1
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Integrating convolutional neural networks, kNN, and Bayesian optimization for efficient diagnosis of Alzheimer's disease in magnetic resonance images. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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2
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Nazari E, Biviji R, Roshandel D, Pour R, Shahriari MH, Mehrabian A, Tabesh H. Decision fusion in healthcare and medicine: a narrative review. Mhealth 2022; 8:8. [PMID: 35178439 PMCID: PMC8800206 DOI: 10.21037/mhealth-21-15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 08/02/2021] [Indexed: 11/06/2022] Open
Abstract
OBJECTIVE To provide an overview of the decision fusion (DF) technique and describe the applications of the technique in healthcare and medicine at prevention, diagnosis, treatment and administrative levels. BACKGROUND The rapid development of technology over the past 20 years has led to an explosion in data growth in various industries, like healthcare. Big data analysis within the healthcare systems is essential for arriving to a value-based decision over a period of time. Diversity and uncertainty in big data analytics have made it impossible to analyze data by using conventional data mining techniques and thus alternative solutions are required. DF is a form of data fusion techniques that could increase the accuracy of diagnosis and facilitate interpretation, summarization and sharing of information. METHODS We conducted a review of articles published between January 1980 and December 2020 from various databases such as Google Scholar, IEEE, PubMed, Science Direct, Scopus and web of science using the keywords decision fusion (DF), information fusion, healthcare, medicine and big data. A total of 141 articles were included in this narrative review. CONCLUSIONS Given the importance of big data analysis in reducing costs and improving the quality of healthcare; along with the potential role of DF in big data analysis, it is recommended to know the full potential of this technique including the advantages, challenges and applications of the technique before its use. Future studies should focus on describing the methodology and types of data used for its applications within the healthcare sector.
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Affiliation(s)
- Elham Nazari
- Department of Medical Informatics, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Rizwana Biviji
- Science of Healthcare Delivery, College of Health Solutions, Arizona State University, Phoenix, AZ, USA
| | - Danial Roshandel
- Centre for Ophthalmology and Visual Science (affiliated with the Lions Eye Institute), The University of Western Australia, Perth, Western Australia, Australia
| | - Reza Pour
- Department of Computer Engineering, Azad University, Mashhad, Iran
| | - Mohammad Hasan Shahriari
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Amin Mehrabian
- Warwick Medical School, University of Warwick, Coventry, UK
| | - Hamed Tabesh
- Department of Medical Informatics, Mashhad University of Medical Sciences, Mashhad, Iran
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3
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Yu Q, Cai Z, Li C, Xiong Y, Yang Y, He S, Tang H, Zhang B, Du S, Yan H, Chang C, Wang N. A Novel Spectrum Contrast Mapping Method for Functional Magnetic Resonance Imaging Data Analysis. Front Hum Neurosci 2021; 15:739668. [PMID: 34566609 PMCID: PMC8455948 DOI: 10.3389/fnhum.2021.739668] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Accepted: 08/18/2021] [Indexed: 12/18/2022] Open
Abstract
Many studies reported that spontaneous fluctuation of the blood oxygen level-dependent signal exists in multiple frequency components and changes over time. By assuming a reliable energy contrast between low- and high-frequency bands for each voxel, we developed a novel spectrum contrast mapping (SCM) method to decode brain activity at the voxel-wise level and further validated it in designed experiments. SCM consists of the following steps: first, the time course of each given voxel is subjected to fast Fourier transformation; the corresponding spectrum is divided into low- and high-frequency bands by given reference frequency points; then, the spectral energy ratio of the low- to high-frequency bands is calculated for each given voxel. Finally, the activity decoding map is formed by the aforementioned energy contrast values of each voxel. Our experimental results demonstrate that the SCM (1) was able to characterize the energy contrast of task-related brain regions; (2) could decode brain activity at rest, as validated by the eyes-closed and eyes-open resting-state experiments; (3) was verified with test-retest validation, indicating excellent reliability with most coefficients > 0.9 across the test sessions; and (4) could locate the aberrant energy contrast regions which might reveal the brain pathology of brain diseases, such as Parkinson’s disease. In summary, we demonstrated that the reliable energy contrast feature was a useful biomarker in characterizing brain states, and the corresponding SCM showed excellent brain activity-decoding performance at the individual and group levels, implying its potentially broad application in neuroscience, neuroimaging, and brain diseases.
