develop targeted interventions. In light of this issue, our review manuscript seeks to bridge this critical gap by delving into the domain of mesoscale neuroimaging. We aim to provide a comprehensive overview of conditions affected by aberrant neural connections, image acquisition techniques, feature extraction, and data analysis methods that are specifically tailored to the mesoscale. We further delineate the potential of brain connectivity research to elucidate complex biological questions, with a particular focus on schizophrenia and epilepsy. This review encompasses topics such as dendritic spine quantification, single neuron morphology, and brain region connectivity. We aim to showcase the applicability and significance of mesoscale neuroimaging techniques in the field of neuroscience, highlighting their potential for gaining insights into the complexities of neurological and psychiatric disorders.
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Affiliation(s)
- Ana Clara Caznok Silveira
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
- School of Electrical and Computer Engineering, University of Campinas, Campinas, Brazil
| | | | - Maria Carolina Pedro Athié
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | - Bárbara Filomena da Silva
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | | | - Camila Canateli
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | - Marina Alves Fontoura
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | - Allan Pinto
- Brazilian Synchrotron Light Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | | | - Simoni Helena Avansini
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | - Murilo de Carvalho
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
- Brazilian Synchrotron Light Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
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5
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Astolfi P, Verhagen R, Petit L, Olivetti E, Sarubbo S, Masci J, Boscaini D, Avesani P. Supervised tractogram filtering using Geometric Deep Learning. Med Image Anal 2023; 90:102893. [PMID: 37741032 DOI: 10.1016/j.media.2023.102893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 04/18/2023] [Accepted: 07/07/2023] [Indexed: 09/25/2023]
Abstract
A tractogram is a virtual representation of the brain white matter. It is composed of millions of virtual fibers, encoded as 3D polylines, which approximate the white matter axonal pathways. To date, tractograms are the most accurate white matter representation and thus are used for tasks like presurgical planning and investigations of neuroplasticity, brain disorders, or brain networks. However, it is a well-known issue that a large portion of tractogram fibers is not anatomically plausible and can be considered artifacts of the tracking procedure. With Verifyber, we tackle the problem of filtering out such non-plausible fibers using a novel fully-supervised learning approach. Differently from other approaches based on signal reconstruction and/or brain topology regularization, we guide our method with the existing anatomical knowledge of the white matter. Using tractograms annotated according to anatomical principles, we train our model, Verifyber, to classify fibers as either anatomically plausible or non-plausible. The proposed Verifyber model is an original Geometric Deep Learning method that can deal with variable size fibers, while being invariant to fiber orientation. Our model considers each fiber as a graph of points, and by learning features of the edges between consecutive points via the proposed sequence Edge Convolution, it can capture the underlying anatomical properties. The output filtering results highly accurate and robust across an extensive set of experiments, and fast; with a 12GB GPU, filtering a tractogram of 1M fibers requires less than a minute.
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Affiliation(s)
- Pietro Astolfi
- NILab, TeV, Fondazione Bruno Kessler, Trento, Italy; PAVIS, Istituto Italiano di Tecnologia, Geonva, Italy; Center for Mind/Brain Sciences (CiMeC), University of Trento, Rovereto, Italy
| | | | - Laurent Petit
- GIN, IMN, CNRS, CEA, Université de Bordeaux, Bordeaux, France
| | - Emanuele Olivetti
- NILab, TeV, Fondazione Bruno Kessler, Trento, Italy; Center for Mind/Brain Sciences (CiMeC), University of Trento, Rovereto, Italy
| | - Silvio Sarubbo
- Center for Mind/Brain Sciences (CiMeC), University of Trento, Rovereto, Italy; Department of Neurosurgery, Azienda Provinciale per i Servizi Sanitari, "Santa Chiara" Hospital, Trento, Italy
| | | | | | - Paolo Avesani
- NILab, TeV, Fondazione Bruno Kessler, Trento, Italy; Center for Mind/Brain Sciences (CiMeC), University of Trento, Rovereto, Italy.
