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The Road to Personalized Medicine in Alzheimer’s Disease: The Use of Artificial Intelligence. Biomedicines 2022; 10:biomedicines10020315. [PMID: 35203524 PMCID: PMC8869403 DOI: 10.3390/biomedicines10020315] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 01/21/2022] [Accepted: 01/24/2022] [Indexed: 02/05/2023] Open
Abstract
Dementia remains an extremely prevalent syndrome among older people and represents a major cause of disability and dependency. Alzheimer’s disease (AD) accounts for the majority of dementia cases and stands as the most common neurodegenerative disease. Since age is the major risk factor for AD, the increase in lifespan not only represents a rise in the prevalence but also adds complexity to the diagnosis. Moreover, the lack of disease-modifying therapies highlights another constraint. A shift from a curative to a preventive approach is imminent and we are moving towards the application of personalized medicine where we can shape the best clinical intervention for an individual patient at a given point. This new step in medicine requires the most recent tools and analysis of enormous amounts of data where the application of artificial intelligence (AI) plays a critical role on the depiction of disease–patient dynamics, crucial in reaching early/optimal diagnosis, monitoring and intervention. Predictive models and algorithms are the key elements in this innovative field. In this review, we present an overview of relevant topics regarding the application of AI in AD, detailing the algorithms and their applications in the fields of drug discovery, and biomarkers.
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E-Commerce Credit Risk Assessment Based on Fuzzy Neural Network. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:3088915. [PMID: 35035456 PMCID: PMC8759834 DOI: 10.1155/2022/3088915] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 12/14/2021] [Indexed: 12/28/2022]
Abstract
In this paper, we propose a cooperative strategy-based self-organization mechanism to reconstruct the network. The mechanism includes a comprehensive evaluation algorithm and structure adjustment mechanism. The self-organization mechanism can be carried out simultaneously with the parameter optimization process. By calculating the similarity and independent contribution of normative neurons, the effectiveness of fuzzy rules can be jointly evaluated, and effective structural changes can be realized. Moreover, this mechanism should not set the threshold in advance in practical application. In order to optimize the parameters of SC-IR2FNN, we developed a parameter optimization mechanism based on an interaction strategy. The parameter optimization mechanism based on a joint strategy, namely multilayer optimization engine, can split SC-IR2FNN parameters into nonlinear and linear parameters for joint optimization. The nonlinear parameters are optimized by an advanced two-level algorithm, and the linear parameters are updated with the minimum biological multiplication. Two parameter optimization algorithms optimize nonlinear and linear parameters, reduce the computational complexity of SC-IR2FNN, and improve the learning rate. Using the principal component factor analysis method, seven representative common factors are selected to replace the original variables, which include the profitability factor of the financing enterprise, the solvency factor of the financing enterprise, the profitability factor of the core enterprise, the operation guarantee factor, and the growth ability of the financing enterprise. Factors, supply chain online degree factors, financing enterprise quality, and cooperation factors, can well measure the credit risk of online supply chains. The logistic model shows that the profitability factor of the financing company, the debt repayment factor of the financing company, and the profitability of the core company are three factors that have a significant impact on the credit risk of online supply chain finance. Based on the improved credit calculation model, we developed an online clue risk calculation. This method is based on site conditions and can evaluate credit risk. From the test results, the improved credit scoring system is the result of facing speculative and circular credit fraud and implies that the traders of risk commentators are in a leading position in each electronic device. The results show that risk analysis is effective in any case.
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Mirabnahrazam G, Ma D, Lee S, Popuri K, Lee H, Cao J, Wang L, Galvin JE, Beg MF. Machine Learning Based Multimodal Neuroimaging Genomics Dementia Score for Predicting Future Conversion to Alzheimer's Disease. J Alzheimers Dis 2022; 87:1345-1365. [PMID: 35466939 PMCID: PMC9195128 DOI: 10.3233/jad-220021] [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: 11/15/2022]
Abstract
BACKGROUND The increasing availability of databases containing both magnetic resonance imaging (MRI) and genetic data allows researchers to utilize multimodal data to better understand the characteristics of dementia of Alzheimer's type (DAT). OBJECTIVE The goal of this study was to develop and analyze novel biomarkers that can help predict the development and progression of DAT. METHODS We used feature selection and ensemble learning classifier to develop an image/genotype-based DAT score that represents a subject's likelihood of developing DAT in the future. Three feature types were used: MRI only, genetic only, and combined multimodal data. We used a novel data stratification method to better represent different stages of DAT. Using a pre-defined 0.5 threshold on DAT scores, we predicted whether a subject would develop DAT in the future. RESULTS Our results on Alzheimer's Disease Neuroimaging Initiative (ADNI) database showed that dementia scores using genetic data could better predict future DAT progression for currently normal control subjects (Accuracy = 0.857) compared to MRI (Accuracy = 0.143), while MRI can better characterize subjects with stable mild cognitive impairment (Accuracy = 0.614) compared to genetics (Accuracy = 0.356). Combining MRI and genetic data showed improved classification performance in the remaining stratified groups. CONCLUSION MRI and genetic data can contribute to DAT prediction in different ways. MRI data reflects anatomical changes in the brain, while genetic data can detect the risk of DAT progression prior to the symptomatic onset. Combining information from multimodal data appropriately can improve prediction performance.
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Affiliation(s)
| | - Da Ma
- School of Engineering, Simon Fraser University, Burnaby, BC, Canada
- School of Medicine, Wake Forest University, Winston-Salem, NC, USA
| | - Sieun Lee
- School of Engineering, Simon Fraser University, Burnaby, BC, Canada
- Mental Health & Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Karteek Popuri
- School of Engineering, Simon Fraser University, Burnaby, BC, Canada
| | - Hyunwoo Lee
- Division of Neurology, Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Jiguo Cao
- Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, BC, Canada
| | - Lei Wang
- Psychiatry and Behavioral Health, Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - James E Galvin
- Comprehensive Center for Brain Health, Department of Neurology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Mirza Faisal Beg
- School of Engineering, Simon Fraser University, Burnaby, BC, Canada
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Bogdanovic B, Solari EL, Villagran Asiares A, McIntosh L, van Marwick S, Schachoff S, Nekolla SG. PET/MR Technology: Advancement and Challenges. Semin Nucl Med 2021; 52:340-355. [PMID: 34969520 DOI: 10.1053/j.semnuclmed.2021.11.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 11/25/2021] [Accepted: 11/29/2021] [Indexed: 01/07/2023]
Abstract
When this article was written, it coincided with the 11th anniversary of the installation of our PET/MR device in Munich. In fact, this was the first fully integrated device to be in clinical use. During this time, we have observed many interesting behaviors, to put it kindly. However, it is more critical that in this process, our understanding of the system also improved - including the advantages and limitations from a technical, logistical, and medical perspective. The last decade of PET/MRI research has certainly been characterized by most sites looking for a "key application." There were many ideas in this context and before and after the devices became available, some of which were based on the earlier work with integrating data from single devices. These involved validating classical PET methods with MRI (eg, perfusion or oncology diagnostics). More important, however, were the scenarios where intermodal synergies could be expected. In this review, we look back on this decade-long journey, at the challenges overcome and those still to come.
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Affiliation(s)
- Borjana Bogdanovic
- Department of Nuclear Medicine, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Esteban Lucas Solari
- Department of Nuclear Medicine, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Alberto Villagran Asiares
- Department of Nuclear Medicine, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Lachlan McIntosh
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Sandra van Marwick
- Department of Nuclear Medicine, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Sylvia Schachoff
- Department of Nuclear Medicine, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Stephan G Nekolla
- Department of Nuclear Medicine, Klinikum rechts der Isar, Technische Universität München, Munich, Germany; DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany.
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Ahmed KT, Sun J, Cheng S, Yong J, Zhang W. Multi-omics data integration by generative adversarial network. Bioinformatics 2021; 38:179-186. [PMID: 34415323 DOI: 10.1093/bioinformatics/btab608] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 07/27/2021] [Accepted: 08/18/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Accurate disease phenotype prediction plays an important role in the treatment of heterogeneous diseases like cancer in the era of precision medicine. With the advent of high throughput technologies, more comprehensive multi-omics data is now available that can effectively link the genotype to phenotype. However, the interactive relation of multi-omics datasets makes it particularly challenging to incorporate different biological layers to discover the coherent biological signatures and predict phenotypic outcomes. In this study, we introduce omicsGAN, a generative adversarial network model to integrate two omics data and their interaction network. The model captures information from the interaction network as well as the two omics datasets and fuse them to generate synthetic data with better predictive signals. RESULTS Large-scale experiments on The Cancer Genome Atlas breast cancer, lung cancer and ovarian cancer datasets validate that (i) the model can effectively integrate two omics data (e.g. mRNA and microRNA expression data) and their interaction network (e.g. microRNA-mRNA interaction network). The synthetic omics data generated by the proposed model has a better performance on cancer outcome classification and patients survival prediction compared to original omics datasets. (ii) The integrity of the interaction network plays a vital role in the generation of synthetic data with higher predictive quality. Using a random interaction network does not allow the framework to learn meaningful information from the omics datasets; therefore, results in synthetic data with weaker predictive signals. AVAILABILITY AND IMPLEMENTATION Source code is available at: https://github.com/CompbioLabUCF/omicsGAN. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Khandakar Tanvir Ahmed
- Department of Computer Science, University of Central Florida, Orlando, FL 32816, USA
- Genomics and Bioinformatics Cluster, University of Central Florida, Orlando, FL 32816, USA
| | - Jiao Sun
- Department of Computer Science, University of Central Florida, Orlando, FL 32816, USA
- Genomics and Bioinformatics Cluster, University of Central Florida, Orlando, FL 32816, USA
| | - Sze Cheng
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota Twin Cities, Minneapolis, MN 55455, USA
| | - Jeongsik Yong
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota Twin Cities, Minneapolis, MN 55455, USA
| | - Wei Zhang
- Department of Computer Science, University of Central Florida, Orlando, FL 32816, USA
- Genomics and Bioinformatics Cluster, University of Central Florida, Orlando, FL 32816, USA
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Guan H, Wang C, Tao D. MRI-based Alzheimer's disease prediction via distilling the knowledge in multi-modal data. Neuroimage 2021; 244:118586. [PMID: 34563678 DOI: 10.1016/j.neuroimage.2021.118586] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 09/09/2021] [Accepted: 09/16/2021] [Indexed: 12/14/2022] Open
Abstract
Mild cognitive impairment (MCI) conversion prediction, i.e., identifying MCI patients of high risks converting to Alzheimer's disease (AD), is essential for preventing or slowing the progression of AD. Although previous studies have shown that the fusion of multi-modal data can effectively improve the prediction accuracy, their applications are largely restricted by the limited availability or high cost of multi-modal data. Building an effective prediction model using only magnetic resonance imaging (MRI) remains a challenging research topic. In this work, we propose a multi-modal multi-instance distillation scheme, which aims to distill the knowledge learned from multi-modal data to an MRI-based network for MCI conversion prediction. In contrast to existing distillation algorithms, the proposed multi-instance probabilities demonstrate a superior capability of representing the complicated atrophy distributions, and can guide the MRI-based network to better explore the input MRI. To our best knowledge, this is the first study that attempts to improve an MRI-based prediction model by leveraging extra supervision distilled from multi-modal information. Experiments demonstrate the advantage of our framework, suggesting its potentials in the data-limited clinical settings.