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Affiliation(s)
- Qin Yu
- Artificial Intelligence and Neuro-Informatics Engineering (ARINE) Laboratory, School of Computer Engineering, Jiangsu Ocean University, Lianyungang, China
| | - Zenglin Cai
- Department of Neurology, The Affiliated Suzhou Science and Technology Town Hospital of Nanjing Medical University, Suzhou, China
| | - Cunhua Li
- Artificial Intelligence and Neuro-Informatics Engineering (ARINE) Laboratory, School of Computer Engineering, Jiangsu Ocean University, Lianyungang, China
| | - Yulong Xiong
- Artificial Intelligence and Neuro-Informatics Engineering (ARINE) Laboratory, School of Computer Engineering, Jiangsu Ocean University, Lianyungang, China
| | - Yang Yang
- Center for Brain Science and Learning Difficulties, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Shuang He
- Artificial Intelligence and Neuro-Informatics Engineering (ARINE) Laboratory, School of Computer Engineering, Jiangsu Ocean University, Lianyungang, China
| | - Haitong Tang
- Artificial Intelligence and Neuro-Informatics Engineering (ARINE) Laboratory, School of Computer Engineering, Jiangsu Ocean University, Lianyungang, China
| | - Bo Zhang
- Department of Radiology, Affiliated Lianyungang Hospital of Xuzhou Medical University, Lianyungang, China
| | - Shouyun Du
- Department of Neurology, Guanyun People's Hospital, Guanyun, China
| | - Hongjie Yan
- Department of Neurology, Affiliated Lianyungang Hospital of Xuzhou Medical University, Lianyungang, China
| | - Chunqi Chang
- Health Science Center, School of Biomedical Engineering, Shenzhen University, Shenzhen, China.,Pengcheng Laboratory, Shenzhen, China
| | - Nizhuan Wang
- Artificial Intelligence and Neuro-Informatics Engineering (ARINE) Laboratory, School of Computer Engineering, Jiangsu Ocean University, Lianyungang, China
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4
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Besga A, Chyzhyk D, Graña M, Gonzalez-Pinto A. An Imaging and Blood Biomarkers Open Dataset on Alzheimer's Disease vs. Late Onset Bipolar Disorder. Front Aging Neurosci 2020; 12:583212. [PMID: 33192477 PMCID: PMC7658647 DOI: 10.3389/fnagi.2020.583212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 08/24/2020] [Indexed: 11/15/2022] Open
Affiliation(s)
- Ariadna Besga
- Centre for Biomedical Research Network on MentalHealth, Madrid, Spain.,Department of Internal Medicine, Hospital Universitario de Alava, Vitoria-Gasteiz, Spain
| | - Darya Chyzhyk
- Computational Intelligence Group, University of the Basque Country (UPV/EHU), San Sebastian, Spain.,ACPySS, San Sebastian, Spain
| | - Manuel Graña
- Computational Intelligence Group, University of the Basque Country (UPV/EHU), San Sebastian, Spain.,ACPySS, San Sebastian, Spain
| | - Ana Gonzalez-Pinto
- Centre for Biomedical Research Network on MentalHealth, Madrid, Spain.,Department of Psychiatry, University Hospital of Alava-Santiago, Vitoria-Gasteiz, Spain.,School of Medicine, University of the Basque Country, Vitoria-Gasteiz, Spain
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5
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Faviez C, Chen X, Garcelon N, Neuraz A, Knebelmann B, Salomon R, Lyonnet S, Saunier S, Burgun A. Diagnosis support systems for rare diseases: a scoping review. Orphanet J Rare Dis 2020; 15:94. [PMID: 32299466 PMCID: PMC7164220 DOI: 10.1186/s13023-020-01374-z] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Accepted: 03/31/2020] [Indexed: 12/14/2022] Open
Abstract
INTRODUCTION Rare diseases affect approximately 350 million people worldwide. Delayed diagnosis is frequent due to lack of knowledge of most clinicians and a small number of expert centers. Consequently, computerized diagnosis support systems have been developed to address these issues, with many relying on rare disease expertise and taking advantage of the increasing volume of generated and accessible health-related data. Our objective is to perform a review of all initiatives aiming to support the diagnosis of rare diseases. METHODS A scoping review was conducted based on methods proposed by Arksey and O'Malley. A charting form for relevant study analysis was developed and used to categorize data. RESULTS Sixty-eight studies were retained at the end of the charting process. Diagnosis targets varied from 1 rare disease to all rare diseases. Material used for diagnosis support consisted mostly of phenotype concepts, images or fluids. Fifty-seven percent of the studies used expert knowledge. Two-thirds of the studies relied on machine learning algorithms, and one-third used simple similarities. Manual algorithms were encountered as well. Most of the studies presented satisfying performance of evaluation by comparison with references or with external validation. Fourteen studies provided online tools, most of which aimed to support the diagnosis of all rare diseases by considering queries based on phenotype concepts. CONCLUSION Numerous solutions relying on different materials and use of various methodologies are emerging with satisfying preliminary results. However, the variability of approaches and evaluation processes complicates the comparison of results. Efforts should be made to adequately validate these tools and guarantee reproducibility and explicability.