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6
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Meesters S, Landers M, Rutten GJ, Florack L. Subject-Specific Automatic Reconstruction of White Matter Tracts. J Digit Imaging 2023; 36:2648-2661. [PMID: 37537513 PMCID: PMC10584769 DOI: 10.1007/s10278-023-00883-0] [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] [Received: 03/01/2023] [Revised: 07/05/2023] [Accepted: 07/05/2023] [Indexed: 08/05/2023] Open
Abstract
MRI-based tractography is still underexploited and unsuited for routine use in brain tumor surgery due to heterogeneity of methods and functional-anatomical definitions and above all, the lack of a turn-key system. Standardization of methods is therefore desirable, whereby an objective and reliable approach is a prerequisite before the results of any automated procedure can subsequently be validated and used in neurosurgical practice. In this work, we evaluated these preliminary but necessary steps in healthy volunteers. Specifically, we evaluated the robustness and reliability (i.e., test-retest reproducibility) of tractography results of six clinically relevant white matter tracts by using healthy volunteer data (N = 136) from the Human Connectome Project consortium. A deep learning convolutional network-based approach was used for individualized segmentation of regions of interest, combined with an evidence-based tractography protocol and appropriate post-tractography filtering. Robustness was evaluated by estimating the consistency of tractography probability maps, i.e., averaged tractograms in normalized space, through the use of a hold-out cross-validation approach. No major outliers were found, indicating a high robustness of the tractography results. Reliability was evaluated at the individual level. First by examining the overlap of tractograms that resulted from repeatedly processed identical MRI scans (N = 10, 10 iterations) to establish an upper limit of reliability of the pipeline. Second, by examining the overlap for subjects that were scanned twice at different time points (N = 40). Both analyses indicated high reliability, with the second analysis showing a reliability near the upper limit. The robust and reliable subject-specific generation of white matter tracts in healthy subjects holds promise for future validation of our pipeline in a clinical population and subsequent implementation in brain tumor surgery.
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Affiliation(s)
- Stephan Meesters
- Department of Mathematics & Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands
- Department of Neurosurgery, Elisabeth-Tweesteden Hospital, Tilburg, The Netherlands
| | - Maud Landers
- Department of Neurosurgery, Elisabeth-Tweesteden Hospital, Tilburg, The Netherlands
| | - Geert-Jan Rutten
- Department of Neurosurgery, Elisabeth-Tweesteden Hospital, Tilburg, The Netherlands.
| | - Luc Florack
- Department of Mathematics & Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands
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7
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Qu R, Ji X, Wang S, Wang Z, Wang L, Yang X, Yin S, Gu J, Wang A, Xu G. An Integrated Multi-Channel Deep Neural Network for Mesial Temporal Lobe Epilepsy Identification Using Multi-Modal Medical Data. Bioengineering (Basel) 2023; 10:1234. [PMID: 37892964 PMCID: PMC10604199 DOI: 10.3390/bioengineering10101234] [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: 08/31/2023] [Revised: 09/22/2023] [Accepted: 10/13/2023] [Indexed: 10/29/2023] Open
Abstract
Epilepsy is a chronic brain disease with recurrent seizures. Mesial temporal lobe epilepsy (MTLE) is the most common pathological cause of epilepsy. With the development of computer-aided diagnosis technology, there are many auxiliary diagnostic approaches based on deep learning algorithms. However, the causes of epilepsy are complex, and distinguishing different types of epilepsy accurately is challenging with a single mode of examination. In this study, our aim is to assess the combination of multi-modal epilepsy medical information from structural MRI, PET image, typical clinical symptoms and personal demographic and cognitive data (PDC) by adopting a multi-channel 3D deep convolutional neural network and pre-training PET images. The results show better diagnosis accuracy than using one single type of medical data alone. These findings reveal the potential of a deep neural network in multi-modal medical data fusion.
