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Ahammad I, Lamisa AB, Bhattacharjee A, Jamal TB, Arefin MS, Chowdhury ZM, Hossain MU, Das KC, Keya CA, Salimullah M. AITeQ: a machine learning framework for Alzheimer's prediction using a distinctive five-gene signature. Brief Bioinform 2024; 25:bbae291. [PMID: 38877887 PMCID: PMC11179120 DOI: 10.1093/bib/bbae291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 05/23/2024] [Accepted: 06/04/2024] [Indexed: 06/18/2024] Open
Abstract
Neurodegenerative diseases, such as Alzheimer's disease, pose a significant global health challenge with their complex etiology and elusive biomarkers. In this study, we developed the Alzheimer's Identification Tool (AITeQ) using ribonucleic acid-sequencing (RNA-seq), a machine learning (ML) model based on an optimized ensemble algorithm for the identification of Alzheimer's from RNA-seq data. Analysis of RNA-seq data from several studies identified 87 differentially expressed genes. This was followed by a ML protocol involving feature selection, model training, performance evaluation, and hyperparameter tuning. The feature selection process undertaken in this study, employing a combination of four different methodologies, culminated in the identification of a compact yet impactful set of five genes. Twelve diverse ML models were trained and tested using these five genes (CNKSR1, EPHA2, CLSPN, OLFML3, and TARBP1). Performance metrics, including precision, recall, F1 score, accuracy, Matthew's correlation coefficient, and receiver operating characteristic area under the curve were assessed for the finally selected model. Overall, the ensemble model consisting of logistic regression, naive Bayes classifier, and support vector machine with optimized hyperparameters was identified as the best and was used to develop AITeQ. AITeQ is available at: https://github.com/ishtiaque-ahammad/AITeQ.
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Affiliation(s)
- Ishtiaque Ahammad
- Bioinformatics Division, National Institute of Biotechnology, Ganakbari, Ashulia, Savar, Dhaka 1349, Bangladesh
| | - Anika Bushra Lamisa
- Bioinformatics Division, National Institute of Biotechnology, Ganakbari, Ashulia, Savar, Dhaka 1349, Bangladesh
| | - Arittra Bhattacharjee
- Bioinformatics Division, National Institute of Biotechnology, Ganakbari, Ashulia, Savar, Dhaka 1349, Bangladesh
| | - Tabassum Binte Jamal
- Bioinformatics Division, National Institute of Biotechnology, Ganakbari, Ashulia, Savar, Dhaka 1349, Bangladesh
| | - Md Shamsul Arefin
- Department of Biochemistry and Microbiology, North South University, Bashundhara, Dhaka 1229, Bangladesh
| | - Zeshan Mahmud Chowdhury
- Bioinformatics Division, National Institute of Biotechnology, Ganakbari, Ashulia, Savar, Dhaka 1349, Bangladesh
| | - Mohammad Uzzal Hossain
- Bioinformatics Division, National Institute of Biotechnology, Ganakbari, Ashulia, Savar, Dhaka 1349, Bangladesh
| | - Keshob Chandra Das
- Molecular Biotechnology Division, National Institute of Biotechnology, Ganakbari, Ashulia, Savar, Dhaka 1349, Bangladesh
| | - Chaman Ara Keya
- Department of Biochemistry and Microbiology, North South University, Bashundhara, Dhaka 1229, Bangladesh
| | - Md Salimullah
- Molecular Biotechnology Division, National Institute of Biotechnology, Ganakbari, Ashulia, Savar, Dhaka 1349, Bangladesh
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Cheung EYW, Wu RWK, Chu ESM, Mak HKF. Integrating Demographics and Imaging Features for Various Stages of Dementia Classification: Feed Forward Neural Network Multi-Class Approach. Biomedicines 2024; 12:896. [PMID: 38672253 PMCID: PMC11047992 DOI: 10.3390/biomedicines12040896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 03/05/2024] [Accepted: 03/12/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND MRI magnetization-prepared rapid acquisition (MPRAGE) is an easily available imaging modality for dementia diagnosis. Previous studies suggested that volumetric analysis plays a crucial role in various stages of dementia classification. In this study, volumetry, radiomics and demographics were integrated as inputs to develop an artificial intelligence model for various stages, including Alzheimer's disease (AD), mild cognitive decline (MCI) and cognitive normal (CN) dementia classifications. METHOD The Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset was separated into training and testing groups, and the Open Access Series of Imaging Studies (OASIS) dataset was used as the second testing group. The MRI MPRAGE image was reoriented via statistical parametric mapping (SPM12). Freesurfer was employed for brain segmentation, and 45 regional brain volumes were retrieved. The 3D Slicer software was employed for 107 radiomics feature extractions from within the whole brain. Data on patient demographics were collected from the datasets. The feed-forward neural network (FFNN) and the other most common artificial intelligence algorithms, including support vector machine (SVM), ensemble classifier (EC) and decision tree (DT), were used to build the models using various features. RESULTS The integration of brain regional volumes, radiomics and patient demographics attained the highest overall accuracy at 76.57% and 73.14% in ADNI and OASIS testing, respectively. The subclass accuracies in MCI, AD and CN were 78.29%, 89.71% and 85.14%, respectively, in ADNI testing, as well as 74.86%, 88% and 83.43% in OASIS testing. Balanced sensitivity and specificity were obtained for all subclass classifications in MCI, AD and CN. CONCLUSION The FFNN yielded good overall accuracy for MCI, AD and CN categorization, with balanced subclass accuracy, sensitivity and specificity. The proposed FFNN model is simple, and it may support the triage of patients for further confirmation of the diagnosis.
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Affiliation(s)
- Eva Y. W. Cheung
- School of Medical and Health Sciences, Tung Wah College, 31 Wylie Road, HoManTin, Hong Kong
| | - Ricky W. K. Wu
- Department of Biological and Biomedical Sciences, School of Health and Life Sciences, Glasgow Caledonian University, Glasgow G4 0BA, UK
| | - Ellie S. M. Chu
- School of Medical and Health Sciences, Tung Wah College, 31 Wylie Road, HoManTin, Hong Kong
| | - Henry K. F. Mak
- Department of Diagnostic Radiology, School of Clinical Medicine, LKS Faculty of Medicine, University of Hong Kong, Hong Kong
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Jomeiri A, Navin AH, Shamsi M. Longitudinal MRI analysis using a hybrid DenseNet-BiLSTM method for Alzheimer's disease prediction. Behav Brain Res 2024; 463:114900. [PMID: 38341100 DOI: 10.1016/j.bbr.2024.114900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 12/16/2023] [Accepted: 02/06/2024] [Indexed: 02/12/2024]
Abstract
Alzheimer's disease is a progressive neurological disorder characterized by brain atrophy and cell death, leading to cognitive decline and impaired functioning. Previous research has primarily focused on using cross-sectional data for Alzheimer's disease identification, but analyzing longitudinal sequential MR images is crucial for improved diagnostic accuracy and understanding disease progression. However, existing deep learning models face challenges in learning spatial and temporal features from such data. To address these challenges, this study presents a novel hybrid DenseNet-BiLSTM method for Alzheimer's disease prediction using longitudinal MRI analysis. The proposed framework combines Convolutional DenseNet for spatial information extraction and joined BiLSTM layers for capturing temporal characteristics and relationships between longitudinal images at different time points. This approach overcomes issues like overfitting, vanishing gradients, and incomplete patient data. We evaluated the model on 684 longitudinal MRI images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including normal controls, individuals with mild cognitive impairment, and Alzheimer's disease patients. The results demonstrate high classification accuracy, with 95.28% for AD/CN, 88.19% for NC/MCI, 83.51% for sMCI/pMCI, and 92.14% for MCI/AD. These findings highlight the substantial improvement in Alzheimer's disease diagnosis achieved through the utilization of longitudinal MRI images. The contributions of this study lie in both the deep learning and medical domains. In the deep learning domain, our hybrid framework effectively learns spatial and temporal features from longitudinal data, addressing the challenges associated with multi-dimensional and sequential time series data. In the medical domain, our study emphasizes the importance of analyzing baseline and longitudinal MR images for accurate diagnosis and understanding disease progression.