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Affiliation(s)
- Carole Faviez
- Centre de Recherche des Cordeliers, INSERM, Université de Paris, Sorbonne Université, F-75006, Paris, France.
| | - Xiaoyi Chen
- Centre de Recherche des Cordeliers, INSERM, Université de Paris, Sorbonne Université, F-75006, Paris, France
| | - Nicolas Garcelon
- Centre de Recherche des Cordeliers, INSERM, Université de Paris, Sorbonne Université, F-75006, Paris, France.,Institut Imagine, Université de Paris, F-75015, Paris, France
| | - Antoine Neuraz
- Centre de Recherche des Cordeliers, INSERM, Université de Paris, Sorbonne Université, F-75006, Paris, France.,Département d'informatique médicale, Hôpital Necker-Enfants Malades, Assistance Publique - Hôpitaux de Paris (AP-HP), F-75015, Paris, France
| | - Bertrand Knebelmann
- Service de Néphrologie Transplantation Adultes, Hôpital Necker-Enfants Malades, F-75015, Paris, France.,Université de Paris, F-75006, Paris, France.,Institut Necker-Enfants Malades, INSERM, Hôpital Necker-Enfants Malades, F-75015, Paris, France
| | - Rémi Salomon
- Institut Imagine, Université de Paris, F-75015, Paris, France.,Service de Néphrologie Pédiatrique, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris (AP-HP), Université de Paris, F-75015, Paris, France
| | - Stanislas Lyonnet
- Université de Paris, F-75006, Paris, France.,Laboratory of Embryology and Genetics of Congenital Malformations, INSERM UMR 1163, Université de Paris, Imagine Institute, F-75015, Paris, France.,Service de génétique, Hôpital Necker-Enfants Malades, Assistance Publique - Hôpitaux de Paris (AP-HP), F-75015, Paris, France
| | - Sophie Saunier
- Université de Paris, F-75006, Paris, France.,Laboratory of Renal Hereditary Diseases, INSERM UMR 1163, Université de Paris, Imagine Institute, F-75015, Paris, France
| | - Anita Burgun
- Centre de Recherche des Cordeliers, INSERM, Université de Paris, Sorbonne Université, F-75006, Paris, France.,Département d'informatique médicale, Hôpital Necker-Enfants Malades, Assistance Publique - Hôpitaux de Paris (AP-HP), F-75015, Paris, France.,Université de Paris, F-75006, Paris, France.,PaRis Artificial Intelligence Research InstitutE (PRAIRIE), Paris, France
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6
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Attallah O, Karthikesalingam A, Holt PJ, Thompson MM, Sayers R, Bown MJ, Choke EC, Ma X. Using multiple classifiers for predicting the risk of endovascular aortic aneurysm repair re-intervention through hybrid feature selection. Proc Inst Mech Eng H 2017; 231:1048-1063. [PMID: 28925817 DOI: 10.1177/0954411917731592] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Feature selection is essential in medical area; however, its process becomes complicated with the presence of censoring which is the unique character of survival analysis. Most survival feature selection methods are based on Cox's proportional hazard model, though machine learning classifiers are preferred. They are less employed in survival analysis due to censoring which prevents them from directly being used to survival data. Among the few work that employed machine learning classifiers, partial logistic artificial neural network with auto-relevance determination is a well-known method that deals with censoring and perform feature selection for survival data. However, it depends on data replication to handle censoring which leads to unbalanced and biased prediction results especially in highly censored data. Other methods cannot deal with high censoring. Therefore, in this article, a new hybrid feature selection method is proposed which presents a solution to high level censoring. It combines support vector machine, neural network, and K-nearest neighbor classifiers using simple majority voting and a new weighted majority voting method based on survival metric to construct a multiple classifier system. The new hybrid feature selection process uses multiple classifier system as a wrapper method and merges it with iterated feature ranking filter method to further reduce features. Two endovascular aortic repair datasets containing 91% censored patients collected from two centers were used to construct a multicenter study to evaluate the performance of the proposed approach. The results showed the proposed technique outperformed individual classifiers and variable selection methods based on Cox's model such as Akaike and Bayesian information criterions and least absolute shrinkage and selector operator in p values of the log-rank test, sensitivity, and concordance index. This indicates that the proposed classifier is more powerful in correctly predicting the risk of re-intervention enabling doctor in selecting patients' future follow-up plan.