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Affiliation(s)
- Hao Guan
- School of Computer Science, The University of Sydney, Australia
| | - Chaoyue Wang
- School of Computer Science, The University of Sydney, Australia.
| | - Dacheng Tao
- School of Computer Science, The University of Sydney, Australia; JD Explore Academy, China.
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Rowe TW, Katzourou IK, Stevenson-Hoare JO, Bracher-Smith MR, Ivanov DK, Escott-Price V. Machine learning for the life-time risk prediction of Alzheimer's disease: a systematic review. Brain Commun 2021; 3:fcab246. [PMID: 34805994 PMCID: PMC8598986 DOI: 10.1093/braincomms/fcab246] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 06/30/2021] [Accepted: 07/19/2021] [Indexed: 12/23/2022] Open
Abstract
Alzheimer’s disease is a neurodegenerative disorder and the most common form of dementia. Early diagnosis may assist interventions to delay onset and reduce the progression rate of the disease. We systematically reviewed the use of machine learning algorithms for predicting Alzheimer’s disease using single nucleotide polymorphisms and instances where these were combined with other types of data. We evaluated the ability of machine learning models to distinguish between controls and cases, while also assessing their implementation and potential biases. Articles published between December 2009 and June 2020 were collected using Scopus, PubMed and Google Scholar. These were systematically screened for inclusion leading to a final set of 12 publications. Eighty-five per cent of the included studies used the Alzheimer's Disease Neuroimaging Initiative dataset. In studies which reported area under the curve, discrimination varied (0.49–0.97). However, more than half of the included manuscripts used other forms of measurement, such as accuracy, sensitivity and specificity. Model calibration statistics were also found to be reported inconsistently across all studies. The most frequent limitation in the assessed studies was sample size, with the total number of participants often numbering less than a thousand, whilst the number of predictors usually ran into the many thousands. In addition, key steps in model implementation and validation were often not performed or unreported, making it difficult to assess the capability of machine learning models.
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Affiliation(s)
- Thomas W Rowe
- UK Dementia Research Institute, Cardiff University, Cardiff, UK
| | | | | | - Matthew R Bracher-Smith
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff CF24 4HQ, UK
| | - Dobril K Ivanov
- UK Dementia Research Institute, Cardiff University, Cardiff, UK
| | - Valentina Escott-Price
- UK Dementia Research Institute, Cardiff University, Cardiff, UK.,Division of Psychological Medicine and Clinical Neurosciences, School of Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff CF24 4HQ, UK
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58
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Hu K, Wang M, Liu Y, Yan H, Song M, Chen J, Chen Y, Wang H, Guo H, Wan P, Lv L, Yang Y, Li P, Lu L, Yan J, Wang H, Zhang H, Zhang D, Wu H, Ning Y, Jiang T, Liu B. Multisite schizophrenia classification by integrating structural magnetic resonance imaging data with polygenic risk score. Neuroimage Clin 2021; 32:102860. [PMID: 34749286 PMCID: PMC8567302 DOI: 10.1016/j.nicl.2021.102860] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 09/29/2021] [Accepted: 10/13/2021] [Indexed: 11/21/2022]
Abstract
Previous brain structural magnetic resonance imaging studies reported that patients with schizophrenia have brain structural abnormalities, which have been used to discriminate schizophrenia patients from normal controls. However, most existing studies identified schizophrenia patients at a single site, and the genetic features closely associated with highly heritable schizophrenia were not considered. In this study, we performed standardized feature extraction on brain structural magnetic resonance images and on genetic data to separate schizophrenia patients from normal controls. A total of 1010 participants, 508 schizophrenia patients and 502 normal controls, were recruited from 8 independent sites across China. Classification experiments were carried out using different machine learning methods and input features. We tested a support vector machine, logistic regression, and an ensemble learning strategy using 3 feature sets of interest: (1) imaging features: gray matter volume, (2) genetic features: polygenic risk scores, and (3) a fusion of imaging features and genetic features. The performance was assessed by leave-one-site-out cross-validation. Finally, some important brain and genetic features were identified. We found that the models with both imaging and genetic features as input performed better than models with either alone. The average accuracy of the classification models with the best performance in the cross-validation was 71.6%. The genetic feature that measured the cumulative risk of the genetic variants most associated with schizophrenia contributed the most to the classification. Our work took the first step toward considering both structural brain alterations and genome-wide genetic factors in a large-scale multisite schizophrenia classification. Our findings may provide insight into the underlying pathophysiology and risk mechanisms of schizophrenia.
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Affiliation(s)
- Ke Hu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Meng Wang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Yong Liu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Hao Yan
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China; Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Ming Song
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Jun Chen
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yunchun Chen
- Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi'an, China
| | - Huaning Wang
- Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi'an, China
| | - Hua Guo
- Zhumadian Psychiatric Hospital, Zhumadian, China
| | - Ping Wan
- Zhumadian Psychiatric Hospital, Zhumadian, China
| | - Luxian Lv
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
| | - Yongfeng Yang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
| | - Peng Li
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China; Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Lin Lu
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China; Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Jun Yan
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China; Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Huiling Wang
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China; Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Hongxing Zhang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China; Department of Psychology, Xinxiang Medical University, Xinxiang, China
| | - Dai Zhang
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China; Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China; Center for Life Sciences/PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Huawang Wu
- The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China
| | - Yuping Ning
- The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China
| | - Tianzi Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Queensland Brain Institute, University of Queensland, Brisbane, Australia.
| | - Bing Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Chinese Institute for Brain Research, Beijing, China.
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Vilor-Tejedor N, Garrido-Martín D, Rodriguez-Fernandez B, Lamballais S, Guigó R, Gispert JD. Multivariate Analysis and Modelling of multiple Brain endOphenotypes: Let's MAMBO! Comput Struct Biotechnol J 2021; 19:5800-5810. [PMID: 34765095 PMCID: PMC8567328 DOI: 10.1016/j.csbj.2021.10.019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 10/08/2021] [Accepted: 10/12/2021] [Indexed: 12/01/2022] Open
Abstract
Imaging genetic studies aim to test how genetic information influences brain structure and function by combining neuroimaging-based brain features and genetic data from the same individual. Most studies focus on individual correlation and association tests between genetic variants and a single measurement of the brain. Despite the great success of univariate approaches, given the capacity of neuroimaging methods to provide a multiplicity of cerebral phenotypes, the development and application of multivariate methods become crucial. In this article, we review novel methods and strategies focused on the analysis of multiple phenotypes and genetic data. We also discuss relevant aspects of multi-trait modelling in the context of neuroimaging data.
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Affiliation(s)
- Natalia Vilor-Tejedor
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Spain
- Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, Netherlands
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Diego Garrido-Martín
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Spain
| | | | - Sander Lamballais
- Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, Netherlands
| | - Roderic Guigó
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina, Madrid, Spain
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Sheng J, Wang L, Cheng H, Zhang Q, Zhou R, Shi Y. Strategies for multivariate analyses of imaging genetics study in Alzheimer's disease. Neurosci Lett 2021; 762:136147. [PMID: 34332030 DOI: 10.1016/j.neulet.2021.136147] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 03/27/2021] [Accepted: 07/26/2021] [Indexed: 11/16/2022]
Abstract
Alzheimer's disease (AD) is an incurable neurodegenerative disease primarily affecting the elderly population. Early diagnosis of AD is critical for the management of this disease. Imaging genetics examines the influence of genetic variants (i.e., single nucleotide polymorphisms (SNPs)) on brain structure and function and many novel approaches of imaging genetics are proposed for studying AD. We review and synthesize the Alzheimer's Disease Neuroimaging Initiative (ADNI) genetic associations with quantitative disease endophenotypes including structural and functional neuroimaging, diffusion tensor imaging (DTI), positron emission tomography (PET), and fluid biomarker assays. In this review, we survey recent publications using neuroimaging and genetic data of AD, with a focus on methods capturing multivariate effects accommodating the large number variables from both imaging data and genetic data. We review methods focused on bridging the imaging and genetic data by establishing genotype-phenotype association, including sparse canonical correlation analysis, parallel independent component analysis, sparse reduced rank regression, sparse partial least squares, genome-wide association study, and so on. The broad availability and wide scope of ADNI genetic and phenotypic data has advanced our understanding of the genetic basis of AD and has nominated novel targets for future pharmaceutical therapy and biomarker development.