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Affiliation(s)
- Alireza Jomeiri
- Department of Computer Engineering, Qom Branch, Islamic Azad University, Qom, Iran
| | - Ahmad Habibizad Navin
- Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran.
| | - Mahboubeh Shamsi
- Department of Engineering, Qom University of Technology, Qom, Iran
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Bajaj S, Mahesh R. Converged avenues: depression and Alzheimer's disease- shared pathophysiology and novel therapeutics. Mol Biol Rep 2024; 51:225. [PMID: 38281208 DOI: 10.1007/s11033-023-09170-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 12/15/2023] [Indexed: 01/30/2024]
Abstract
Depression, a highly prevalent disorder affecting over 280 million people worldwide, is comorbid with many neurological disorders, particularly Alzheimer's disease (AD). Depression and AD share overlapping pathophysiology, and the search for accountable biological substrates made it an essential and intriguing field of research. The paper outlines the neurobiological pathways coinciding with depression and AD, including neurotrophin signalling, the hypothalamic-pituitary-adrenal axis (HPA), cellular apoptosis, neuroinflammation, and other aetiological factors. Understanding overlapping pathways is crucial in identifying common pathophysiological substrates that can be targeted for effective management of disease state. Antidepressants, particularly monoaminergic drugs (first-line therapy), are shown to have modest or no clinical benefits. Regardless of the ineffectiveness of conventional antidepressants, these drugs remain the mainstay for treating depressive symptoms in AD. To overcome the ineffectiveness of traditional pharmacological agents in treating comorbid conditions, a novel therapeutic class has been discussed in the paper. This includes neurotransmitter modulators, glutamatergic system modulators, mitochondrial modulators, antioxidant agents, HPA axis targeted therapy, inflammatory system targeted therapy, neurogenesis targeted therapy, repurposed anti-diabetic agents, and others. The primary clinical challenge is the development of therapeutic agents and the effective diagnosis of the comorbid condition for which no specific diagnosable scale is present. Hence, introducing Artificial Intelligence (AI) into the healthcare system is revolutionary. AI implemented with interdisciplinary strategies (neuroimaging, EEG, molecular biomarkers) bound to have accurate clinical interpretation of symptoms. Moreover, AI has the potential to forecast neurodegenerative and psychiatric illness much in advance before visible/observable clinical symptoms get precipitated.
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Affiliation(s)
- Shivanshu Bajaj
- Department of Pharmacy, Birla Institute of Technology and Science (BITS), Pilani, 333031, Rajasthan, India
| | - Radhakrishnan Mahesh
- Department of Pharmacy, Birla Institute of Technology and Science (BITS), Pilani, 333031, Rajasthan, India.
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Rahman A, Debnath T, Kundu D, Khan MSI, Aishi AA, Sazzad S, Sayduzzaman M, Band SS. Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities. AIMS Public Health 2024; 11:58-109. [PMID: 38617415 PMCID: PMC11007421 DOI: 10.3934/publichealth.2024004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Accepted: 12/18/2023] [Indexed: 04/16/2024] Open
Abstract
In recent years, machine learning (ML) and deep learning (DL) have been the leading approaches to solving various challenges, such as disease predictions, drug discovery, medical image analysis, etc., in intelligent healthcare applications. Further, given the current progress in the fields of ML and DL, there exists the promising potential for both to provide support in the realm of healthcare. This study offered an exhaustive survey on ML and DL for the healthcare system, concentrating on vital state of the art features, integration benefits, applications, prospects and future guidelines. To conduct the research, we found the most prominent journal and conference databases using distinct keywords to discover scholarly consequences. First, we furnished the most current along with cutting-edge progress in ML-DL-based analysis in smart healthcare in a compendious manner. Next, we integrated the advancement of various services for ML and DL, including ML-healthcare, DL-healthcare, and ML-DL-healthcare. We then offered ML and DL-based applications in the healthcare industry. Eventually, we emphasized the research disputes and recommendations for further studies based on our observations.
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Affiliation(s)
- Anichur Rahman
- Department of CSE, National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka-1350
- Department of CSE, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Tanoy Debnath
- Department of CSE, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
- Department of CSE, Green University of Bangladesh, 220/D, Begum Rokeya Sarani, Dhaka -1207, Bangladesh
| | - Dipanjali Kundu
- Department of CSE, National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka-1350
| | - Md. Saikat Islam Khan
- Department of CSE, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Airin Afroj Aishi
- Department of Computing and Information System, Daffodil International University, Savar, Dhaka, Bangladesh
| | - Sadia Sazzad
- Department of CSE, National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka-1350
| | - Mohammad Sayduzzaman
- Department of CSE, National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka-1350
| | - Shahab S. Band
- Department of Information Management, International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, Taiwan
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Bucholc M, James C, Khleifat AA, Badhwar A, Clarke N, Dehsarvi A, Madan CR, Marzi SJ, Shand C, Schilder BM, Tamburin S, Tantiangco HM, Lourida I, Llewellyn DJ, Ranson JM. Artificial intelligence for dementia research methods optimization. Alzheimers Dement 2023; 19:5934-5951. [PMID: 37639369 DOI: 10.1002/alz.13441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 07/19/2023] [Accepted: 07/23/2023] [Indexed: 08/31/2023]
Abstract
Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high-dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well-documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI-enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co-produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care. HIGHLIGHTS: Machine learning (ML) can improve diagnosis, prevention, and management of dementia. Inadequate reporting of ML procedures affects reproduction/replication of results. ML models built on unrepresentative datasets do not generalize to new datasets. Obligatory metrics for certain model structures and use cases have not been defined. Interpretability and trust in ML predictions are barriers to clinical translation.