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Affiliation(s)
- Omneya Attallah
- 1 Department of Electronics and Communications, College of Engineering and Technology, Arab Academy for Science and Technology, Alexandria, Egypt.,2 School of Engineering and Applied Science, Aston University, Birmingham, UK
| | - Alan Karthikesalingam
- 3 St George's Vascular Institute, St George's University Hospitals NHS Foundation Trust, London, UK
| | - Peter Je Holt
- 3 St George's Vascular Institute, St George's University Hospitals NHS Foundation Trust, London, UK
| | - Matthew M Thompson
- 3 St George's Vascular Institute, St George's University Hospitals NHS Foundation Trust, London, UK
| | - Rob Sayers
- 4 NIHR Leicester Cardiovascular Biomedical Research Unit and Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
| | - Matthew J Bown
- 4 NIHR Leicester Cardiovascular Biomedical Research Unit and Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
| | - Eddie C Choke
- 4 NIHR Leicester Cardiovascular Biomedical Research Unit and Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
| | - Xianghong Ma
- 2 School of Engineering and Applied Science, Aston University, Birmingham, UK
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7
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Besga A, Chyzhyk D, Gonzalez-Ortega I, Echeveste J, Graña-Lecuona M, Graña M, Gonzalez-Pinto A. White Matter Tract Integrity in Alzheimer's Disease vs. Late Onset Bipolar Disorder and Its Correlation with Systemic Inflammation and Oxidative Stress Biomarkers. Front Aging Neurosci 2017; 9:179. [PMID: 28670271 PMCID: PMC5472694 DOI: 10.3389/fnagi.2017.00179] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2017] [Accepted: 05/23/2017] [Indexed: 12/16/2022] Open
Abstract
Background: Late Onset Bipolar Disorder (LOBD) is the development of Bipolar Disorder (BD) at an age above 50 years old. It is often difficult to differentiate from other aging dementias, such as Alzheimer's Disease (AD), because they share cognitive and behavioral impairment symptoms. Objectives: We look for WM tract voxel clusters showing significant differences when comparing of AD vs. LOBD, and its correlations with systemic blood plasma biomarkers (inflammatory, neurotrophic factors, and oxidative stress). Materials: A sample of healthy controls (HC) (n = 19), AD patients (n = 35), and LOBD patients (n = 24) was recruited at the Alava University Hospital. Blood plasma samples were obtained at recruitment time and analyzed to extract the inflammatory, oxidative stress, and neurotrophic factors. Several modalities of MRI were acquired for each subject, Methods: Fractional anisotropy (FA) coefficients are obtained from diffusion weighted imaging (DWI). Tract based spatial statistics (TBSS) finds FA skeleton clusters of WM tract voxels showing significant differences for all possible contrasts between HC, AD, and LOBD. An ANOVA F-test over all contrasts is carried out. Results of F-test are used to mask TBSS detected clusters for the AD > LOBD and LOBD > AD contrast to select the image clusters used for correlation analysis. Finally, Pearson's correlation coefficients between FA values at cluster sites and systemic blood plasma biomarker values are computed. Results: The TBSS contrasts with by ANOVA F-test has identified strongly significant clusters in the forceps minor, inferior longitudinal fasciculus, inferior fronto-occipital fasciculus, and cingulum gyrus. The correlation analysis of these tract clusters found strong negative correlation of AD with the nerve growth factor (NGF) and brain derived neurotrophic factor (BDNF) blood biomarkers. Negative correlation of AD and positive correlation of LOBD with inflammation biomarker IL6 was also found. Conclusion: TBSS voxel clusters tract atlas localizations are consistent with greater behavioral impairment and mood disorders in LOBD than in AD. Correlation analysis confirms that neurotrophic factors (i.e., NGF, BDNF) play a great role in AD while are absent in LOBD pathophysiology. Also, correlation results of IL1 and IL6 suggest stronger inflammatory effects in LOBD than in AD.