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Affiliation(s)
- Ruowei Qu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, China; (R.Q.); (X.J.); (Z.W.); (X.Y.)
| | - Xuan Ji
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, China; (R.Q.); (X.J.); (Z.W.); (X.Y.)
| | - Shifeng Wang
- Tianjin Universal Medical Imaging Diagnostic Center, Tianjin 300110, China;
| | - Zhaonan Wang
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, China; (R.Q.); (X.J.); (Z.W.); (X.Y.)
| | - Le Wang
- Department of Functional Neurosurgery, Huanhu Hospital, Tianjin 300350, China; (L.W.); (S.Y.)
| | - Xinsheng Yang
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, China; (R.Q.); (X.J.); (Z.W.); (X.Y.)
| | - Shaoya Yin
- Department of Functional Neurosurgery, Huanhu Hospital, Tianjin 300350, China; (L.W.); (S.Y.)
| | - Junhua Gu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, China; (R.Q.); (X.J.); (Z.W.); (X.Y.)
| | - Alan Wang
- Auckland Bioengineering Institute, The University of Auckland, Grafton, Auckland 1010, New Zealand;
| | - Guizhi Xu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, China; (R.Q.); (X.J.); (Z.W.); (X.Y.)
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8
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García-Ramó KB, Sanchez-Catasus CA, Winston GP. Deep learning in neuroimaging of epilepsy. Clin Neurol Neurosurg 2023; 232:107879. [PMID: 37473486 DOI: 10.1016/j.clineuro.2023.107879] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 05/24/2023] [Accepted: 07/04/2023] [Indexed: 07/22/2023]
Abstract
In recent years, artificial intelligence, particularly deep learning (DL), has demonstrated utility in diverse areas of medicine. DL uses neural networks to automatically learn features from the raw data while this is not possible with conventional machine learning. It is helpful for the assessment of patients with epilepsy and whilst most published studies have been aimed at the automatic detection and prediction of seizures from electroencephalographic records, there is a growing number of investigations that use neuroimaging modalities (structural and functional magnetic resonance imaging, diffusion-weighted imaging and positron emission tomography) as input data. We review the application of DL to neuroimaging (sMRI, fMRI, DWI and PET) of focal epilepsy, specifically presurgical evaluation of drug-refractory epilepsy. First, a brief theoretical overview of artificial neural networks and deep learning is presented. Next, we review applications of deep learning to neuroimaging of epilepsy: diagnosis and lateralization, automated detection of lesion, presurgical evaluation and prediction of postsurgical outcome. Finally, the limitations, challenges and possible future directions in the application of these methods in the study of epilepsies are discussed. This approach could become an essential tool in clinical practice, particularly in the evaluation of images considered negative by visual inspection, in individualized treatments, and in the approach to epilepsy as a network disorder. However, greater multicenter collaboration is required to achieve the collection of sufficient data with the required quality together with the open access availability of the developed codes and tools.
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Affiliation(s)
- Karla Batista García-Ramó
- Group of Neuroimaging Processing, International Center for Neurological Restoration, Cuba; Department of Clinical Investigations, Center of Isotopes, Cuba.
| | - Carlos A Sanchez-Catasus
- Department of Neurology, Clínica Universidad de Navarra, Spain; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, the Netherlands.
| | - Gavin P Winston
- Division of Neurology, Department of Medicine, Queen's University, Canada; Centre for Neuroscience Studies, Queen's University, Canada.