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Affiliation(s)
- Magda Bucholc
- Cognitive Analytics Research Lab, School of Computing, Engineering & Intelligent Systems, Ulster University, Derry, UK
| | - Charlotte James
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and University of Bristol, Bristol, UK
| | - Ahmad Al Khleifat
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - AmanPreet Badhwar
- Multiomics Investigation of Neurodegenerative Diseases (MIND) Lab, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Quebec, Canada
- Institut de génie biomédical, Université de Montréal, Montréal, Quebec, Canada
- Département de Pharmacologie et Physiologie, Université de Montréal, Montréal, Quebec, Canada
| | - Natasha Clarke
- Multiomics Investigation of Neurodegenerative Diseases (MIND) Lab, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Quebec, Canada
| | - Amir Dehsarvi
- Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences, and Nutrition, University of Aberdeen, Aberdeen, UK
| | | | - Sarah J Marzi
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Cameron Shand
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Brian M Schilder
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Stefano Tamburin
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | | | | | - David J Llewellyn
- University of Exeter Medical School, Exeter, UK
- The Alan Turing Institute, London, UK
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Shahzadi S, Butt NA, Sana MU, Pascual IE, Urbano MB, Díez IDLT, Ashraf I. Voxel Extraction and Multiclass Classification of Identified Brain Regions across Various Stages of Alzheimer's Disease Using Machine Learning Approaches. Diagnostics (Basel) 2023; 13:2871. [PMID: 37761238 PMCID: PMC10527683 DOI: 10.3390/diagnostics13182871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 08/25/2023] [Accepted: 08/30/2023] [Indexed: 09/29/2023] Open
Abstract
This study sought to investigate how different brain regions are affected by Alzheimer's disease (AD) at various phases of the disease, using independent component analysis (ICA). The study examines six regions in the mild cognitive impairment (MCI) stage, four in the early stage of Alzheimer's disease (AD), six in the moderate stage, and six in the severe stage. The precuneus, cuneus, middle frontal gyri, calcarine cortex, superior medial frontal gyri, and superior frontal gyri were the areas impacted at all phases. A general linear model (GLM) is used to extract the voxels of the previously mentioned regions. The resting fMRI data for 18 AD patients who had advanced from MCI to stage 3 of the disease were obtained from the ADNI public source database. The subjects include eight women and ten men. The voxel dataset is used to train and test ten machine learning algorithms to categorize the MCI, mild, moderate, and severe stages of Alzheimer's disease. The accuracy, recall, precision, and F1 score were used as conventional scoring measures to evaluate the classification outcomes. AdaBoost fared better than the other algorithms and obtained a phenomenal accuracy of 98.61%, precision of 99.00%, and recall and F1 scores of 98.00% each.
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Affiliation(s)
- Samra Shahzadi
- Department of Computer Science, Faculty of Computing and Information Technology, University of Gujrat, Gujrat 50700, Pakistan; (S.S.); (N.A.B.)
| | - Naveed Anwer Butt
- Department of Computer Science, Faculty of Computing and Information Technology, University of Gujrat, Gujrat 50700, Pakistan; (S.S.); (N.A.B.)
| | - Muhammad Usman Sana
- Department of Information Technology, University of Gujrat, Gujrat 50700, Pakistan;
| | - Iñaki Elío Pascual
- Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain; (I.E.P.); (M.B.U.)
- Universidade Internacional do Cuanza, Cuito EN250, Bié, Angola
- Fundación Universitaria Internacional de Colombia, Bogotá 11001, Colombia
| | - Mercedes Briones Urbano
- Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain; (I.E.P.); (M.B.U.)
- Universidad Internacional Iberoamericana, Campeche 24560, Mexico
- Universidad Internacional Iberoamericana, Arecibo, PR 00613, USA
| | - Isabel de la Torre Díez
- Department of Signal Theory, Communications and Telematics Engineering, Unviersity of Valladolid, Paseo de Belén, 15, 47011 Valladolid, Spain
| | - Imran Ashraf
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
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Zhao Y, Wang B, Liu CF, Faria AV, Miller MI, Caffo BS, Luo X. Identifying brain hierarchical structures associated with Alzheimer's disease using a regularized regression method with tree predictors. Biometrics 2023; 79:2333-2345. [PMID: 36263865 PMCID: PMC10115907 DOI: 10.1111/biom.13775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 10/03/2022] [Indexed: 11/30/2022]
Abstract
Brain segmentation at different levels is generally represented as hierarchical trees. Brain regional atrophy at specific levels was found to be marginally associated with Alzheimer's disease outcomes. In this study, we propose an ℓ1 -type regularization for predictors that follow a hierarchical tree structure. Considering a tree as a directed acyclic graph, we interpret the model parameters from a path analysis perspective. Under this concept, the proposed penalty regulates the total effect of each predictor on the outcome. With regularity conditions, it is shown that under the proposed regularization, the estimator of the model coefficient is consistent in ℓ2 -norm and the model selection is also consistent. When applied to a brain sMRI dataset acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the proposed approach identifies brain regions where atrophy in these regions demonstrates the declination in memory. With regularization on the total effects, the findings suggest that the impact of atrophy on memory deficits is localized from small brain regions, but at various levels of brain segmentation. Data used in preparation of this paper were obtained from the ADNI database.
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Affiliation(s)
- Yi Zhao
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Bingkai Wang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Chin-Fu Liu
- Center for Imaging Science, Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Andreia V. Faria
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Michael I. Miller
- Center for Imaging Science, Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Brian S. Caffo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Xi Luo
- Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, Houston, Texas, USA
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Sendi MS, Zendehrouh E, Fu Z, Liu J, Du Y, Mormino E, Salat DH, Calhoun VD, Miller RL. Disrupted Dynamic Functional Network Connectivity Among Cognitive Control Networks in the Progression of Alzheimer's Disease. Brain Connect 2023; 13:334-343. [PMID: 34102870 PMCID: PMC10442683 DOI: 10.1089/brain.2020.0847] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Background: Alzheimer's disease (AD) is the most common age-related dementia that promotes a decline in memory, thinking, and social skills. The initial stages of dementia can be associated with mild symptoms, and symptom progression to a more severe state is heterogeneous across patients. Recent work has demonstrated the potential for functional network mapping to assist in the prediction of symptomatic progression. However, this work has primarily used static functional connectivity (sFC) from resting-state functional magnetic resonance imaging. Recently, dynamic functional connectivity (dFC) has been recognized as a powerful advance in functional connectivity methodology to differentiate brain network dynamics between healthy and diseased populations. Methods: Group independent component analysis was applied to extract 17 components within the cognitive control network (CCN) from 1385 individuals across varying stages of AD symptomology. We estimated dFC among 17 components within the CCN, followed by clustering the dFCs into 3 recurring brain states, and then estimated a hidden Markov model and the occupancy rate for each subject. Then, we investigated the link between CCN dFC features and AD progression. Also, we investigated the link between sFC and AD progression and compared its results with dFC results. Results: Progression of AD symptoms was associated with increases in connectivity within the middle frontal gyrus. Also, the very mild AD (vmAD) showed less connectivity within the inferior parietal lobule (in both sFC and dFC) and between this region and the rest of CCN (in dFC analysis). Also, we found that within-middle frontal gyrus connectivity increases with AD progression in both sFC and dFC results. Finally, comparing with vmAD, we found that the normal brain spends significantly more time in a state with lower within-middle frontal gyrus connectivity and higher connectivity between the hippocampus and the rest of CCN, highlighting the importance of assessing the dynamics of brain connectivity in this disease. Conclusion: Our results suggest that AD progress not only alters the CCN connectivity strength but also changes the temporal properties in this brain network. This suggests the temporal and spatial pattern of CCN as a biomarker that differentiates different stages of AD.