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Affiliation(s)
- Jinhua Sheng
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang 310018, China.
| | - Luyun Wang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang 310018, China; College of Information Engineering, Hangzhou Vocational & Technical College, Hangzhou, Zhejiang 310018, China
| | - Hu Cheng
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | | | - Rougang Zhou
- Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang 310018, China; School of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China; Mstar Technologies Inc., Hangzhou, Zhejiang 310018, China
| | - Yuchen Shi
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang 310018, China
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Dashwood M, Churchhouse G, Young M, Kuruvilla T. Artificial intelligence as an aid to diagnosing dementia: an overview. PROGRESS IN NEUROLOGY AND PSYCHIATRY 2021. [DOI: 10.1002/pnp.721] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Mark Dashwood
- Dr Dashwood and Dr Churchhouse are Advanced Trainees in Old Age Psychiatry; Dr Young is a CT2, and Dr Kuruvilla is a Consultant in Old Age Psychiatry, all at Gloucestershire Health & Care NHS Foundation Trust, Cheltenham
| | - Gabrielle Churchhouse
- Dr Dashwood and Dr Churchhouse are Advanced Trainees in Old Age Psychiatry; Dr Young is a CT2, and Dr Kuruvilla is a Consultant in Old Age Psychiatry, all at Gloucestershire Health & Care NHS Foundation Trust, Cheltenham
| | - Matilda Young
- Dr Dashwood and Dr Churchhouse are Advanced Trainees in Old Age Psychiatry; Dr Young is a CT2, and Dr Kuruvilla is a Consultant in Old Age Psychiatry, all at Gloucestershire Health & Care NHS Foundation Trust, Cheltenham
| | - Tarun Kuruvilla
- Dr Dashwood and Dr Churchhouse are Advanced Trainees in Old Age Psychiatry; Dr Young is a CT2, and Dr Kuruvilla is a Consultant in Old Age Psychiatry, all at Gloucestershire Health & Care NHS Foundation Trust, Cheltenham
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Nayor M, Shen L, Hunninghake GM, Kochunov P, Barr RG, Bluemke DA, Broeckel U, Caravan P, Cheng S, de Vries PS, Hoffmann U, Kolossváry M, Li H, Luo J, McNally EM, Thanassoulis G, Arnett DK, Vasan RS. Progress and Research Priorities in Imaging Genomics for Heart and Lung Disease: Summary of an NHLBI Workshop. Circ Cardiovasc Imaging 2021; 14:e012943. [PMID: 34387095 PMCID: PMC8486340 DOI: 10.1161/circimaging.121.012943] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Imaging genomics is a rapidly evolving field that combines state-of-the-art bioimaging with genomic information to resolve phenotypic heterogeneity associated with genomic variation, improve risk prediction, discover prevention approaches, and enable precision diagnosis and treatment. Contemporary bioimaging methods provide exceptional resolution generating discrete and quantitative high-dimensional phenotypes for genomics investigation. Despite substantial progress in combining high-dimensional bioimaging and genomic data, methods for imaging genomics are evolving. Recognizing the potential impact of imaging genomics on the study of heart and lung disease, the National Heart, Lung, and Blood Institute convened a workshop to review cutting-edge approaches and methodologies in imaging genomics studies, and to establish research priorities for future investigation. This report summarizes the presentations and discussions at the workshop. In particular, we highlight the need for increased availability of imaging genomics data in diverse populations, dedicated focus on less common conditions, and centralization of efforts around specific disease areas.
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Affiliation(s)
- Matthew Nayor
- Cardiology Division, Department of Medicine, Massachusetts
General Hospital, Harvard Medical School, Boston, MA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics,
Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Gary M. Hunninghake
- Division of Pulmonary and Critical Care Medicine, Harvard
Medical School, Brigham and Women’s Hospital, Boston, MA
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of
Psychiatry, University of Maryland School of Medicine, Baltimore, MD
| | - R. Graham Barr
- Department of Medicine and Department of Epidemiology,
Mailman School of Public Health, Columbia University Irving Medical Center, New
York, NY
| | - David A. Bluemke
- Department of Radiology, University of Wisconsin-Madison
School of Medicine and Public Health, Madison, WI
| | - Ulrich Broeckel
- Section of Genomic Pediatrics, Department of Pediatrics,
Medicine and Physiology, Children’s Research Institute and Genomic Sciences
and Precision Medicine Center, Medical College of Wisconsin, Milwaukee, WI
| | - Peter Caravan
- Institute for Innovation in Imaging, Athinoula A. Martinos
Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical
School, Charlestown, MA
| | - Susan Cheng
- Department of Cardiology, Smidt Heart Institute,
Cedars-Sinai Medical Center, Los Angeles, CA
| | - Paul S. de Vries
- Human Genetics Center, Department of Epidemiology, Human
Genetics, and Environmental Sciences, School of Public Health, The University of
Texas Health Science Center at Houston, Houston, TX
| | - Udo Hoffmann
- Department of Radiology, Harvard Medical School,
Massachusetts General Hospital, Boston, Massachusetts
| | - Márton Kolossváry
- Department of Radiology, Harvard Medical School,
Massachusetts General Hospital, Boston, Massachusetts
| | - Huiqing Li
- Division of Cardiovascular Sciences, National Heart,
Lung, and Blood Institute, Bethesda, MD
| | - James Luo
- Division of Cardiovascular Sciences, National Heart,
Lung, and Blood Institute, Bethesda, MD
| | - Elizabeth M. McNally
- Center for Genetic Medicine, Northwestern University
Feinberg School of Medicine, Chicago, IL
| | - George Thanassoulis
- Preventive and Genomic Cardiology, McGill University
Health Center and Research Institute, Montreal, Quebec, Canada
| | - Donna K. Arnett
- College of Public Health, University of Kentucky,
Lexington KY
| | - Ramachandran S. Vasan
- Sections of Preventive Medicine and Epidemiology, and
Cardiology, Department of Medicine, Department of Epidemiology, Boston University
Schools of Medicine and Public Health, and Center for Computing and Data Sciences,
Boston University, Boston, MA
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63
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Lin E, Lin CH, Lane HY. Deep Learning with Neuroimaging and Genomics in Alzheimer's Disease. Int J Mol Sci 2021; 22:7911. [PMID: 34360676 PMCID: PMC8347529 DOI: 10.3390/ijms22157911] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 07/17/2021] [Accepted: 07/22/2021] [Indexed: 12/21/2022] Open
Abstract
A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer's disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.
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Affiliation(s)
- Eugene Lin
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA;
- Department of Electrical & Computer Engineering, University of Washington, Seattle, WA 98195, USA
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
| | - Chieh-Hsin Lin
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
- Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 83301, Taiwan
- School of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
| | - Hsien-Yuan Lane
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
- Department of Psychiatry, China Medical University Hospital, Taichung 40447, Taiwan
- Brain Disease Research Center, China Medical University Hospital, Taichung 40447, Taiwan
- Department of Psychology, College of Medical and Health Sciences, Asia University, Taichung 41354, Taiwan
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64
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Huang M, Lai H, Yu Y, Chen X, Wang T, Feng Q. Deep-gated recurrent unit and diet network-based genome-wide association analysis for detecting the biomarkers of Alzheimer's disease. Med Image Anal 2021; 73:102189. [PMID: 34343841 DOI: 10.1016/j.media.2021.102189] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 05/30/2021] [Accepted: 07/16/2021] [Indexed: 01/01/2023]
Abstract
Genome-wide association analysis (GWAS) is a commonly used method to detect the potential biomarkers of Alzheimer's disease (AD). Most existing GWAS methods entail a high computational cost, disregard correlations among imaging data and correlations among genetic data, and ignore various associations between longitudinal imaging and genetic data. A novel GWAS method was proposed to identify potential AD biomarkers and address these problems. A network based on a gated recurrent unit was applied without imputing incomplete longitudinal imaging data to integrate the longitudinal data of variable lengths and extract an image representation. In this study, a modified diet network that can considerably reduce the number of parameters in the genetic network was proposed to perform GWAS between image representation and genetic data. Genetic representation can be extracted in this way. A link between genetic representation and AD was established to detect potential AD biomarkers. The proposed method was tested on a set of simulated data and a real AD dataset. Results of the simulated data showed that the proposed method can accurately detect relevant biomarkers. Moreover, the results of real AD dataset showed that the proposed method can detect some new risk-related genes of AD. Based on previous reports, no research has incorporated a deep-learning model into a GWAS framework to investigate the potential information on super-high-dimensional genetic data and longitudinal imaging data and create a link between imaging genetics and AD for detecting potential AD biomarkers. Therefore, the proposed method may provide new insights into the underlying pathological mechanism of AD.
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Affiliation(s)
- Meiyan Huang
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China.
| | - Haoran Lai
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
| | - Yuwei Yu
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
| | - Xiumei Chen
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
| | - Tao Wang
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
| | - Qianjin Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou 510515, China.
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65
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Alzheimer's disease diagnosis framework from incomplete multimodal data using convolutional neural networks. J Biomed Inform 2021; 121:103863. [PMID: 34229061 DOI: 10.1016/j.jbi.2021.103863] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 06/09/2021] [Accepted: 07/01/2021] [Indexed: 11/23/2022]
Abstract
Alzheimer's disease (AD) is a severe irreversible neurodegenerative disease that has great sufferings on patients and eventually leads to death. Early detection of AD and its prodromal stage, mild cognitive impairment (MCI) which can be either stable (sMCI) or progressive (pMCI), is highly desirable for effective treatment planning and tailoring therapy. Recent studies recommended using multimodal data fusion of genetic (single nucleotide polymorphisms, SNPs) and neuroimaging data (magnetic resonance imaging (MRI) and positron emission tomography (PET)) to discriminate AD/MCI from normal control (NC) subjects. However, missing multimodal data in the cohort under study is inevitable. In addition, data heterogeneity between phenotypes and genotypes biomarkers makes learning capability of the models more challenging. Also, the current studies mainly focus on identifying brain disease classification and ignoring the regression task. Furthermore, they utilize multistage for predicting the brain disease progression. To address these issues, we propose a novel multimodal neuroimaging and genetic data fusion for joint classification and clinical score regression tasks using the maximum number of available samples in one unified framework using convolutional neural network (CNN). Specifically, we initially perform a technique based on linear interpolation to fill the missing features for each incomplete sample. Then, we learn the neuroimaging features from MRI, PET, and SNPs using CNN to alleviate the heterogeneity among genotype and phenotype data. Meanwhile, the high learned features from each modality are combined for jointly identifying brain diseases and predicting clinical scores. To validate the performance of the proposed method, we test our method on 805 subjects from Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Also, we verify the similarity between the synthetic and real data using statistical analysis. Moreover, the experimental results demonstrate that the proposed method can yield better performance in both classification and regression tasks. Specifically, our proposed method achieves accuracy of 98.22%, 93.11%, and 97.35% for NC vs. AD, NC vs. sMCI, and NC vs. pMCI, respectively. On the other hand, our method attains the lowest root mean square error and the highest correlation coefficient for different clinical scores regression tasks compared with the state-of-the-art methods.
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66
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Soumare H, Rezgui S, Gmati N, Benkahla A. New neural network classification method for individuals ancestry prediction from SNPs data. BioData Min 2021; 14:30. [PMID: 34183066 PMCID: PMC8240223 DOI: 10.1186/s13040-021-00258-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 03/29/2021] [Indexed: 11/18/2022] Open
Abstract
Artificial Neural Network (ANN) algorithms have been widely used to analyse genomic data. Single Nucleotide Polymorphisms(SNPs) represent the genetic variations, the most common in the human genome, it has been shown that they are involved in many genetic diseases, and can be used to predict their development. Developing ANN to handle this type of data can be considered as a great success in the medical world. However, the high dimensionality of genomic data and the availability of a limited number of samples can make the learning task very complicated. In this work, we propose a New Neural Network classification method based on input perturbation. The idea is first to use SVD to reduce the dimensionality of the input data and to train a classification network, which prediction errors are then reduced by perturbing the SVD projection matrix. The proposed method has been evaluated on data from individuals with different ancestral origins, the experimental results have shown the effectiveness of the proposed method. Achieving up to 96.23% of classification accuracy, this approach surpasses previous Deep learning approaches evaluated on the same dataset.