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Affiliation(s)
- Ariadna Besga
- Centre for Biomedical Research Network on Mental HealthSpain.,Department of Internal Medicine of Hospital Universitario de AlavaVitoria, Spain
| | - Darya Chyzhyk
- Computational Intelligence Group, University of the Basque Country (UPV/EHU)San Sebastian, Spain.,ACPySSSan Sebastian, Spain
| | - Itxaso Gonzalez-Ortega
- Department of Psychiatry, University Hospital of Alava-SantiagoVitoria, Spain.,School of Psychology, University of the Basque Country (UPV/EHU)San Sebastian, Spain
| | - Jon Echeveste
- Magnetic Resonance Imaging DepartmentOsatek, Vitoria, Spain
| | | | - Manuel Graña
- Computational Intelligence Group, University of the Basque Country (UPV/EHU)San Sebastian, Spain.,ACPySSSan Sebastian, Spain
| | - Ana Gonzalez-Pinto
- Centre for Biomedical Research Network on Mental HealthSpain.,Department of Psychiatry, University Hospital of Alava-SantiagoVitoria, Spain.,School of Medicine, University of the Basque Country (UPV/EHU)Vitoria, Spain
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8
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Beheshti I, Demirel H, Matsuda H. Classification of Alzheimer's disease and prediction of mild cognitive impairment-to-Alzheimer's conversion from structural magnetic resource imaging using feature ranking and a genetic algorithm. Comput Biol Med 2017; 83:109-119. [PMID: 28260614 DOI: 10.1016/j.compbiomed.2017.02.011] [Citation(s) in RCA: 141] [Impact Index Per Article: 20.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Revised: 01/26/2017] [Accepted: 02/23/2017] [Indexed: 01/18/2023]
Abstract
We developed a novel computer-aided diagnosis (CAD) system that uses feature-ranking and a genetic algorithm to analyze structural magnetic resonance imaging data; using this system, we can predict conversion of mild cognitive impairment (MCI)-to-Alzheimer's disease (AD) at between one and three years before clinical diagnosis. The CAD system was developed in four stages. First, we used a voxel-based morphometry technique to investigate global and local gray matter (GM) atrophy in an AD group compared with healthy controls (HCs). Regions with significant GM volume reduction were segmented as volumes of interest (VOIs). Second, these VOIs were used to extract voxel values from the respective atrophy regions in AD, HC, stable MCI (sMCI) and progressive MCI (pMCI) patient groups. The voxel values were then extracted into a feature vector. Third, at the feature-selection stage, all features were ranked according to their respective t-test scores and a genetic algorithm designed to find the optimal feature subset. The Fisher criterion was used as part of the objective function in the genetic algorithm. Finally, the classification was carried out using a support vector machine (SVM) with 10-fold cross validation. We evaluated the proposed automatic CAD system by applying it to baseline values from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (160 AD, 162 HC, 65 sMCI and 71 pMCI subjects). The experimental results indicated that the proposed system is capable of distinguishing between sMCI and pMCI patients, and would be appropriate for practical use in a clinical setting.
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Affiliation(s)
- Iman Beheshti
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, 4-1-1, Ogawahigashi-cho, Kodaira, Tokyo 187-8551, Japan.
| | - Hasan Demirel
- Biomedical Image Processing Group, Department of Electrical & Electronic Engineering, Eastern Mediterranean University, Famagusta, Mersin 10, Turkey
| | - Hiroshi Matsuda
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, 4-1-1, Ogawahigashi-cho, Kodaira, Tokyo 187-8551, Japan
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9
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Beheshti I, Demirel H. Feature-ranking-based Alzheimer’s disease classification from structural MRI. Magn Reson Imaging 2016; 34:252-63. [PMID: 26657976 DOI: 10.1016/j.mri.2015.11.009] [Citation(s) in RCA: 82] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2015] [Revised: 08/25/2015] [Accepted: 11/29/2015] [Indexed: 11/25/2022]
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10
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Besga A, Gonzalez I, Echeburua E, Savio A, Ayerdi B, Chyzhyk D, Madrigal JLM, Leza JC, Graña M, Gonzalez-Pinto AM. Discrimination between Alzheimer's Disease and Late Onset Bipolar Disorder Using Multivariate Analysis. Front Aging Neurosci 2015; 7:231. [PMID: 26696883 PMCID: PMC4677464 DOI: 10.3389/fnagi.2015.00231] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2015] [Accepted: 11/25/2015] [Indexed: 11/13/2022] Open
Abstract
Background Late onset bipolar disorder (LOBD) is often difficult to distinguish from degenerative dementias, such as Alzheimer disease (AD), due to comorbidities and common cognitive symptoms. Moreover, LOBD prevalence in the elder population is not negligible and it is increasing. Both pathologies share pathophysiological neuroinflammation features. Improvements in differential diagnosis of LOBD and AD will help to select the best personalized treatment. Objective The aim of this study is to assess the relative significance of clinical observations, neuropsychological tests, and specific blood plasma biomarkers (inflammatory and neurotrophic), separately and combined, in the differential diagnosis of LOBD versus AD. It was carried out evaluating the accuracy achieved by classification-based computer-aided diagnosis (CAD) systems based on these variables. Materials A sample of healthy controls (HC) (n = 26), AD patients (n = 37), and LOBD patients (n = 32) was recruited at the Alava University Hospital. Clinical observations, neuropsychological tests, and plasma biomarkers were measured at recruitment time. Methods We applied multivariate machine learning classification methods to discriminate subjects from HC, AD, and LOBD populations