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Affiliation(s)
- Mohammad S.E. Sendi
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Elaheh Zendehrouh
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Jingyu Liu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
| | - Yuhui Du
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
- School of Computer and Information Technology, Shanxi University, Taiyuan, China
| | | | - David H. Salat
- Harvard Medical School, Cambridge, Massachusetts, USA
- Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Vince D. Calhoun
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
| | - Robyn L. Miller
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
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OViTAD: Optimized Vision Transformer to Predict Various Stages of Alzheimer's Disease Using Resting-State fMRI and Structural MRI Data. Brain Sci 2023; 13:brainsci13020260. [PMID: 36831803 PMCID: PMC9954686 DOI: 10.3390/brainsci13020260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 01/19/2023] [Accepted: 02/01/2023] [Indexed: 02/09/2023] Open
Abstract
Advances in applied machine learning techniques for neuroimaging have encouraged scientists to implement models to diagnose brain disorders such as Alzheimer's disease at early stages. Predicting the exact stage of Alzheimer's disease is challenging; however, complex deep learning techniques can precisely manage this. While successful, these complex architectures are difficult to interrogate and computationally expensive. Therefore, using novel, simpler architectures with more efficient pattern extraction capabilities, such as transformers, is of interest to neuroscientists. This study introduced an optimized vision transformer architecture to predict the group membership by separating healthy adults, mild cognitive impairment, and Alzheimer's brains within the same age group (>75 years) using resting-state functional (rs-fMRI) and structural magnetic resonance imaging (sMRI) data aggressively preprocessed by our pipeline. Our optimized architecture, known as OViTAD is currently the sole vision transformer-based end-to-end pipeline and outperformed the existing transformer models and most state-of-the-art solutions. Our model achieved F1-scores of 97%±0.0 and 99.55%±0.39 from the testing sets for the rs-fMRI and sMRI modalities in the triple-class prediction experiments. Furthermore, our model reached these performances using 30% fewer parameters than a vanilla transformer. Furthermore, the model was robust and repeatable, producing similar estimates across three runs with random data splits (we reported the averaged evaluation metrics). Finally, to challenge the model, we observed how it handled increasing noise levels by inserting varying numbers of healthy brains into the two dementia groups. Our findings suggest that optimized vision transformers are a promising and exciting new approach for neuroimaging applications, especially for Alzheimer's disease prediction.
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Zhou K, Piao S, Liu X, Luo X, Chen H, Xiang R, Geng D. A novel cascade machine learning pipeline for Alzheimer's disease identification and prediction. Front Aging Neurosci 2023; 14:1073909. [PMID: 36726800 PMCID: PMC9884698 DOI: 10.3389/fnagi.2022.1073909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 12/30/2022] [Indexed: 01/19/2023] Open
Abstract
Introduction Alzheimer's disease (AD) is a progressive and irreversible brain degenerative disorder early. Among all diagnostic strategies, hippocampal atrophy is considered a promising diagnostic method. In order to proactively detect patients with early Alzheimer's disease, we built an Alzheimer's segmentation and classification (AL-SCF) pipeline based on machine learning. Methods In our study, we collected coronal T1 weighted images that include 187 patients with AD and 230 normal controls (NCs). Our pipeline began with the segmentation of the hippocampus by using a modified U2-net. Subsequently, we extracted 851 radiomics features and selected 37 features most relevant to AD by the Hierarchical clustering method and Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. At last, four classifiers were implemented to distinguish AD from NCs, and the performance of the models was evaluated by accuracy, specificity, sensitivity, and area under the curve. Results Our proposed pipeline showed excellent discriminative performance of classification with AD vs NC in the training set (AUC=0.97, 95% CI: (0.96-0.98)). The model was also verified in the validation set with Dice=0.93 for segmentation and accuracy=0.95 for classification. Discussion The AL-SCF pipeline can automate the process from segmentation to classification, which may assist doctors with AD diagnosis and develop individualized medical plans for AD in clinical practice.
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Affiliation(s)
- Kun Zhou
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Sirong Piao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Xiao Liu
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China
| | - Xiao Luo
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Hongyi Chen
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Rui Xiang
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Daoying Geng
- Academy for Engineering and Technology, Fudan University, Shanghai, China,Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China,*Correspondence: Daoying Geng,
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Garg N, Choudhry MS, Bodade RM. A review on Alzheimer's disease classification from normal controls and mild cognitive impairment using structural MR images. J Neurosci Methods 2023; 384:109745. [PMID: 36395961 DOI: 10.1016/j.jneumeth.2022.109745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 10/04/2022] [Accepted: 11/11/2022] [Indexed: 11/16/2022]
Abstract
Alzheimer's disease (AD) is an irreversible neurodegenerative brain disorder that degrades the memory and cognitive ability in elderly people. The main reason for memory loss and reduction in cognitive ability is the structural changes in the brain that occur due to neuronal loss. These structural changes are most conspicuous in the hippocampus, cortex, and grey matter and can be assessed by using neuroimaging techniques viz. Positron Emission Tomography (PET), structural Magnetic Resonance Imaging (MRI) and functional MRI (fMRI), etc. Out of these neuroimaging techniques, structural MRI has evolved as the best technique as it indicates the best soft tissue contrast and high spatial resolution which is important for AD detection. Currently, the focus of researchers is on predicting the conversion of Mild Cognitive Impairment (MCI) into AD. MCI represents the transition state between expected cognitive changes with normal aging and Alzheimer's disease. Not every MCI patient progresses into Alzheimer's disease. MCI can develop into stable MCI (sMCI, patients are called non-converters) or into progressive MCI (pMCI, patients are diagnosed as MCI converters). This paper discusses the prognosis of MCI to AD conversion and presents a review of structural MRI-based studies for AD detection. AD detection framework includes feature extraction, feature selection, and classification process. This paper reviews the studies for AD detection based on different feature extraction methods and machine learning algorithms for classification. The performance of various feature extraction methods has been compared and it has been observed that the wavelet transform-based feature extraction method would give promising results for AD classification. The present study indicates that researchers are successful in classifying AD from Normal Controls (NrmC) but, it still requires a lot of work to be done for MCI/ NrmC and MCI/AD, which would help in detecting AD at its early stage.
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Affiliation(s)
- Neha Garg
- Delhi Technological University, Department of Electronics and Communication, Delhi 110042, India.
| | - Mahipal Singh Choudhry
- Delhi Technological University, Department of Electronics and Communication, Delhi 110042, India.
| | - Rajesh M Bodade
- Military College of Telecommunication Engineering (MCTE), Mhow, Indore 453441, Madhya Pradesh, India.