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Affiliation(s)
- H. Soumare
- The Laboratory of Mathematical Modelling and Numeric in Engineering Sciences, National Engineering School of Tunis, Rue Béchir Salem Belkhiria Campus universitaire, B.P. 37, 1002 Tunis Belvédère, University of Tunis El Manar, Tunis, Tunisia
- Laboratory of BioInformatics, bioMathematics, and bioStatistics, 13 place Pasteur, B.P. 74 1002 Tunis, Belvédère, Institut Pasteur de Tunis, University of Tunis El Manar, Tunis, Tunisia
| | - S. Rezgui
- ADAGOS. Le Belvédère centre, 61 rue El Khartoum, El Menzah, Tunis, Tunisia
| | - N. Gmati
- College of sciences & Basic and Applied Scientific Research Center, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, 31441, Dammam, Kingdom of Saudi Arabia, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - A. Benkahla
- Laboratory of BioInformatics, bioMathematics, and bioStatistics, 13 place Pasteur, B.P. 74 1002 Tunis, Belvédère, Institut Pasteur de Tunis, University of Tunis El Manar, Tunis, Tunisia
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67
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The Deep Learning LSTM and MTD Models Best Predict Acute Respiratory Infection among Under-Five-Year Old Children in Somaliland. Symmetry (Basel) 2021. [DOI: 10.3390/sym13071156] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
The most effective techniques for predicting time series patterns include machine learning and classical time series methods. The aim of this study is to search for the best artificial intelligence and classical forecasting techniques that can predict the spread of acute respiratory infection (ARI) and pneumonia among under-five-year old children in Somaliland. The techniques used in the study include seasonal autoregressive integrated moving averages (SARIMA), mixture transitions distribution (MTD), and long short term memory (LSTM) deep learning. The data used in the study were monthly observations collected from five regions in Somaliland from 2011–2014. Prediction results from the three best competing models are compared by using root mean square error (RMSE) and absolute mean deviation (MAD) accuracy measures. Results have shown that the deep learning LSTM and MTD models slightly outperformed the classical SARIMA model in predicting ARI values.
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68
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A Deep Neural Network-Based Method for Prediction of Dementia Using Big Data. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18105386. [PMID: 34070100 PMCID: PMC8158341 DOI: 10.3390/ijerph18105386] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Revised: 05/12/2021] [Accepted: 05/13/2021] [Indexed: 01/14/2023]
Abstract
The rise in dementia among the aging Korean population will quickly create a financial burden on society, but timely recognition of early warning for dementia and proper responses to the occurrence of dementia can enhance medical treatment. Health behavior and medical service usage data are relatively more accessible than clinical data, and a prescreening tool with easily accessible data could be a good solution for dementia-related problems. In this paper, we apply a deep neural network (DNN) to prediction of dementia using health behavior and medical service usage data, using data from 7031 subjects aged over 65 collected from the Korea National Health and Nutrition Examination Survey (KNHANES) in 2001 and 2005. In the proposed model, principal component analysis (PCA) featuring and min/max scaling are used to preprocess and extract relevant background features. We compared our proposed methodology, a DNN/scaled PCA, with five well-known machine learning algorithms. The proposed methodology shows 85.5% of the area under the curve (AUC), a better result than that using other algorithms. The proposed early prescreening method for possible dementia can be used by both patients and doctors.
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69
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Ma Z, Jing B, Li Y, Yan H, Li Z, Ma X, Zhuo Z, Wei L, Li H. Identifying Mild Cognitive Impairment with Random Forest by Integrating Multiple MRI Morphological Metrics. J Alzheimers Dis 2021; 73:991-1002. [PMID: 31884464 DOI: 10.3233/jad-190715] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Mild cognitive impairment (MCI) exhibits a high risk of progression to Alzheimer's disease (AD), and it is commonly deemed as the precursor of AD. It is important to find effective and robust ways for the early diagnosis of MCI. In this paper, a random forest-based method combining multiple morphological metrics was proposed to identify MCI from normal controls (NC). Voxel-based morphometry, deformation-based morphometry, and surface-based morphometry were utilized to extract morphological metrics such as gray matter volume, Jacobian determinant value, cortical thickness, gyrification index, sulcus depth, and fractal dimension. An initial discovery dataset (56 MCI/55 NC) from the ADNI were used to construct classification models and the performances were testified with 10-fold cross validation. To test the generalization of the proposed method, two extra validation datasets including longitudinal ADNI data (30 MCI/16 NC) and collected data from Xuanwu Hospital (27 MCI/32 NC) were employed respectively to evaluate the performance. No matter whether testing was done on the discovery dataset or the extra validation datasets, the accuracies were about 80% with the combined morphological metrics, which were significantly superior to single metric (accuracy: 45% ∼76%) and also displayed good generalization across datasets. Additionally, gyrification index and cortical thickness derived from surface-based morphometry outperformed other features in MCI identification, suggesting they were some key morphological biomarkers for early MCI diagnosis. Combining the multiple morphological metrics together resulted in a significantly better and reliable identification model, which may be helpful to assist in the clinical diagnosis of MCI.
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Affiliation(s)
- Zhe Ma
- School of Biomedical Engineering, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Bin Jing
- School of Biomedical Engineering, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Yuxia Li
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China
| | - Huagang Yan
- School of Biomedical Engineering, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Zhaoxia Li
- School of Chinese Medicine, Capital Medical University, Beijing, China
| | - Xiangyu Ma
- School of Biomedical Engineering, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Zhizheng Zhuo
- School of Biomedical Engineering, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Lijiang Wei
- School of Biomedical Engineering, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Haiyun Li
- School of Biomedical Engineering, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
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70
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Lei B, Yu S, Zhao X, Frangi AF, Tan EL, Elazab A, Wang T, Wang S. Diagnosis of early Alzheimer's disease based on dynamic high order networks. Brain Imaging Behav 2021; 15:276-287. [PMID: 32789620 DOI: 10.1007/s11682-019-00255-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Machine learning methods have been widely used for early diagnosis of Alzheimer's disease (AD) via functional connectivity networks (FCNs) analysis from neuroimaging data. The conventional low-order FCNs are obtained by time-series correlation of the whole brain based on resting-state functional magnetic resonance imaging (R-fMRI). However, FCNs overlook inter-region interactions, which limits application to brain disease diagnosis. To overcome this drawback, we develop a novel framework to exploit the high-level dynamic interactions among brain regions for early AD diagnosis. Specifically, a sliding window approach is employed to generate some R-fMRI sub-series. The correlations among these sub-series are then used to construct a series of dynamic FCNs. High-order FCNs based on the topographical similarity between each pair of the dynamic FCNs are then constructed. Afterward, a local weight clustering method is used to extract effective features of the network, and the least absolute shrinkage and selection operation method is chosen for feature selection. A support vector machine is employed for classification, and the dynamic high-order network approach is evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Our experimental results demonstrate that the proposed approach not only achieves promising results for AD classification, but also successfully recognizes disease-related biomarkers.
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Affiliation(s)
- Baiying Lei
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Shuangzhi Yu
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Xin Zhao
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Alejandro F Frangi
- CISTIB Centre for Computational Imaging & Simulation technologies in Biomedicine, School of Computing and the School of Medicine, University of Leeds, Leeds, UK
| | - Ee-Leng Tan
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore
| | - Ahmed Elazab
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Tianfu Wang
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University, Shenzhen, 518060, China.
| | - Shuqiang Wang
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong Province, 518000, People's Republic of China.
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71
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Arabi H, AkhavanAllaf A, Sanaat A, Shiri I, Zaidi H. The promise of artificial intelligence and deep learning in PET and SPECT imaging. Phys Med 2021; 83:122-137. [DOI: 10.1016/j.ejmp.2021.03.008] [Citation(s) in RCA: 84] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Revised: 02/18/2021] [Accepted: 03/03/2021] [Indexed: 02/06/2023] Open
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72
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Zhou J, Hu L, Jiang Y, Liu L. A Correlation Analysis between SNPs and ROIs of Alzheimer's Disease Based on Deep Learning. BIOMED RESEARCH INTERNATIONAL 2021; 2021:8890513. [PMID: 33628827 PMCID: PMC7886593 DOI: 10.1155/2021/8890513] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Revised: 12/23/2020] [Accepted: 01/27/2021] [Indexed: 12/31/2022]
Abstract
Motivation. At present, the research methods for image genetics of Alzheimer's disease based on machine learning are mainly divided into three steps: the first step is to preprocess the original image and gene information into digital signals that are easy to calculate; the second step is feature selection aiming at eliminating redundant signals and obtain representative features; and the third step is to build a learning model and predict the unknown data with regression or bivariate correlation analysis. This type of method requires manual extraction of feature single-nucleotide polymorphisms (SNPs), and the extraction process relies on empirical knowledge to a certain extent, such as linkage imbalance and gene function information in a group sparse model, which puts forward certain requirements for applicable scenarios and application personnel. To solve the problems of insufficient biological significance and large errors in the previous methods of association analysis and disease diagnosis, this paper presents a method of correlation analysis and disease diagnosis between SNP and region of interest (ROI) based on a deep learning model. It is a data-driven method, which has no obvious feature selection process. Results. The deep learning method adopted in this paper has no obvious feature extraction process relying on prior knowledge and model assumptions. From the results of correlation analysis between SNP and ROI, this method is complementary to other regression model methods in application scenarios. In order to improve the disease diagnosis performance of deep learning, we use the deep learning model to integrate SNP characteristics and ROI characteristics. The SNP feature, ROI feature, and SNP-ROI joint feature were input into the deep learning model and trained by cross-validation technique. The experimental results show that the SNP-ROI joint feature describes the information of the samples from different angles, which makes the diagnosis accuracy higher.