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9
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Jha RR, Pathak SK, Nath V, Schneider W, Kumar BVR, Bhavsar A, Nigam A. VRfRNet: Volumetric ROI fODF reconstruction network for estimation of multi-tissue constrained spherical deconvolution with only single shell dMRI. Magn Reson Imaging 2022; 90:1-16. [PMID: 35341904 DOI: 10.1016/j.mri.2022.03.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 02/19/2022] [Accepted: 03/19/2022] [Indexed: 10/18/2022]
Abstract
Diffusion MRI (dMRI) is one of the most popular techniques for studying the brain structure, mainly the white matter region. Among several sampling methods in dMRI, the high angular resolution diffusion imaging (HARDI) technique has attracted researchers due to its more accurate fiber orientation estimation. However, the current single-shell HARDI makes the intravoxel structure challenging to estimate accurately. While multi-shell acquisition can address this problem, it takes a longer scanning time, restricting its use in clinical applications. In addition, most existing dMRI scanners with low gradient-strengths often acquire single-shell up to b=1000s/mm2 because of signal-to-noise ratio issues and severe image artefacts. Hence, we propose a novel generative adversarial network, VRfRNet, for the reconstruction of multi-shell multi-tissue fiber orientation distribution function from single-shell HARDI volumes. Such a transformation learning is performed in the spherical harmonics (SH) space, as raw input HARDI volume is transformed to SH coefficients to soften gradient directions. The proposed VRfRNet consists of several modules, such as multi-context feature enrichment module, feature level attention, and softmax level attention. In addition, three loss functions have been used to optimize network learning, including L1, adversarial, and total variation. The network is trained and tested using standard qualitative and quantitative performance metrics on the publicly available HCP data-set.
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Affiliation(s)
- Ranjeet Ranjan Jha
- MANAS Lab, School of Computing and Electrical Engineering (SCEE), Indian Institute of Technology (IIT) Mandi, India.
| | - Sudhir K Pathak
- Learning Research and Development Center, University of Pittsburgh, USA
| | - Vishwesh Nath
- Vanderbilt Institute for Surgery and Engineering, Nashville, Tennessee, USA
| | - Walter Schneider
- Learning Research and Development Center, University of Pittsburgh, USA
| | - B V Rathish Kumar
- Department of Mathematics and Statistics, Indian Institute of Technology Kanpur, India
| | - Arnav Bhavsar
- MANAS Lab, School of Computing and Electrical Engineering (SCEE), Indian Institute of Technology (IIT) Mandi, India
| | - Aditya Nigam
- MANAS Lab, School of Computing and Electrical Engineering (SCEE), Indian Institute of Technology (IIT) Mandi, India
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10
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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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11
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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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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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13
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Sargolzaei S. Can Deep Learning Hit a Moving Target? A Scoping Review of Its Role to Study Neurological Disorders in Children. Front Comput Neurosci 2021; 15:670489. [PMID: 34025380 PMCID: PMC8131543 DOI: 10.3389/fncom.2021.670489] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Accepted: 04/09/2021] [Indexed: 12/12/2022] Open
Abstract
Neurological disorders dramatically impact patients of any age population, their families, and societies. Pediatrics are among vulnerable age populations who differently experience the devastating consequences of neurological conditions, such as attention-deficit hyperactivity disorders (ADHD), autism spectrum disorders (ASD), cerebral palsy, concussion, and epilepsy. System-level understanding of these neurological disorders, particularly from the brain networks' dynamic perspective, has led to the significant trend of recent scientific investigations. While a dramatic maturation in the network science application domain is evident, leading to a better understanding of neurological disorders, such rapid utilization for studying pediatric neurological disorders falls behind that of the adult population. Aside from the specific technological needs and constraints in studying neurological disorders in children, the concept of development introduces uncertainty and further complexity topping the existing neurologically driven processes caused by disorders. To unravel these complexities, indebted to the availability of high-dimensional data and computing capabilities, approaches based on machine learning have rapidly emerged a new trend to understand pathways better, accurately diagnose, and better manage the disorders. Deep learning has recently gained an ever-increasing role in the era of health and medical investigations. Thanks to its relatively more minor dependency on feature exploration and engineering, deep learning may overcome the challenges mentioned earlier in studying neurological disorders in children. The current scoping review aims to explore challenges concerning pediatric brain development studies under the constraints of neurological disorders and offer an insight into the potential role of deep learning methodology on such a task with varying and uncertain nature. Along with pinpointing recent advancements, possible research directions are highlighted where deep learning approaches can assist in computationally targeting neurological disorder-related processes and translating them into windows of opportunities for interventions in diagnosis, treatment, and management of neurological disorders in children.
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Affiliation(s)
- Saman Sargolzaei
- Department of Engineering, College of Engineering and Natural Sciences, University of Tennessee at Martin, Martin, TN, United States
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