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13
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M Arabi E, S Ahmed K, S Mohra A. Advanced Diagnostic Technique for Alzheimer's Disease using MRI Top-Ranked Volume and Surface-based Features. J Biomed Phys Eng 2022; 12:569-582. [PMID: 36569569 PMCID: PMC9759646 DOI: 10.31661/jbpe.v0i0.2112-1440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 03/20/2022] [Indexed: 12/05/2022]
Abstract
Background Alzheimer's disease (AD) is the most dominant type of dementia that has not been treated completely yet. Few Alzheimer's patients are correctly diagnosed on time. Therefore, diagnostic tools are needed for better and more efficient diagnoses. Objective This study aimed to develop an efficient automated method to differentiate Alzheimer's patients from normal elderly and present the essential features with accurate Alzheimer's diagnosis. Material and Methods In this analytical study, 154 Magnetic Resonance Imaging (MRI) scans were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, preprocessed, and normalized by the head size for extracting features (volume, cortical thickness, Sulci depth, and Gyrification Index Features (GIF). Relief-F algorithm, t-test, and one way-ANOVA were used for feature ranking to obtain the most effective features representing the AD for the classification process. Finally, in the classification step, four classifiers were used with 10 folds cross-validation as follows: Gaussian Support Vector Machine (GSVM), Linear Support Vector Machine (LSVM), Weighted K-Nearest Neighbors (W-KNN), and Decision Tree algorithm. Results The LSVM classifier and W-KNN produce a testing accuracy of 100% with only seven features. Additionally, GSVM and decision tree produce a testing accuracy of 97.83% and 93.48%, respectively. Conclusion The proposed system represents an automatic and highly accurate AD detection with a few reliable and effective features and minimum time.
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Affiliation(s)
- Esraa M Arabi
- MSc, Department of Electrical Engineering, Benha Faculty of Engineering, Benha University, Benha, Egypt
| | - Khaled S Ahmed
- PhD, Department of Electrical Engineering, Benha Faculty of Engineering, Benha University, Benha, Egypt
| | - Ashraf S Mohra
- PhD, Department of Electrical Engineering, Benha Faculty of Engineering, Benha University, Benha, Egypt
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14
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Thushara A. An efficient Alzheimer's disease prediction based on MEPC-SSC segmentation and momentum geo-transient MLPs. Comput Biol Med 2022; 151:106247. [PMID: 36375415 DOI: 10.1016/j.compbiomed.2022.106247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 10/12/2022] [Accepted: 10/22/2022] [Indexed: 11/06/2022]
Abstract
A decline in cognitive functioning of the brain termed Alzheimer's Disease (AD) is an irremediable progressive brain disorder, which has no corroborated disease-modifying treatment. Therefore, to slow or avoid disease progression, a greater endeavour has been made to develop techniques for earlier detection, particularly at pre-symptomatic stages. To predict AD, several strategies have been developed. Nevertheless, it is still challenging to predict AD by classifying them into AD, Mild Cognitive Impairment (MCI), along with Normal Control (NC) regarding larger features. By utilizing the Momentum Golden Eagle Optimizer-centric Transient Multi-Layer Perceptron network (Momentum GEO-Transient MLP), an effectual AD prediction technique has been proposed to trounce the aforementioned issues. Firstly, the input images are supplied for post-processing. In post-processing, by employing Patch Wise L1 Norm (PWL1N), the image resizing along with noise removal is engendered. Then, by utilizing Truncate Intensity Based Operation (TIBO) from the post-processed images, the unwanted brain parts are taken away. Next, the skull-stripped images are pre-processed. In this, by deploying Carnot Cycle Entropy-centric Global and Local technique (c2EBGAL), the images are normalized along with ameliorated. Afterward, by implementing Modified Emperor Penguins Colony-centered Sparse Subspace Clustering (MEPC-SSC), the pre-processed images are segmented. Then, for extracting the features, the segmented images are utilized; subsequently, the features being extracted are fed to the Momentum GEO-Transient MLPs.For transferring images fromMRI into more compact higher-level features, this system is wielded for fusing features from diverse layers. The parameters, which minimize the computation complexity, are decreased. For AD classification, the proposed technique is analogized to the prevailing methodologies regardingaccuracy, sensitivity, specificity et cetera along with acquired enhanced outcomes. Thus, the proposed system is apt for the AD diagnosis.
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Affiliation(s)
- A Thushara
- Department of Computer Science and Engineering, TKM College of Engineering Kollam, APJ Abdul Kalam Technological University, Thiruvananthapuram, India.
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15
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Golovanevsky M, Eickhoff C, Singh R. Multimodal attention-based deep learning for Alzheimer's disease diagnosis. J Am Med Inform Assoc 2022; 29:2014-2022. [PMID: 36149257 PMCID: PMC9667156 DOI: 10.1093/jamia/ocac168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 08/10/2022] [Accepted: 09/21/2022] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE Alzheimer's disease (AD) is the most common neurodegenerative disorder with one of the most complex pathogeneses, making effective and clinically actionable decision support difficult. The objective of this study was to develop a novel multimodal deep learning framework to aid medical professionals in AD diagnosis. MATERIALS AND METHODS We present a Multimodal Alzheimer's Disease Diagnosis framework (MADDi) to accurately detect the presence of AD and mild cognitive impairment (MCI) from imaging, genetic, and clinical data. MADDi is novel in that we use cross-modal attention, which captures interactions between modalities-a method not previously explored in this domain. We perform multi-class classification, a challenging task considering the strong similarities between MCI and AD. We compare with previous state-of-the-art models, evaluate the importance of attention, and examine the contribution of each modality to the model's performance. RESULTS MADDi classifies MCI, AD, and controls with 96.88% accuracy on a held-out test set. When examining the contribution of different attention schemes, we found that the combination of cross-modal attention with self-attention performed the best, and no attention layers in the model performed the worst, with a 7.9% difference in F1-scores. DISCUSSION Our experiments underlined the importance of structured clinical data to help machine learning models contextualize and interpret the remaining modalities. Extensive ablation studies showed that any multimodal mixture of input features without access to structured clinical information suffered marked performance losses. CONCLUSION This study demonstrates the merit of combining multiple input modalities via cross-modal attention to deliver highly accurate AD diagnostic decision support.
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Affiliation(s)
- Michal Golovanevsky
- Department of Computer Science, Brown University, Providence, Rhode Island, USA
| | - Carsten Eickhoff
- Department of Computer Science, Brown University, Providence, Rhode Island, USA
- Center for Biomedical Informatics, Brown University, Providence, Rhode Island, USA
| | - Ritambhara Singh
- Department of Computer Science, Brown University, Providence, Rhode Island, USA
- Center for Computational Molecular Biology, Brown University, Providence, Rhode Island, USA
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Multi-class classification of Alzheimer’s disease through distinct neuroimaging computational approaches using Florbetapir PET scans. EVOLVING SYSTEMS 2022. [DOI: 10.1007/s12530-022-09467-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/07/2022]
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17
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Shastry KA, Vijayakumar V, V MKM, B A M, B N C. Deep Learning Techniques for the Effective Prediction of Alzheimer’s Disease: A Comprehensive Review. Healthcare (Basel) 2022; 10:healthcare10101842. [PMID: 36292289 PMCID: PMC9601959 DOI: 10.3390/healthcare10101842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 09/15/2022] [Accepted: 09/15/2022] [Indexed: 11/23/2022] Open
Abstract
“Alzheimer’s disease” (AD) is a neurodegenerative disorder in which the memory shrinks and neurons die. “Dementia” is described as a gradual decline in mental, psychological, and interpersonal qualities that hinders a person’s ability to function autonomously. AD is the most common degenerative brain disease. Among the first signs of AD are missing recent incidents or conversations. “Deep learning” (DL) is a type of “machine learning” (ML) that allows computers to learn by doing, much like people do. DL techniques can attain cutting-edge precision, beating individuals in certain cases. A large quantity of tagged information with multi-layered “neural network” architectures is used to perform analysis. Because significant advancements in computed tomography have resulted in sizable heterogeneous brain signals, the use of DL for the timely identification as well as automatic classification of AD has piqued attention lately. With these considerations in mind, this paper provides an in-depth examination of the various DL approaches and their implementations for the identification and diagnosis of AD. Diverse research challenges are also explored, as well as current methods in the field.