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Affiliation(s)
- Juan Zhou
- School of Software, East China Jiaotong University, Nanchang 330013, China
| | - Linfeng Hu
- School of Software, East China Jiaotong University, Nanchang 330013, China
| | - Yu Jiang
- School of Software, East China Jiaotong University, Nanchang 330013, China
| | - Liyue Liu
- School of Software, East China Jiaotong University, Nanchang 330013, China
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73
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Li C, Yang Y, Liang H, Wu B. Transfer learning for establishment of recognition of COVID-19 on CT imaging using small-sized training datasets. Knowl Based Syst 2021; 218:106849. [PMID: 33584016 PMCID: PMC7866884 DOI: 10.1016/j.knosys.2021.106849] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 01/27/2021] [Accepted: 02/02/2021] [Indexed: 01/08/2023]
Abstract
The coronavirus disease, called COVID-19, which is spreading fast worldwide since the end of 2019, and has become a global challenging pandemic. Until 27th May 2020, it caused more than 5.6 million individuals infected throughout the world and resulted in greater than 348,145 deaths. CT images-based classification technique has been tried to use the identification of COVID-19 with CT imaging by hospitals, which aims to minimize the possibility of virus transmission and alleviate the burden of clinicians and radiologists. Early diagnosis of COVID-19, which not only prevents the disease from spreading further but allows more reasonable allocation of limited medical resources. Therefore, CT images play an essential role in identifying cases of COVID-19 that are in great need of intensive clinical care. Unfortunately, the current public health emergency, which has caused great difficulties in collecting a large set of precise data for training neural networks. To tackle this challenge, our first thought is transfer learning, which is a technique that aims to transfer the knowledge from one or more source tasks to a target task when the latter has fewer training data. Since the training data is relatively limited, so a transfer learning-based DensNet-121 approach for the identification of COVID-19 is established. The proposed method is inspired by the precious work of predecessors such as CheXNet for identifying common Pneumonia, which was trained using the large Chest X-ray14 dataset, and the dataset contains 112,120 frontal chest X-rays of 14 different chest diseases (including Pneumonia) that are individually labeled and achieved good performance. Therefore, CheXNet as the pre-trained network was used for the target task (COVID-19 classification) by fine-tuning the network weights on the small-sized dataset in the target task. Finally, we evaluated our proposed method on the COVID-19-CT dataset. Experimentally, our method achieves state-of-the-art performance for the accuracy (ACC) and F1-score. The quantitative indicators show that the proposed method only uses a GPU can reach the best performance, up to 0.87 and 0.86, respectively, compared with some widely used and recent deep learning methods, which are helpful for COVID-19 diagnosis and patient triage. The codes used in this manuscript are publicly available on GitHub at (https://github.com/lichun0503/CT-Classification).
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Affiliation(s)
- Chun Li
- School of Science, Harbin Institute of Technology, Shenzhen, 518055, China
| | - Yunyun Yang
- School of Science, Harbin Institute of Technology, Shenzhen, 518055, China
| | - Hui Liang
- School of Science, Harbin Institute of Technology, Shenzhen, 518055, China
| | - Boying Wu
- Department of Mathematics, Harbin Institute of Technology, Harbin, 150006, China
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Liang L, Zhou P, Lu W, Guo X, Ye C, Lv H, Wang T, Ma T. An anatomical knowledge-based MRI deep learning pipeline for white matter hyperintensity quantification associated with cognitive impairment. Comput Med Imaging Graph 2021; 89:101873. [PMID: 33610084 DOI: 10.1016/j.compmedimag.2021.101873] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2020] [Revised: 12/29/2020] [Accepted: 01/27/2021] [Indexed: 11/29/2022]
Abstract
Recent studies have confirmed that white matter hyperintensities (WMHs) accumulated in strategic brain regions can predict cognitive impairments associated with Alzheimer's disease (AD). The knowledge of white matter anatomy facilitates lesion-symptom mapping associated with cognition, and provides important spatial information for lesion segmentation algorithms. However, deep learning-based methods in the white matter hyperintensity (WMH) segmentation realm do not take full advantage of anatomical knowledge in decision-making and lesion localization processes. In this paper, we proposed an anatomical knowledge-based MRI deep learning pipeline (AU-Net), handcrafted anatomical-based spatial features developed from brain atlas were integrated with a well-designed U-Net configuration to simultaneously segment and locate WMHs. Manually annotated data from WMH segmentation challenge were used for the evaluation. We then applied this pipeline to investigate the association between WMH burden and cognition within another publicly available database. The results showed that AU-Net significantly improved segmentation performance compared with methods that did not incorporate anatomical knowledge (p < 0.05), and achieved a 14-17% increase based on area under the curve (AUC) in the cohort with mild WMH burden. Different configurations for incorporating anatomical knowledge were evaluated, the proposed stage-wise AU-Net-two-step method achieved the best performance (Dice: 0.86, modified Hausdorff distance: 3.06 mm), which was comparable with the state-of-the-art method (Dice: 0.87, modified Hausdorff distance: 3.62 mm). We observed different WMH accumulation patterns associated with normal aging and cognitive impairments. Furthermore, the characteristics of individual WMH lesions located in strategic regions (frontal and parietal subcortical white matter, as well as corpus callosum) were significantly correlated with cognition after adjusting for total lesion volumes. Our findings suggest that AU-Net is a reliable method to segment and quantify brain WMHs in elderly cohorts, and is valuable in individual cognition evaluation.
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Affiliation(s)
- Li Liang
- School of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, Guangdong, China; Peng Cheng Laboratory, Shenzhen, Guangdong, China
| | | | - Wanxin Lu
- School of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, Guangdong, China
| | - Xutao Guo
- School of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, Guangdong, China; Peng Cheng Laboratory, Shenzhen, Guangdong, China
| | - Chenfei Ye
- Peng Cheng Laboratory, Shenzhen, Guangdong, China
| | - Haiyan Lv
- Mindsgo Life Science Shenzhen Ltd, Shenzhen, Guangdong, China
| | - Tong Wang
- School of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, Guangdong, China
| | - Ting Ma
- School of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, Guangdong, China; Peng Cheng Laboratory, Shenzhen, Guangdong, China; National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
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75
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Zhou T, Fan DP, Cheng MM, Shen J, Shao L. RGB-D salient object detection: A survey. COMPUTATIONAL VISUAL MEDIA 2021; 7:37-69. [PMID: 33432275 PMCID: PMC7788385 DOI: 10.1007/s41095-020-0199-z] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 10/07/2020] [Indexed: 06/12/2023]
Abstract
Salient object detection, which simulates human visual perception in locating the most significant object(s) in a scene, has been widely applied to various computer vision tasks. Now, the advent of depth sensors means that depth maps can easily be captured; this additional spatial information can boost the performance of salient object detection. Although various RGB-D based salient object detection models with promising performance have been proposed over the past several years, an in-depth understanding of these models and the challenges in this field remains lacking. In this paper, we provide a comprehensive survey of RGB-D based salient object detection models from various perspectives, and review related benchmark datasets in detail. Further, as light fields can also provide depth maps, we review salient object detection models and popular benchmark datasets from this domain too. Moreover, to investigate the ability of existing models to detect salient objects, we have carried out a comprehensive attribute-based evaluation of several representative RGB-D based salient object detection models. Finally, we discuss several challenges and open directions of RGB-D based salient object detection for future research. All collected models, benchmark datasets, datasets constructed for attribute-based evaluation, and related code are publicly available at https://github.com/taozh2017/RGBD-SODsurvey.
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Affiliation(s)
- Tao Zhou
- Inception Institute of Artificial Intelligence (IIAI), Abu Dhabi, United Arab Emirates
| | - Deng-Ping Fan
- Inception Institute of Artificial Intelligence (IIAI), Abu Dhabi, United Arab Emirates
| | | | - Jianbing Shen
- Inception Institute of Artificial Intelligence (IIAI), Abu Dhabi, United Arab Emirates
| | - Ling Shao
- Inception Institute of Artificial Intelligence (IIAI), Abu Dhabi, United Arab Emirates
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76
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Pan X, Phan TL, Adel M, Fossati C, Gaidon T, Wojak J, Guedj E. Multi-View Separable Pyramid Network for AD Prediction at MCI Stage by 18F-FDG Brain PET Imaging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:81-92. [PMID: 32894711 DOI: 10.1109/tmi.2020.3022591] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Alzheimer's Disease (AD), one of the main causes of death in elderly people, is characterized by Mild Cognitive Impairment (MCI) at prodromal stage. Nevertheless, only part of MCI subjects could progress to AD. The main objective of this paper is thus to identify those who will develop a dementia of AD type among MCI patients. 18F-FluoroDeoxyGlucose Positron Emission Tomography (18F-FDG PET) serves as a neuroimaging modality for early diagnosis as it can reflect neural activity via measuring glucose uptake at resting-state. In this paper, we design a deep network on 18F-FDG PET modality to address the problem of AD identification at early MCI stage. To this end, a Multi-view Separable Pyramid Network (MiSePyNet) is proposed, in which representations are learned from axial, coronal and sagittal views of PET scans so as to offer complementary information and then combined to make a decision jointly. Different from the widely and naturally used 3D convolution operations for 3D images, the proposed architecture is deployed with separable convolution from slice-wise to spatial-wise successively, which can retain the spatial information and reduce training parameters compared to 2D and 3D networks, respectively. Experiments on ADNI dataset show that the proposed method can yield better performance than both traditional and deep learning-based algorithms for predicting the progression of Mild Cognitive Impairment, with a classification accuracy of 83.05%.
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77
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Almubark I, Chang LC, Shattuck KF, Nguyen T, Turner RS, Jiang X. A 5-min Cognitive Task With Deep Learning Accurately Detects Early Alzheimer's Disease. Front Aging Neurosci 2020; 12:603179. [PMID: 33343337 PMCID: PMC7744695 DOI: 10.3389/fnagi.2020.603179] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2020] [Accepted: 11/13/2020] [Indexed: 12/15/2022] Open
Abstract
Introduction: The goal of this study was to investigate and compare the classification performance of machine learning with behavioral data from standard neuropsychological tests, a cognitive task, or both. Methods: A neuropsychological battery and a simple 5-min cognitive task were administered to eight individuals with mild cognitive impairment (MCI), eight individuals with mild Alzheimer's disease (AD), and 41 demographically match controls (CN). A fully connected multilayer perceptron (MLP) network and four supervised traditional machine learning algorithms were used. Results: Traditional machine learning algorithms achieved similar classification performances with neuropsychological or cognitive data. MLP outperformed traditional algorithms with the cognitive data (either alone or together with neuropsychological data), but not neuropsychological data. In particularly, MLP with a combination of summarized scores from neuropsychological tests and the cognitive task achieved ~90% sensitivity and ~90% specificity. Applying the models to an independent dataset, in which the participants were demographically different from the ones in the main dataset, a high specificity was maintained (100%), but the sensitivity was dropped to 66.67%. Discussion: Deep learning with data from specific cognitive task(s) holds promise for assisting in the early diagnosis of Alzheimer's disease, but future work with a large and diverse sample is necessary to validate and to improve this approach.