in the study. We analyzed, for each classification contrast, feature sets combining clinical observations, neuropsychological measures, and biological markers, including inflammation biomarkers. Furthermore, we analyzed reduced feature sets containing variables with significative differences determined by a Welch’s t-test. Furthermore, a battery of classifier architectures were applied, encompassing linear and non-linear Support Vector Machines (SVM), Random Forests (RF), Classification and regression trees (CART), and their performance was evaluated in a leave-one-out (LOO) cross-validation scheme. Post hoc analysis of Gini index in CART classifiers provided a measure of each variable importance. Results Welch’s t-test found one biomarker (Malondialdehyde) with significative differences (p < 0.001) in LOBD vs. AD contrast. Classification results with the best features are as follows: discrimination of HC vs. AD patients reaches accuracy 97.21% and AUC 98.17%. Discrimination of LOBD vs. AD patients reaches accuracy 90.26% and AUC 89.57%. Discrimination of HC vs LOBD patients achieves accuracy 95.76% and AUC 88.46%. Conclusion It is feasible to build CAD systems for differential diagnosis of LOBD and AD on the basis of a reduced set of clinical variables. Clinical observations provide the greatest discrimination. Neuropsychological tests are improved by the addition of biomarkers, and both contribute significantly to improve the overall predictive performance.
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Affiliation(s)
- Ariadna Besga
- Department of Psychiatry, University Hospital of Alava-Santiago , Vitoria , Spain ; Centre for Biomedical Research Network on Mental Health (CIBERSAM) , Madrid , Spain ; School of Medicine, University of the Basque Country , Vitoria , Spain
| | - Itxaso Gonzalez
- Department of Psychiatry, University Hospital of Alava-Santiago , Vitoria , Spain ; Centre for Biomedical Research Network on Mental Health (CIBERSAM) , Madrid , Spain ; School of Psychology, University of the Basque Country , San Sebastian , Spain
| | - Enrique Echeburua
- Centre for Biomedical Research Network on Mental Health (CIBERSAM) , Madrid , Spain ; School of Psychology, University of the Basque Country , San Sebastian , Spain
| | - Alexandre Savio
- Computational Intelligence Group (GIC), University of the Basque Country , San Sebastian , Spain ; ENGINE Centre, Wrocław University of Technology , Wrocław , Poland
| | - Borja Ayerdi
- Computational Intelligence Group (GIC), University of the Basque Country , San Sebastian , Spain
| | - Darya Chyzhyk
- Computational Intelligence Group (GIC), University of the Basque Country , San Sebastian , Spain ; Department of Computer and Information Science and Engineering, University of Florida , Gainesville, FL , USA
| | - Jose L M Madrigal
- Centre for Biomedical Research Network on Mental Health (CIBERSAM) , Madrid , Spain ; Department of Pharmacology, Faculty of Medicine, University Complutense and IIS Hospital 12 de Octubre , Madrid , Spain
| | - Juan C Leza
- Centre for Biomedical Research Network on Mental Health (CIBERSAM) , Madrid , Spain ; Department of Pharmacology, Faculty of Medicine, University Complutense and IIS Hospital 12 de Octubre , Madrid , Spain
| | - Manuel Graña
- Computational Intelligence Group (GIC), University of the Basque Country , San Sebastian , Spain ; ENGINE Centre, Wrocław University of Technology , Wrocław , Poland ; Asociacion de Ciencias de la Programacion Python San Sebastian (ACPySS) , San Sebastian , Spain
| | - Ana Maria Gonzalez-Pinto
- Department of Psychiatry, University Hospital of Alava-Santiago , Vitoria , Spain ; Centre for Biomedical Research Network on Mental Health (CIBERSAM) , Madrid , Spain
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11
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Beheshti I, Demirel H. Probability distribution function-based classification of structural MRI for the detection of Alzheimer's disease. Comput Biol Med 2015; 64:208-16. [PMID: 26226415 DOI: 10.1016/j.compbiomed.2015.07.006] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2015] [Revised: 07/08/2015] [Accepted: 07/10/2015] [Indexed: 02/06/2023]
Abstract
High-dimensional classification methods have been a major target of machine learning for the automatic classification of patients who suffer from Alzheimer's disease (AD). One major issue of automatic classification is the feature-selection method from high-dimensional data. In this paper, a novel approach for statistical feature reduction and selection in high-dimensional magnetic resonance imaging (MRI) data based on the probability distribution function (PDF) is introduced. To develop an automatic computer-aided diagnosis (CAD) technique, this research explores the statistical patterns extracted from structural MRI (sMRI) data on four systematic levels. First, global and local differences of gray matter in patients with AD compared to healthy controls (HCs) using the voxel-based morphometric (VBM) technique with 3-Tesla 3D T1-weighted MRI are investigated. Second, feature extraction based on the voxel clusters detected by VBM on sMRI and voxel values as volume of interest (VOI) is used. Third, a novel statistical feature-selection process is employed, utilizing the PDF of the VOI to represent statistical patterns of the respective high-dimensional sMRI sample. Finally, the proposed feature-selection method for early detection of AD with support vector machine (SVM) classifiers compared to other standard feature selection methods, such as partial least squares (PLS) techniques, is assessed. The performance of the proposed technique is evaluated using 130 AD and 130 HC MRI data from the ADNI dataset with 10-fold cross validation(1). The results show that the PDF-based feature selection approach is a reliable technique that is highly competitive with respect to the state-of-the-art techniques in classifying AD from high-dimensional sMRI samples.