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Affiliation(s)
- K Aditya Shastry
- Department of Information Science and Engineering, Nitte Meenakshi Institute of Technology, Bangalore 560064, India
- Correspondence: (K.A.S.); (V.V.)
| | - V Vijayakumar
- School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW 2052, Australia
- School of NUOVOS, Ajeenkya D Y Patil University, Pune 412105, India
- Swiss School of Business and Management, 1213 Geneva, Switzerland
- Correspondence: (K.A.S.); (V.V.)
| | - Manoj Kumar M V
- Department of Information Science and Engineering, Nitte Meenakshi Institute of Technology, Bangalore 560064, India
| | - Manjunatha B A
- Department of Information Science and Engineering, Nitte Meenakshi Institute of Technology, Bangalore 560064, India
| | - Chandrashekhar B N
- Department of Information Science and Engineering, Nitte Meenakshi Institute of Technology, Bangalore 560064, India
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18
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Liu R, Li G, Gao M, Cai W, Ning X. Large Margin and Local Structure Preservation Sparse Representation Classifier for Alzheimer’s Magnetic Resonance Imaging Classification. Front Aging Neurosci 2022; 14:916020. [PMID: 35693338 PMCID: PMC9177229 DOI: 10.3389/fnagi.2022.916020] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 05/09/2022] [Indexed: 11/15/2022] Open
Abstract
Alzheimer’s disease (AD) is a progressive dementia in which the brain shrinks as the disease progresses. The use of machine learning and brain magnetic resonance imaging (MRI) for the early diagnosis of AD has a high probability of clinical value and social significance. Sparse representation classifier (SRC) is widely used in MRI image classification. However, the traditional SRC only considers the reconstruction error and classification error of the dictionary, and does not consider the global and local structural information between images, which results in unsatisfactory classification performance. Therefore, a large margin and local structure preservation sparse representation classifier (LMLS-SRC) is developed in this manuscript. The LMLS-SRC algorithm uses the classification large margin term based on the representation coefficient, which results in compactness between representation coefficients of the same class and a large margin between representation coefficients of different classes. The LMLS-SRC algorithm uses local structure preservation term to inherit the manifold structure of the original data. In addition, the LMLS-SRC algorithm imposes the ℓ2,1-norm on the representation coefficients to enhance the sparsity and robustness of the model. Experiments on the KAGGLE Alzheimer’s dataset show that the LMLS-SRC algorithm can effectively diagnose non AD, moderate AD, mild AD, and very mild AD.
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Affiliation(s)
- Runmin Liu
- College of Sports Engineering and Information Technology, Wuhan Sports University, Wuhan, China
| | - Guangjun Li
- College of Sports Engineering and Information Technology, Wuhan Sports University, Wuhan, China
- *Correspondence: Guangjun Li,
| | - Ming Gao
- College of Sports Science and Technology, Wuhan Sports University, Wuhan, China
| | - Weiwei Cai
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China
- AiTech Artificial Intelligence Research Institute, Changsha, China
| | - Xin Ning
- Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
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19
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Hydraulic Tests of the PZ0 Gear Micropump and the Importance Rank of Its Design and Operating Parameters. ENERGIES 2022. [DOI: 10.3390/en15093068] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
This article presents the results of hydraulic tests performed for a prototype gear micropump of a new design. The development of modern hydraulic systems is following two directions: the integration of hydraulic and electronic engineering and a reduction in the mass and the dimensions of the system. The scope of this research involved identifying static characteristics of the prototype gear pump, i.e., its efficiency, torque and power. The tests were performed for PZ0 pumps with the following specific deliveries (which are their design parameters), 0.25 cm3/rev, 0.315 cm3/rev, 0.5 cm3/rev, 0.8 cm3/rev, and 1.0 cm3/rev—with two different types of hydraulic oils, Azolla ZS 22 and HL 68. The operating parameters included the rotational speed, the discharge pressure and the flow rate. The obtained research results suggest an application of the decision tree induction-based classification method for identifying the most important design and operating parameters and their values influencing total efficiency. In addition, the article investigates the influence of rotational speed and specific delivery on total efficiency. The total efficiency translates indirectly into the energy consumption of the solution. The criterion of energy consumption is a key parameter for ecological and economical reasons, related mainly to future operating costs.
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20
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Khasawneh AM, Bukhari A, Al-Khasawneh MA. Early Detection of Medical Image Analysis by Using Machine Learning Method. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:3041811. [PMID: 38170039 PMCID: PMC10761224 DOI: 10.1155/2022/3041811] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Revised: 02/02/2022] [Accepted: 02/07/2022] [Indexed: 01/05/2024]
Abstract
We develop effective medical image classification techniques, with an emphasis on histopathology and magnetic resonance imaging (MRI). The trainer utilized the curriculum as a starting point for a set of data and a restricted number of samples, and we used it as a starting point for a set of data. As calibrating a machine learning model is difficult, we used alternative methods as unsupervised feature extracts or weight-conditioning factors for identifying pathological histology pictures. As a result, the pretrained models will be trained on 3-channel RGB pictures, while the MRI sample has more slices. To alter the working model using the MRI data, the convolutional neural network (CNN) must be fine-tuned. Pretrained models are placed and then used as feature snippets. However, there is a scarcity of well-done medical photos, making training machine learning models a difficult endeavor to begin with. In any case, data augmentation aids in the generation of sufficient training samples; however, it is unclear if data augmentation aids in the prediction of unknown data samples. As a result, we fine-tuned machine learning models without using any additional data. Furthermore, rather than utilizing a standard machine learning classifier for the MRI data, we created a unique CNN that uses both 3D shear descriptors and deep features as input. This custom network identifies the MRI sample after processing our representation of the characteristics from beginning to end. On the hidden MRI dataset, our bespoke CNN outperforms traditional machine learning. Our CNN model is less prone to overfitting as a result of this. Furthermore, we have given cutting-edge outcomes employing machine learning.