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Affiliation(s)
- Ibrahim Almubark
- Department of Electrical Engineering and Computer Science, Catholic University of America, Washington, DC, United States
| | - Lin-Ching Chang
- Department of Electrical Engineering and Computer Science, Catholic University of America, Washington, DC, United States
| | - Kyle F Shattuck
- Department of Neuroscience, Georgetown University Medical Center, Washington, DC, United States
| | - Thanh Nguyen
- Department of Electrical Engineering and Computer Science, Catholic University of America, Washington, DC, United States
| | - Raymond Scott Turner
- Department of Neurology, Georgetown University Medical Center, Washington, DC, United States
| | - Xiong Jiang
- Department of Neuroscience, Georgetown University Medical Center, Washington, DC, United States
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78
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Zhang D, Yang G, Zhao S, Zhang Y, Ghista D, Zhang H, Li S. Direct Quantification of Coronary Artery Stenosis Through Hierarchical Attentive Multi-View Learning. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:4322-4334. [PMID: 32804646 DOI: 10.1109/tmi.2020.3017275] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Quantification of coronary artery stenosis on X-ray angiography (XRA) images is of great importance during the intraoperative treatment of coronary artery disease. It serves to quantify the coronary artery stenosis by estimating the clinical morphological indices, which are essential in clinical decision making. However, stenosis quantification is still a challenging task due to the overlapping, diversity and small-size region of the stenosis in the XRA images. While efforts have been devoted to stenosis quantification through low-level features, these methods have difficulty in learning the real mapping from these features to the stenosis indices. These methods are still cumbersome and unreliable for the intraoperative procedures due to their two-phase quantification, which depends on the results of segmentation or reconstruction of the coronary artery. In this work, we are proposing a hierarchical attentive multi-view learning model (HEAL) to achieve a direct quantification of coronary artery stenosis, without the intermediate segmentation or reconstruction. We have designed a multi-view learning model to learn more complementary information of the stenosis from different views. For this purpose, an intra-view hierarchical attentive block is proposed to learn the discriminative information of stenosis. Additionally, a stenosis representation learning module is developed to extract the multi-scale features from the keyframe perspective for considering the clinical workflow. Finally, the morphological indices are directly estimated based on the multi-view feature embedding. Extensive experiment studies on clinical multi-manufacturer dataset consisting of 228 subjects show the superiority of our HEAL against nine comparing methods, including direct quantification methods and multi-view learning methods. The experimental results demonstrate the better clinical agreement between the ground truth and the prediction, which endows our proposed method with a great potential for the efficient intraoperative treatment of coronary artery disease.
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79
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Low Frequency Vibration Visual Monitoring System Based on Multi-Modal 3DCNN-ConvLSTM. SENSORS 2020; 20:s20205872. [PMID: 33080814 PMCID: PMC7589111 DOI: 10.3390/s20205872] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 10/13/2020] [Accepted: 10/15/2020] [Indexed: 12/04/2022]
Abstract
Low frequency vibration monitoring has significant implications on environmental safety and engineering practices. Vibration expressed by visual information should contain sufficient spatial information. RGB-D camera could record diverse spatial information of vibration in frame images. Deep learning can adaptively transform frame images into deep abstract features through nonlinear mapping, which is an effective method to improve the intelligence of vibration monitoring. In this paper, a multi-modal low frequency visual vibration monitoring system based on Kinect v2 and 3DCNN-ConvLSTM is proposed. Microsoft Kinect v2 collects RGB and depth video information of vibrating objects in unstable ambient light. The 3DCNN-ConvLSTM architecture can effectively learn the spatial-temporal characteristics of muti-frequency vibration. The short-term spatiotemporal feature of the collected vibration information is learned through 3D convolution networks and the long-term spatiotemporal feature is learned through convolutional LSTM. Multi-modal fusion of RGB and depth mode is used to further improve the monitoring accuracy to 93% in the low frequency vibration range of 0–10 Hz. The results show that the system can monitor low frequency vibration and meet the basic measurement requirements.
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80
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Segato A, Marzullo A, Calimeri F, De Momi E. Artificial intelligence for brain diseases: A systematic review. APL Bioeng 2020; 4:041503. [PMID: 33094213 PMCID: PMC7556883 DOI: 10.1063/5.0011697] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 09/09/2020] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence (AI) is a major branch of computer science that is fruitfully used for analyzing complex medical data and extracting meaningful relationships in datasets, for several clinical aims. Specifically, in the brain care domain, several innovative approaches have achieved remarkable results and open new perspectives in terms of diagnosis, planning, and outcome prediction. In this work, we present an overview of different artificial intelligent techniques used in the brain care domain, along with a review of important clinical applications. A systematic and careful literature search in major databases such as Pubmed, Scopus, and Web of Science was carried out using "artificial intelligence" and "brain" as main keywords. Further references were integrated by cross-referencing from key articles. 155 studies out of 2696 were identified, which actually made use of AI algorithms for different purposes (diagnosis, surgical treatment, intra-operative assistance, and postoperative assessment). Artificial neural networks have risen to prominent positions among the most widely used analytical tools. Classic machine learning approaches such as support vector machine and random forest are still widely used. Task-specific algorithms are designed for solving specific problems. Brain images are one of the most used data types. AI has the possibility to improve clinicians' decision-making ability in neuroscience applications. However, major issues still need to be addressed for a better practical use of AI in the brain. To this aim, it is important to both gather comprehensive data and build explainable AI algorithms.
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Affiliation(s)
- Alice Segato
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan 20133, Italy
| | - Aldo Marzullo
- Department of Mathematics and Computer Science, University of Calabria, Rende 87036, Italy
| | - Francesco Calimeri
- Department of Mathematics and Computer Science, University of Calabria, Rende 87036, Italy
| | - Elena De Momi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan 20133, Italy
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81
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Thurnhofer-Hemsi K, Domínguez E. A Convolutional Neural Network Framework for Accurate Skin Cancer Detection. Neural Process Lett 2020. [DOI: 10.1007/s11063-020-10364-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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82
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Venkatesh V, Sharma N, Singh M. Intensity inhomogeneity correction of MRI images using InhomoNet. Comput Med Imaging Graph 2020; 84:101748. [DOI: 10.1016/j.compmedimag.2020.101748] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2020] [Revised: 04/28/2020] [Accepted: 06/05/2020] [Indexed: 10/24/2022]
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83
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Zhou T, Fu H, Chen G, Shen J, Shao L. Hi-Net: Hybrid-Fusion Network for Multi-Modal MR Image Synthesis. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:2772-2781. [PMID: 32086202 DOI: 10.1109/tmi.2020.2975344] [Citation(s) in RCA: 95] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Magnetic resonance imaging (MRI) is a widely used neuroimaging technique that can provide images of different contrasts (i.e., modalities). Fusing this multi-modal data has proven particularly effective for boosting model performance in many tasks. However, due to poor data quality and frequent patient dropout, collecting all modalities for every patient remains a challenge. Medical image synthesis has been proposed as an effective solution, where any missing modalities are synthesized from the existing ones. In this paper, we propose a novel Hybrid-fusion Network (Hi-Net) for multi-modal MR image synthesis, which learns a mapping from multi-modal source images (i.e., existing modalities) to target images (i.e., missing modalities). In our Hi-Net, a modality-specific network is utilized to learn representations for each individual modality, and a fusion network is employed to learn the common latent representation of multi-modal data. Then, a multi-modal synthesis network is designed to densely combine the latent representation with hierarchical features from each modality, acting as a generator to synthesize the target images. Moreover, a layer-wise multi-modal fusion strategy effectively exploits the correlations among multiple modalities, where a Mixed Fusion Block (MFB) is proposed to adaptively weight different fusion strategies. Extensive experiments demonstrate the proposed model outperforms other state-of-the-art medical image synthesis methods.
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84
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Zhou T, Zhang C, Peng X, Bhaskar H, Yang J. Dual Shared-Specific Multiview Subspace Clustering. IEEE TRANSACTIONS ON CYBERNETICS 2020; 50:3517-3530. [PMID: 31226094 DOI: 10.1109/tcyb.2019.2918495] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Multiview subspace clustering has received significant attention as the availability of diverse of multidomain and multiview real-world data has rapidly increased in the recent years. Boosting the performance of multiview clustering algorithms is challenged by two major factors. First, since original features from multiview data are highly redundant, reconstruction based on these attributes inevitably results in inferior performance. Second, since each view of such multiview data may contain unique knowledge as against the others, it remains a challenge to exploit complimentary information across multiple views while simultaneously investigating the uniqueness of each view. In this paper, we present a novel dual shared-specific multiview subspace clustering (DSS-MSC) approach that simultaneously learns the correlations between shared information across multiple views and also utilizes view-specific information to depict specific property for each independent view. Further, we formulate a dual learning framework to capture shared-specific information into the dimensional reduction and self-representation processes, which strengthens the ability of our approach to exploit shared information while preserving view-specific property effectively. The experimental results on several benchmark datasets have demonstrated the effectiveness of the proposed approach against other state-of-the-art techniques.
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85
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Thomas SA, Titus G. Design of a portable retinal imaging module with automatic abnormality detection. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.101962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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86
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Duffy IR, Boyle AJ, Vasdev N. Improving PET Imaging Acquisition and Analysis With Machine Learning: A Narrative Review With Focus on Alzheimer's Disease and Oncology. Mol Imaging 2020; 18:1536012119869070. [PMID: 31429375 PMCID: PMC6702769 DOI: 10.1177/1536012119869070] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Machine learning (ML) algorithms have found increasing utility in the medical imaging field and numerous applications in the analysis of digital biomarkers within positron emission tomography (PET) imaging have emerged. Interest in the use of artificial intelligence in PET imaging for the study of neurodegenerative diseases and oncology stems from the potential for such techniques to streamline decision support for physicians providing early and accurate diagnosis and allowing personalized treatment regimens. In this review, the use of ML to improve PET image acquisition and reconstruction is presented, along with an overview of its applications in the analysis of PET images for the study of Alzheimer's disease and oncology.