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Affiliation(s)
- I Beheshti
- Biomedical Image Processing Lab, Department of Electrical & Electronic Engineering, Eastern Mediterranean University, Gazimagusa, Mersin 10, Turkey.
| | - H Demirel
- Biomedical Image Processing Lab, Department of Electrical & Electronic Engineering, Eastern Mediterranean University, Gazimagusa, Mersin 10, Turkey.
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A lattice computing approach to Alzheimer’s disease computer assisted diagnosis based on MRI data. Neurocomputing 2015. [DOI: 10.1016/j.neucom.2014.02.076] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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13
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Tsunoyama T, Pham TD, Fujita T, Sakamoto T. Identification of intestinal wall abnormalities and ischemia by modeling spatial uncertainty in computed tomography imaging findings. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2014; 117:30-39. [PMID: 24938748 DOI: 10.1016/j.cmpb.2014.05.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2014] [Revised: 05/07/2014] [Accepted: 05/07/2014] [Indexed: 06/03/2023]
Abstract
Intestinal abnormalities and ischemia are medical conditions in which inflammation and injury of the intestine are caused by inadequate blood supply. Acute ischemia of the small bowel can be life-threatening. Computed tomography (CT) is currently a gold standard for the diagnosis of acute intestinal ischemia in the emergency department. However, the assessment of the diagnostic performance of CT findings in the detection of intestinal abnormalities and ischemia has been a difficult task for both radiologists and surgeons. Little effort has been found in developing computerized systems for the automated identification of these types of complex gastrointestinal disorders. In this paper, a geostatistical mapping of spatial uncertainty in CT scans is introduced for medical image feature extraction, which can be effectively applied for diagnostic detection of intestinal abnormalities and ischemia from control patterns. Experimental results obtained from the analysis of clinical data suggest the usefulness of the proposed uncertainty mapping model.
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Affiliation(s)
- Taichiro Tsunoyama
- School of Medicine, Department of Emergency Medicine, Trauma and Resuscitation Center, Teikyo University, Tokyo 173-8606, Japan.
| | - Tuan D Pham
- Aizu Research Cluster for Medical Engineering and Informatics, Research Center for Advanced Information Science and Technology, The University of Aizu, Aizu-Wakamatsu, Fukushima 965-8580, Japan.
| | - Takashi Fujita
- School of Medicine, Department of Emergency Medicine, Trauma and Resuscitation Center, Teikyo University, Tokyo 173-8606, Japan.
| | - Tetsuya Sakamoto
- School of Medicine, Department of Emergency Medicine, Trauma and Resuscitation Center, Teikyo University, Tokyo 173-8606, Japan.