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Affiliation(s)
| | - Amal Bukhari
- College of Computer Science and Engineering, University of Jeddah, Saudi Arabia
| | - Mahmoud Ahmad Al-Khasawneh
- School of Information Technology, Skyline University College, University City Sharjah, 1797 Sharjah, UAE
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21
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Zhao Y, Li L. Multimodal data integration via mediation analysis with high-dimensional exposures and mediators. Hum Brain Mapp 2022; 43:2519-2533. [PMID: 35129252 PMCID: PMC9057105 DOI: 10.1002/hbm.25800] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 01/06/2022] [Accepted: 01/23/2022] [Indexed: 12/28/2022] Open
Abstract
Motivated by an imaging proteomics study for Alzheimer's disease (AD), in this article, we propose a mediation analysis approach with high‐dimensional exposures and high‐dimensional mediators to integrate data collected from multiple platforms. The proposed method combines principal component analysis with penalized least squares estimation for a set of linear structural equation models. The former reduces the dimensionality and produces uncorrelated linear combinations of the exposure variables, whereas the latter achieves simultaneous path selection and effect estimation while allowing the mediators to be correlated. Applying the method to the AD data identifies numerous interesting protein peptides, brain regions, and protein–structure–memory paths, which are in accordance with and also supplement existing findings of AD research. Additional simulations further demonstrate the effective empirical performance of the method.
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Affiliation(s)
- Yi Zhao
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Lexin Li
- Department of Biostatistics and Epidemiology, University of California, Berkeley, Berkeley, California, USA
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22
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Machine learning techniques for diagnosis of alzheimer disease, mild cognitive disorder, and other types of dementia. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103293] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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Jayaram R, Prabakaran S. Onboard disease prediction and rehabilitation monitoring on secure edge-cloud integrated privacy preserving healthcare system. EGYPTIAN INFORMATICS JOURNAL 2021. [DOI: 10.1016/j.eij.2020.12.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Amini M, Pedram MM, Moradi A, Jamshidi M, Ouchani M. Single and Combined Neuroimaging Techniques for Alzheimer's Disease Detection. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:9523039. [PMID: 34335726 PMCID: PMC8292054 DOI: 10.1155/2021/9523039] [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: 04/21/2021] [Revised: 06/04/2021] [Accepted: 06/30/2021] [Indexed: 02/07/2023]
Abstract
Alzheimer's disease (AD) consists of the gradual process of decreasing volume and quality of neuron connection in the brain, which consists of gradual synaptic integrity and loss of cognitive functions. In recent years, there has been significant attention in AD classification and early detection with machine learning algorithms. There are different neuroimaging techniques for capturing data and using it for the classification task. Input data as images will help machine learning models to detect different biomarkers for AD classification. This marker has a more critical role for AD detection than other diseases because beta-amyloid can extract complex structures with some metal ions. Most researchers have focused on using 3D and 4D convolutional neural networks for AD classification due to reasonable amounts of data. Also, combination neuroimaging techniques like functional magnetic resonance imaging and positron emission tomography for AD detection have recently gathered much attention. However, gathering a combination of data can be expensive, complex, and tedious. For time consumption reasons, most patients prefer to throw one of the neuroimaging techniques. So, in this review article, we have surveyed different research studies with various neuroimaging techniques and ML methods to see the effect of using combined data as input. The result has shown that the use of the combination method would increase the accuracy of AD detection. Also, according to the sensitivity metrics from different machine learning methods, MRI and fMRI showed promising results.
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Affiliation(s)
- Morteza Amini
- Department of Cognitive Modeling, Institute for Cognitive Science Studies, Shahid Beheshti University, Tehran, Iran
| | - Mir Mohsen Pedram
- Department of Electrical and Computer Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
- Department of Cognitive Modeling, Institute for Cognitive Science Studies, Tehran, Iran
| | - Alireza Moradi
- Department of Clinical Psychology, Faculty of Psychology and Educational Science, Kharazmi University, Tehran, Iran
- Department of Cognitive Psychology, Institute for Cognitive Science Studies, Tehran, Iran
| | - Mahdieh Jamshidi
- Department of Mathematical Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran
| | - Mahshad Ouchani
- Institute for Cognitive and Brain Science, Shahid Beheshti University, Tehran, Iran
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The Application of Convolutional Neural Network Model in Diagnosis and Nursing of MR Imaging in Alzheimer's Disease. Interdiscip Sci 2021; 14:34-44. [PMID: 34224083 DOI: 10.1007/s12539-021-00450-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 05/07/2021] [Accepted: 06/07/2021] [Indexed: 10/20/2022]
Abstract
The disease Alzheimer is an irrepressible neurologicalbrain disorder. Earlier detection and proper treatment of Alzheimer's disease can help for brain tissue damage prevention. The study was intended to explore the segmentation effects of convolutional neural network (CNN) model on Magnetic Resonance (MR) imaging for Alzheimer's diagnosis and nursing. Specifically, 18 Alzheimer's patients admitted to Indira Gandhi Medical College (IGMC) hospital were selected as the experimental group, with 18 healthy volunteers in the Ctrl group. Furthermore, the CNN model was applied to segment the MR imaging of Alzheimer's patients, and its segmentation effects were compared with those of the fully convolutional neural network (FCNN) and support vector machine (SVM) algorithms. It was found that the CNN model demonstrated higher segmentation precision, and the experimental group showed a higher clinical dementia rating (CDR) score and a lower mini-mental state examination (MMSE) score (P < 0.05). The size of parahippocompalgyrus and putamen was bigger in the Ctrl (P < 0.05). In experimental group, the amplitude of low-frequency fluctuation (ALFF) was positively correlated with the MMSE score in areas of bilateral cingulum gyri (r = 0.65) and precuneus (r = 0.59). In conclusion, the grey matter structure is damaged in Alzheimer's patients, and hippocampus ALFF and regional homogeneity (ReHo) is involved in the neuronal compensation mechanism of hippocampal damage, and the caregivers should take an active nursing method.
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Abuhmed T, El-Sappagh S, Alonso JM. Robust hybrid deep learning models for Alzheimer’s progression detection. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2020.106688] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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Soleimani M, Vahidi A, Vaseghi B. Two-Dimensional Stockwell Transform and Deep Convolutional Neural Network for Multi-Class Diagnosis of Pathological Brain. IEEE Trans Neural Syst Rehabil Eng 2020; 29:163-172. [PMID: 33237865 DOI: 10.1109/tnsre.2020.3040627] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Since the brain lesion detection and classification is a vital diagnosis task, in this paper, the problem of brain magnetic resonance imaging (MRI) classification is investigated. Recent advantages in machine learning and deep learning allows the researchers to develop the robust computer-aided diagnosis (CAD) tools for classification of brain lesions. Feature extraction is an essential step in any machine learning scheme. Time-frequency analysis methods provide localized information that makes them more attractive for image classification applications. Owing to the advantages of two-dimensional discrete orthonormal Stockwell transform (2D DOST), we propose to use it to extract the efficient features from brain MRIs and obtain the feature matrix. Since there are some irrelevant features, two-directional two-dimensional principal component analysis ((2D)2PCA) is used to reduce the dimension of the feature matrix. Finally, convolution neural networks (CNNs) are designed and trained for MRI classification. Simulation results indicate that the proposed CAD tool outperforms the recently introduced ones and can efficiently diagnose the MRI scans.