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Affiliation(s)
- Ian R Duffy
- 1 Azrieli Centre for Neuro-Radiochemistry, Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Amanda J Boyle
- 1 Azrieli Centre for Neuro-Radiochemistry, Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Neil Vasdev
- 1 Azrieli Centre for Neuro-Radiochemistry, Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.,2 Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
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87
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Feng W, Halm-Lutterodt NV, Tang H, Mecum A, Mesregah MK, Ma Y, Li H, Zhang F, Wu Z, Yao E, Guo X. Automated MRI-Based Deep Learning Model for Detection of Alzheimer’s Disease Process. Int J Neural Syst 2020; 30:2050032. [DOI: 10.1142/s012906572050032x] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
In the context of neuro-pathological disorders, neuroimaging has been widely accepted as a clinical tool for diagnosing patients with Alzheimer’s disease (AD) and mild cognitive impairment (MCI). The advanced deep learning method, a novel brain imaging technique, was applied in this study to evaluate its contribution to improving the diagnostic accuracy of AD. Three-dimensional convolutional neural networks (3D-CNNs) were applied with magnetic resonance imaging (MRI) to execute binary and ternary disease classification models. The dataset from the Alzheimer’s disease neuroimaging initiative (ADNI) was used to compare the deep learning performances across 3D-CNN, 3D-CNN-support vector machine (SVM) and two-dimensional (2D)-CNN models. The outcomes of accuracy with ternary classification for 2D-CNN, 3D-CNN and 3D-CNN-SVM were [Formula: see text]%, [Formula: see text]% and [Formula: see text]% respectively. The 3D-CNN-SVM yielded a ternary classification accuracy of 93.71%, 96.82% and 96.73% for NC, MCI and AD diagnoses, respectively. Furthermore, 3D-CNN-SVM showed the best performance for binary classification. Our study indicated that ‘NC versus MCI’ showed accuracy, sensitivity and specificity of 98.90%, 98.90% and 98.80%; ‘NC versus AD’ showed accuracy, sensitivity and specificity of 99.10%, 99.80% and 98.40%; and ‘MCI versus AD’ showed accuracy, sensitivity and specificity of 89.40%, 86.70% and 84.00%, respectively. This study clearly demonstrates that 3D-CNN-SVM yields better performance with MRI compared to currently utilized deep learning methods. In addition, 3D-CNN-SVM proved to be efficient without having to manually perform any prior feature extraction and is totally independent of the variability of imaging protocols and scanners. This suggests that it can potentially be exploited by untrained operators and extended to virtual patient imaging data. Furthermore, owing to the safety, noninvasiveness and nonirradiative properties of the MRI modality, 3D-CNN-SMV may serve as an effective screening option for AD in the general population. This study holds value in distinguishing AD and MCI subjects from normal controls and to improve value-based care of patients in clinical practice.
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Affiliation(s)
- Wei Feng
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, You’anmenwai, Xitoutiao No.10, Beijing, P. R. China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, P. R. China
| | - Nicholas Van Halm-Lutterodt
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, P. R. China
- Department of Orthopaedics and Neurosurgery, Keck Medical Center of USC, Los Angeles, CA, USA
| | - Hao Tang
- School of Computer Science and Technology, University of the Chinese Academy of Sciences, Beijing, P. R. China
| | - Andrew Mecum
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Mohamed Kamal Mesregah
- Department of Orthopaedics and Neurosurgery, Keck Medical Center of USC, Los Angeles, CA, USA
| | - Yuan Ma
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, You’anmenwai, Xitoutiao No.10, Beijing, P. R. China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, P. R. China
| | - Haibin Li
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, You’anmenwai, Xitoutiao No.10, Beijing, P. R. China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, P. R. China
| | - Feng Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, You’anmenwai, Xitoutiao No.10, Beijing, P. R. China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, P. R. China
| | - Zhiyuan Wu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, You’anmenwai, Xitoutiao No.10, Beijing, P. R. China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, P. R. China
| | - Erlin Yao
- School of Computer Science and Technology, University of the Chinese Academy of Sciences, Beijing, P. R. China
| | - Xiuhua Guo
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, You’anmenwai, Xitoutiao No.10, Beijing, P. R. China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, P. R. China
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88
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Zhang T, Shi M. Multi-modal neuroimaging feature fusion for diagnosis of Alzheimer's disease. J Neurosci Methods 2020; 341:108795. [PMID: 32446943 DOI: 10.1016/j.jneumeth.2020.108795] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 05/19/2020] [Accepted: 05/19/2020] [Indexed: 11/24/2022]
Abstract
BACKGROUND Compared with single-modal neuroimages classification of AD, multi-modal classification can achieve better performance by fusing different information. Exploring synergy among various multi-modal neuroimages is contributed to identifying the pathological process of neurological disorders. However, it is still problematic to effectively exploit multi-modal information since the lack of an effective fusion method. NEW METHOD In this paper, we propose a deep multi-modal fusion network based on the attention mechanism, which can selectively extract features from MRI and PET branches and suppress irrelevant information. In the attention model, the fusion ratio of each modality is assigned automatically according to the importance of the data. A hierarchical fusion method is adopted to ensure the effectiveness of Multi-modal Fusion. RESULTS Evaluating the model on the ADNI dataset, the experimental results show that it outperforms the state-of-the-art methods. In particular, the final classification results of the NC/AD, SMCI/PMCI and Four-Class are 95.21 %, 89.79 %, and 86.15 %, respectively. COMPARISON WITH EXISTING METHODS Different from the early fusion and the late fusion, the hierarchical fusion method contributes to learning the synergy between the multi-modal data. Compared with some other prominent algorithms, the attention model enables our network to focus on the regions of interest and effectively fuse the multi-modal data. CONCLUSION Benefit from the hierarchical structure with attention model, the proposed network is capable of exploiting low-level and high-level features extracted from the multi-modal data and improving the accuracy of AD diagnosis. Results show its promising performance.
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Affiliation(s)
- Tao Zhang
- School of Electronic and Information Engineering, Tianjin University, 300387, Tianjin, China
| | - Mingyang Shi
- School of Electronic and Information Engineering, Tianjin University, 300387, Tianjin, China.
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89
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Shankar K, Sait ARW, Gupta D, Lakshmanaprabu S, Khanna A, Pandey HM. Automated detection and classification of fundus diabetic retinopathy images using synergic deep learning model. Pattern Recognit Lett 2020. [DOI: 10.1016/j.patrec.2020.02.026] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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90
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Wen J, Thibeau-Sutre E, Diaz-Melo M, Samper-González J, Routier A, Bottani S, Dormont D, Durrleman S, Burgos N, Colliot O. Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation. Med Image Anal 2020; 63:101694. [PMID: 32417716 DOI: 10.1016/j.media.2020.101694] [Citation(s) in RCA: 204] [Impact Index Per Article: 51.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 03/23/2020] [Accepted: 03/27/2020] [Indexed: 10/24/2022]
Abstract
Numerous machine learning (ML) approaches have been proposed for automatic classification of Alzheimer's disease (AD) from brain imaging data. In particular, over 30 papers have proposed to use convolutional neural networks (CNN) for AD classification from anatomical MRI. However, the classification performance is difficult to compare across studies due to variations in components such as participant selection, image preprocessing or validation procedure. Moreover, these studies are hardly reproducible because their frameworks are not publicly accessible and because implementation details are lacking. Lastly, some of these papers may report a biased performance due to inadequate or unclear validation or model selection procedures. In the present work, we aim to address these limitations through three main contributions. First, we performed a systematic literature review. We identified four main types of approaches: i) 2D slice-level, ii) 3D patch-level, iii) ROI-based and iv) 3D subject-level CNN. Moreover, we found that more than half of the surveyed papers may have suffered from data leakage and thus reported biased performance. Our second contribution is the extension of our open-source framework for classification of AD using CNN and T1-weighted MRI. The framework comprises previously developed tools to automatically convert ADNI, AIBL and OASIS data into the BIDS standard, and a modular set of image preprocessing procedures, classification architectures and evaluation procedures dedicated to deep learning. Finally, we used this framework to rigorously compare different CNN architectures. The data was split into training/validation/test sets at the very beginning and only the training/validation sets were used for model selection. To avoid any overfitting, the test sets were left untouched until the end of the peer-review process. Overall, the different 3D approaches (3D-subject, 3D-ROI, 3D-patch) achieved similar performances while that of the 2D slice approach was lower. Of note, the different CNN approaches did not perform better than a SVM with voxel-based features. The different approaches generalized well to similar populations but not to datasets with different inclusion criteria or demographical characteristics. All the code of the framework and the experiments is publicly available: general-purpose tools have been integrated into the Clinica software (www.clinica.run) and the paper-specific code is available at: https://github.com/aramis-lab/AD-DL.
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Affiliation(s)
- Junhao Wen
- Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France; Inria, Aramis project-team, Paris F-75013, France
| | - Elina Thibeau-Sutre
- Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France; Inria, Aramis project-team, Paris F-75013, France
| | - Mauricio Diaz-Melo
- Inria, Aramis project-team, Paris F-75013, France; Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France
| | - Jorge Samper-González
- Inria, Aramis project-team, Paris F-75013, France; Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France
| | - Alexandre Routier
- Inria, Aramis project-team, Paris F-75013, France; Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France
| | - Simona Bottani
- Inria, Aramis project-team, Paris F-75013, France; Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France
| | - Didier Dormont
- Inria, Aramis project-team, Paris F-75013, France; Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France; Department of Neuroradiology, AP-HP, Hôpital de la PitiéSalpêtrière, Paris F-75013, France
| | - Stanley Durrleman
- Inria, Aramis project-team, Paris F-75013, France; Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France
| | - Ninon Burgos
- Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France; Inria, Aramis project-team, Paris F-75013, France
| | - Olivier Colliot
- Institut du Cerveau et de la Moelleépinière, ICM, Paris F-75013, France; SorbonneUniversité, ParisF-75013,France; Inserm, U 1127, Paris F-75013, France; CNRS, UMR 7225, Paris F-75013, France; Inria, Aramis project-team, Paris F-75013, France; Department of Neuroradiology, AP-HP, Hôpital de la PitiéSalpêtrière, Paris F-75013, France; Department of Neurology, AP-HP, Hôpital de la PitiéSalpêtrière, Paris F-75013, France.
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Xu Q, Zeng Y, Tang W, Peng W, Xia T, Li Z, Teng F, Li W, Guo J. Multi-Task Joint Learning Model for Segmenting and Classifying Tongue Images Using a Deep Neural Network. IEEE J Biomed Health Inform 2020; 24:2481-2489. [PMID: 32310809 DOI: 10.1109/jbhi.2020.2986376] [Citation(s) in RCA: 92] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Automatic tongue image segmentation and tongue image classification are two crucial tongue characterization tasks in traditional Chinese medicine (TCM). Due to the complexity of tongue segmentation and fine-grained traits of tongue image classification, both tasks are challenging. Fortunately, from the perspective of computer vision, these two tasks are highly interrelated, making them compatible with the idea of Multi-Task Joint learning (MTL). By sharing the underlying parameters and adding two different task loss functions, an MTL method for segmenting and classifying tongue images is proposed in this paper. Moreover, two state-of-the-art deep neural network variants (UNET and Discriminative Filter Learning (DFL)) are fused into the MTL to perform these two tasks. To the best of our knowledge, our method is the first attempt to manage both tasks simultaneously with MTL. We conducted extensive experiments with the proposed method. The experimental results show that our joint method outperforms the existing tongue characterization methods. Besides, visualizations and ablation studies are provided to aid in understanding our approach, which suggest that our method is highly consistent with human perception.