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14
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Chyzhyk D, Savio A, Graña M. Evolutionary ELM wrapper feature selection for Alzheimer's disease CAD on anatomical brain MRI. Neurocomputing 2014. [DOI: 10.1016/j.neucom.2013.01.065] [Citation(s) in RCA: 59] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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15
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Termenon M, Graña M, Besga A, Echeveste J, Gonzalez-Pinto A. Lattice independent component analysis feature selection on diffusion weighted imaging for Alzheimer's disease classification. Neurocomputing 2013. [DOI: 10.1016/j.neucom.2012.08.044] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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16
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17
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Meta-ensembles of Classifiers for Alzheimer’s Disease Detection Using Independent ROI Features. NATURAL AND ARTIFICIAL COMPUTATION IN ENGINEERING AND MEDICAL APPLICATIONS 2013. [DOI: 10.1007/978-3-642-38622-0_13] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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18
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Savio A, Graña M. An Ensemble of Classifiers Guided by the AAL Brain Atlas for Alzheimer’s Disease Detection. ADVANCES IN COMPUTATIONAL INTELLIGENCE 2013. [DOI: 10.1007/978-3-642-38682-4_13] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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19
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Kim J, Lee JH. Integration of structural and functional magnetic resonance imaging improves mild cognitive impairment detection. Magn Reson Imaging 2012; 31:718-32. [PMID: 23260395 DOI: 10.1016/j.mri.2012.11.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2012] [Revised: 11/11/2012] [Accepted: 11/12/2012] [Indexed: 11/15/2022]
Abstract
The identification of mild cognitive impairments (MCI) via either structural magnetic resonance imaging (sMRI) or functional MRI (fMRI) has great potential due to the non-invasiveness of the techniques. Furthermore, these techniques allow longitudinal follow-ups of single subjects via repeated measurements. sMRI- or fMRI-based biomarkers have been adopted separately to diagnose MCI; however, there has not been a systematic effort to integrate sMRI- and fMRI-based features to increase MCI detection accuracy. This study investigated whether the detection of MCI can be improved via the integration of biomarkers identified from both sMRI and fMRI modalities. Regional volume sizes and neuronal activity levels of brains from MCI subjects were compared with those from healthy controls and used to identify biomarkers from sMRI and fMRI data, respectively. In the subsequent classification phase, MCI was automatically detected using a support vector machine algorithm that employed the identified sMRI- and fMRI-based biomarkers as an input feature vector. The results indicate that the fMRI-based biomarkers provided more information for detecting MCI than the sMRI-based biomarkers. Moreover, the integrated feature sets using the sMRI- and fMRI-based biomarkers consistently showed greater detection accuracy than the feature sets based only on the fMRI-based biomarkers. The results demonstrate that integration of sMRI and fMRI modalities can provide supplemental information to improve the diagnosis of MCI relative to either the sMRI or fMRI modalities alone.
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Affiliation(s)
- Junghoe Kim
- Department of Brain and Cognitive Engineering, Korea University, Anam-dong 5ga, Seongbuk-gu, Seoul 136-713, Republic of Korea
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20
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Besga A, Termenon M, Graña M, Echeveste J, Pérez JM, Gonzalez-Pinto A. Discovering Alzheimer's disease and bipolar disorder white matter effects building computer aided diagnostic systems on brain diffusion tensor imaging features. Neurosci Lett 2012; 520:71-6. [PMID: 22617636 DOI: 10.1016/j.neulet.2012.05.033] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2011] [Revised: 02/13/2012] [Accepted: 05/10/2012] [Indexed: 10/28/2022]
Abstract
The aim of this study is to look for differential effects in white matter (WM) of bipolar disorder (BD) and Alzheimer's disease (AD) patients. We proceed by investigating the feasibility of discriminating between BD and AD patients, and from healthy controls (HC), using multivariate data analysis based on diffusion tensor imaging (DTI) data features. Specifically, support vector machine (SVM) classifiers were trained and tested on fractional anisotropy (FA). Voxel sites are selected as features for classification if their Pearson's correlation between FA values at voxel site across subjects and the indicative variable specifying the subject class is above the threshold set by a percentile of its empirical distribution. To avoid double dipping, selection was performed only on training data in a leave one out cross-validation study. Classification results show that FA features and a linear SVM classifier achieve perfect accuracy, sensitivity and specificity in AD vs. HC, BD vs. HC, and AD vs. BD leave-one-out cross-validation studies. The localization of the discriminant voxel sites on a probabilistic tractography atlas shows effects on seven major WM tracts in each hemisphere and two commissural tracts.
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Affiliation(s)
- A Besga
- Unidad de Investigación en Psiquiatría, Hospital Santiago Apostol, Vitoria-Gasteiz, Spain
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21
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A Two Stage Sequential Ensemble Applied to the Classification of Alzheimer’s Disease Based on MRI Features. Neural Process Lett 2011. [DOI: 10.1007/s11063-011-9200-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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22
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Computer Aided Diagnosis system for Alzheimer Disease using brain Diffusion Tensor Imaging features selected by Pearson's correlation. Neurosci Lett 2011; 502:225-9. [DOI: 10.1016/j.neulet.2011.07.049] [Citation(s) in RCA: 92] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2011] [Revised: 07/16/2011] [Accepted: 07/26/2011] [Indexed: 11/18/2022]
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23
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Savio A, Grańa M, Villanúa J. Deformation Based Features for Alzheimer’s Disease Detection with Linear SVM. ACTA ACUST UNITED AC 2011. [DOI: 10.1007/978-3-642-21222-2_41] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
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