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Nanni L, Interlenghi M, Brahnam S, Salvatore C, Papa S, Nemni R, Castiglioni I. Comparison of Transfer Learning and Conventional Machine Learning Applied to Structural Brain MRI for the Early Diagnosis and Prognosis of Alzheimer's Disease. Front Neurol 2020; 11:576194. [PMID: 33250847 PMCID: PMC7674838 DOI: 10.3389/fneur.2020.576194] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Accepted: 09/30/2020] [Indexed: 01/22/2023] Open
Abstract
Alzheimer's Disease (AD) is the most common neurodegenerative disease, with 10% prevalence in the elder population. Conventional Machine Learning (ML) was proven effective in supporting the diagnosis of AD, while very few studies investigated the performance of deep learning and transfer learning in this complex task. In this paper, we evaluated the potential of ensemble transfer-learning techniques, pretrained on generic images and then transferred to structural brain MRI, for the early diagnosis and prognosis of AD, with respect to a fusion of conventional-ML approaches based on Support Vector Machine directly applied to structural brain MRI. Specifically, more than 600 subjects were obtained from the ADNI repository, including AD, Mild Cognitive Impaired converting to AD (MCIc), Mild Cognitive Impaired not converting to AD (MCInc), and cognitively-normal (CN) subjects. We used T1-weighted cerebral-MRI studies to train: (1) an ensemble of five transfer-learning architectures pretrained on generic images; (2) a 3D Convolutional Neutral Network (CNN) trained from scratch on MRI volumes; and (3) a fusion of two conventional-ML classifiers derived from different feature extraction/selection techniques coupled to SVM. The AD-vs-CN, MCIc-vs-CN, MCIc-vs-MCInc comparisons were investigated. The ensemble transfer-learning approach was able to effectively discriminate AD from CN with 90.2% AUC, MCIc from CN with 83.2% AUC, and MCIc from MCInc with 70.6% AUC, showing comparable or slightly lower results with the fusion of conventional-ML systems (AD from CN with 93.1% AUC, MCIc from CN with 89.6% AUC, and MCIc from MCInc with AUC in the range of 69.1-73.3%). The deep-learning network trained from scratch obtained lower performance than either the fusion of conventional-ML systems and the ensemble transfer-learning, due to the limited sample of images used for training. These results open new prospective on the use of transfer learning combined with neuroimages for the automatic early diagnosis and prognosis of AD, even if pretrained on generic images.
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Affiliation(s)
- Loris Nanni
- Department of Information Engineering, University of Padua, Padua, Italy
| | - Matteo Interlenghi
- Institute of Molecular Bioimaging and Physiology, National Research Council of Italy (IBFM-CNR), Milan, Italy
| | - Sheryl Brahnam
- Department of IT and Cybersecurity, Missouri State University, Springfield, MO, United States
| | - Christian Salvatore
- Department of Science, Technology and Society, Scuola Universitaria Superiore IUSS Pavia, Pavia, Italy
- DeepTrace Technologies S.R.L., Milan, Italy
| | - Sergio Papa
- Centro Diagnostico Italiano S.p.A., Milan, Italy
| | | | - Isabella Castiglioni
- Institute of Molecular Bioimaging and Physiology, National Research Council of Italy (IBFM-CNR), Milan, Italy
- Department of Physics “G. Occhialini”, University of Milano Bicocca, Milan, Italy
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Naik B, Mehta A, Shah M. Denouements of machine learning and multimodal diagnostic classification of Alzheimer's disease. Vis Comput Ind Biomed Art 2020; 3:26. [PMID: 33151420 PMCID: PMC7642580 DOI: 10.1186/s42492-020-00062-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 10/16/2020] [Indexed: 12/19/2022] Open
Abstract
Alzheimer's disease (AD) is the most common type of dementia. The exact cause and treatment of the disease are still unknown. Different neuroimaging modalities, such as magnetic resonance imaging (MRI), positron emission tomography, and single-photon emission computed tomography, have played a significant role in the study of AD. However, the effective diagnosis of AD, as well as mild cognitive impairment (MCI), has recently drawn large attention. Various technological advancements, such as robots, global positioning system technology, sensors, and machine learning (ML) algorithms, have helped improve the diagnostic process of AD. This study aimed to determine the influence of implementing different ML classifiers in MRI and analyze the use of support vector machines with various multimodal scans for classifying patients with AD/MCI and healthy controls. Conclusions have been drawn in terms of employing different classifier techniques and presenting the optimal multimodal paradigm for the classification of AD.
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Affiliation(s)
- Binny Naik
- Department of Computer Engineering, Indus University, Ahmedabad, Gujarat, 382115, India
| | - Ashir Mehta
- Department of Computer Engineering, Indus University, Ahmedabad, Gujarat, 382115, India
| | - Manan Shah
- Department of Chemical Engineering, School of Technology, Pandit Deendayal Petroleum University, Gandhinagar, Gujarat, 382007, India.
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Ali AM, Kunugi H. Royal Jelly as an Intelligent Anti-Aging Agent-A Focus on Cognitive Aging and Alzheimer's Disease: A Review. Antioxidants (Basel) 2020; 9:E937. [PMID: 33003559 PMCID: PMC7601550 DOI: 10.3390/antiox9100937] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2020] [Revised: 09/24/2020] [Accepted: 09/24/2020] [Indexed: 02/08/2023] Open
Abstract
The astronomical increase of the world's aged population is associated with the increased prevalence of neurodegenerative diseases, heightened disability, and extremely high costs of care. Alzheimer's Disease (AD) is a widespread, age-related, multifactorial neurodegenerative disease that has enormous social and financial drawbacks worldwide. The unsatisfactory outcomes of available AD pharmacotherapy necessitate the search for alternative natural resources that can target various the underlying mechanisms of AD pathology and reduce disease occurrence and/or progression. Royal jelly (RJ) is the main food of bee queens; it contributes to their fertility, long lifespan, and memory performance. It represents a potent nutraceutical with various pharmacological properties, and has been used in a number of preclinical studies to target AD and age-related cognitive deterioration. To understand the mechanisms through which RJ affects cognitive performance both in natural aging and AD, we reviewed the literature, elaborating on the metabolic, molecular, and cellular mechanisms that mediate its anti-AD effects. Preclinical findings revealed that RJ acts as a multidomain cognitive enhancer that can restore cognitive performance in aged and AD models. It promotes brain cell survival and function by targeting multiple adversities in the neuronal microenvironment such as inflammation, oxidative stress, mitochondrial alterations, impaired proteostasis, amyloid-β toxicity, Ca excitotoxicity, and bioenergetic challenges. Human trials using RJ in AD are limited in quantity and quality. Here, the limitations of RJ-based treatment strategies are discussed, and directions for future studies examining the effect of RJ in cognitively impaired subjects are noted.
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Affiliation(s)
- Amira Mohammed Ali
- Department of Mental Disorder Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo 187-0031, Japan;
- Department of Psychiatric Nursing and Mental Health, Faculty of Nursing, Alexandria University, Alexandria 21527, Egypt
| | - Hiroshi Kunugi
- Department of Mental Disorder Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo 187-0031, Japan;
- Department of Psychiatry, Teikyo University School of Medicine, Tokyo 173-8605, Japan
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Battineni G, Chintalapudi N, Amenta F. Machine learning in medicine: Performance calculation of dementia prediction by support vector machines (SVM). INFORMATICS IN MEDICINE UNLOCKED 2019. [DOI: 10.1016/j.imu.2019.100200] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
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