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92
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Research of Low-Rank Representation and Discriminant Correlation Analysis for Alzheimer's Disease Diagnosis. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2020; 2020:5294840. [PMID: 32256681 PMCID: PMC7106873 DOI: 10.1155/2020/5294840] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2020] [Accepted: 02/21/2020] [Indexed: 11/22/2022]
Abstract
As population aging is becoming more common worldwide, applying artificial intelligence into the diagnosis of Alzheimer's disease (AD) is critical to improve the diagnostic level in recent years. In early diagnosis of AD, the fusion of complementary information contained in multimodality data (e.g., magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF)) has obtained enormous achievement. Detecting Alzheimer's disease using multimodality data has two difficulties: (1) there exists noise information in multimodal data; (2) how to establish an effective mathematical model of the relationship between multimodal data? To this end, we proposed a method named LDF which is based on the combination of low-rank representation and discriminant correlation analysis (DCA) to fuse multimodal datasets. Specifically, the low-rank representation method is used to extract the latent features of the submodal data, so the noise information in the submodal data is removed. Then, discriminant correlation analysis is used to fuse the submodal data, so the complementary information can be fully utilized. The experimental results indicate the effectiveness of this method.
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93
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Ebrahimighahnavieh MA, Luo S, Chiong R. Deep learning to detect Alzheimer's disease from neuroimaging: A systematic literature review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 187:105242. [PMID: 31837630 DOI: 10.1016/j.cmpb.2019.105242] [Citation(s) in RCA: 90] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 11/13/2019] [Accepted: 11/25/2019] [Indexed: 06/10/2023]
Abstract
Alzheimer's Disease (AD) is one of the leading causes of death in developed countries. From a research point of view, impressive results have been reported using computer-aided algorithms, but clinically no practical diagnostic method is available. In recent years, deep models have become popular, especially in dealing with images. Since 2013, deep learning has begun to gain considerable attention in AD detection research, with the number of published papers in this area increasing drastically since 2017. Deep models have been reported to be more accurate for AD detection compared to general machine learning techniques. Nevertheless, AD detection is still challenging, and for classification, it requires a highly discriminative feature representation to separate similar brain patterns. This paper reviews the current state of AD detection using deep learning. Through a systematic literature review of over 100 articles, we set out the most recent findings and trends. Specifically, we review useful biomarkers and features (personal information, genetic data, and brain scans), the necessary pre-processing steps, and different ways of dealing with neuroimaging data originating from single-modality and multi-modality studies. Deep models and their performance are described in detail. Although deep learning has achieved notable performance in detecting AD, there are several limitations, especially regarding the availability of datasets and training procedures.
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Affiliation(s)
| | - Suhuai Luo
- The University of Newcastle, University Drive, Callaghan 2308, Australia
| | - Raymond Chiong
- The University of Newcastle, University Drive, Callaghan 2308, Australia.
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94
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Self-calibrated brain network estimation and joint non-convex multi-task learning for identification of early Alzheimer's disease. Med Image Anal 2020; 61:101652. [DOI: 10.1016/j.media.2020.101652] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Revised: 01/16/2020] [Accepted: 01/16/2020] [Indexed: 12/17/2022]
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95
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Abstract
The molecular mechanisms and functions in complex biological systems currently remain elusive. Recent high-throughput techniques, such as next-generation sequencing, have generated a wide variety of multiomics datasets that enable the identification of biological functions and mechanisms via multiple facets. However, integrating these large-scale multiomics data and discovering functional insights are, nevertheless, challenging tasks. To address these challenges, machine learning has been broadly applied to analyze multiomics. This review introduces multiview learning-an emerging machine learning field-and envisions its potentially powerful applications to multiomics. In particular, multiview learning is more effective than previous integrative methods for learning data's heterogeneity and revealing cross-talk patterns. Although it has been applied to various contexts, such as computer vision and speech recognition, multiview learning has not yet been widely applied to biological data-specifically, multiomics data. Therefore, this paper firstly reviews recent multiview learning methods and unifies them in a framework called multiview empirical risk minimization (MV-ERM). We further discuss the potential applications of each method to multiomics, including genomics, transcriptomics, and epigenomics, in an aim to discover the functional and mechanistic interpretations across omics. Secondly, we explore possible applications to different biological systems, including human diseases (e.g., brain disorders and cancers), plants, and single-cell analysis, and discuss both the benefits and caveats of using multiview learning to discover the molecular mechanisms and functions of these systems.
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Affiliation(s)
- Nam D. Nguyen
- Department of Computer Science, Stony Brook University, Stony Brook, New York, United States of America
| | - Daifeng Wang
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
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96
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Zhou T, Thung KH, Liu M, Shi F, Zhang C, Shen D. Multi-modal latent space inducing ensemble SVM classifier for early dementia diagnosis with neuroimaging data. Med Image Anal 2020; 60:101630. [PMID: 31927474 PMCID: PMC8260095 DOI: 10.1016/j.media.2019.101630] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Revised: 12/15/2019] [Accepted: 12/19/2019] [Indexed: 12/21/2022]
Abstract
Fusing multi-modality data is crucial for accurate identification of brain disorder as different modalities can provide complementary perspectives of complex neurodegenerative disease. However, there are at least four common issues associated with the existing fusion methods. First, many existing fusion methods simply concatenate features from each modality without considering the correlations among different modalities. Second, most existing methods often make prediction based on a single classifier, which might not be able to address the heterogeneity of the Alzheimer's disease (AD) progression. Third, many existing methods often employ feature selection (or reduction) and classifier training in two independent steps, without considering the fact that the two pipelined steps are highly related to each other. Forth, there are missing neuroimaging data for some of the participants (e.g., missing PET data), due to the participants' "no-show" or dropout. In this paper, to address the above issues, we propose an early AD diagnosis framework via novel multi-modality latent space inducing ensemble SVM classifier. Specifically, we first project the neuroimaging data from different modalities into a latent space, and then map the learned latent representations into the label space to learn multiple diversified classifiers. Finally, we obtain the more reliable classification results by using an ensemble strategy. More importantly, we present a Complete Multi-modality Latent Space (CMLS) learning model for complete multi-modality data and also an Incomplete Multi-modality Latent Space (IMLS) learning model for incomplete multi-modality data. Extensive experiments using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset have demonstrated that our proposed models outperform other state-of-the-art methods.
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Affiliation(s)
- Tao Zhou
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, NC 27599, USA; Inception Institute of Artificial Intelligence, Abu Dhabi 51133, United Arab Emirates.
| | - Kim-Han Thung
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, NC 27599, USA.
| | - Mingxia Liu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, NC 27599, USA.
| | - Feng Shi
- United Imaging Intelligence, Shanghai, China.
| | - Changqing Zhang
- School of Computer Science and Technology, Tianjin University, Tianjin 300072, China.
| | - Dinggang Shen
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, NC 27599, USA; Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea.
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97
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A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10020559] [Citation(s) in RCA: 174] [Impact Index Per Article: 43.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Pneumonia is among the top diseases which cause most of the deaths all over the world. Virus, bacteria and fungi can all cause pneumonia. However, it is difficult to judge the pneumonia just by looking at chest X-rays. The aim of this study is to simplify the pneumonia detection process for experts as well as for novices. We suggest a novel deep learning framework for the detection of pneumonia using the concept of transfer learning. In this approach, features from images are extracted using different neural network models pretrained on ImageNet, which then are fed into a classifier for prediction. We prepared five different models and analyzed their performance. Thereafter, we proposed an ensemble model that combines outputs from all pretrained models, which outperformed individual models, reaching the state-of-the-art performance in pneumonia recognition. Our ensemble model reached an accuracy of 96.4% with a recall of 99.62% on unseen data from the Guangzhou Women and Children’s Medical Center dataset.
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98
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Adaptive sparse learning using multi-template for neurodegenerative disease diagnosis. Med Image Anal 2020; 61:101632. [PMID: 32028212 DOI: 10.1016/j.media.2019.101632] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Revised: 12/17/2019] [Accepted: 12/20/2019] [Indexed: 12/20/2022]
Abstract
Neurodegenerative diseases are excessively affecting millions of patients, especially elderly people. Early detection and management of these diseases are crucial as the clinical symptoms take years to appear after the onset of neuro-degeneration. This paper proposes an adaptive feature learning framework using multiple templates for early diagnosis. A multi-classification scheme is developed based on multiple brain parcellation atlases with various regions of interest. Different sets of features are extracted and then fused, and a feature selection is applied with an adaptively chosen sparse degree. In addition, both linear discriminative analysis and locally preserving projections are integrated to construct a least square regression model. Finally, we propose a feature space to predict the severity of the disease by the guidance of clinical scores. Our proposed method is validated on both Alzheimer's disease neuroimaging initiative and Parkinson's progression markers initiative databases. Extensive experimental results suggest that the proposed method outperforms the state-of-the-art methods, such as the multi-modal multi-task learning or joint sparse learning. Our method demonstrates that accurate feature learning facilitates the identification of the highly relevant brain regions with significant contribution in the prediction of disease progression. This may pave the way for further medical analysis and diagnosis in practical applications.
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99
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100
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Shen L, Thompson PM. Brain Imaging Genomics: Integrated Analysis and Machine Learning. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2020; 108:125-162. [PMID: 31902950 PMCID: PMC6941751 DOI: 10.1109/jproc.2019.2947272] [Citation(s) in RCA: 76] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Brain imaging genomics is an emerging data science field, where integrated analysis of brain imaging and genomics data, often combined with other biomarker, clinical and environmental data, is performed to gain new insights into the phenotypic, genetic and molecular characteristics of the brain as well as their impact on normal and disordered brain function and behavior. It has enormous potential to contribute significantly to biomedical discoveries in brain science. Given the increasingly important role of statistical and machine learning in biomedicine and rapidly growing literature in brain imaging genomics, we provide an up-to-date and comprehensive review of statistical and machine learning methods for brain imaging genomics, as well as a practical discussion on method selection for various biomedical applications.
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Affiliation(s)
- Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA 90232, USA
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