Special Issue "Machine Learning Approaches for Neurodegenerative Diseases Diagnosis"

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Prof. Dr. Markos G. Tsipouras
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Guest Editor
Department of Electrical and Computer Engineering, University of Western Macedonia, GR50100 Kozani, Greece
Interests: biomedical signal processing; EEG signal processing; data mining; decision support and medical expert systems; data modelling; computational intelligence; image processing; biomedical engineering
Special Issues and Collections in MDPI journals
Prof. Dr. Alexandros T. Tzallas
E-Mail Website
Guest Editor
School of Informatics and Telecommunications, Department of Informatics and Telecommunications, University of Ioannina, Kostakioi, GR-47100 Arta, Greece
Interests: biomedical signal processing; EEG signal processing; brain computer interface systems; wearable devices; image processing; decision support and medical expert systems; biomedical engineering
Special Issues and Collections in MDPI journals
Prof. Dr. Nikolaos Giannakeas
E-Mail Website
Guest Editor
School of Informatics and Telecommunications, Department of Informatics and Telecommunications, University of Ioannina, Kostakioi, GR-47100 Arta, Greece
Interests: biomedical image and signal processing; EEG signal processing; brain computer interface systems; wearable devices; bioinformatics; machine learning; biomedical engineering
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Dr. Katerina D. Tzimourta
E-Mail Website
Guest Editor
Laboratory of Medical Physics, School of Medicine, University of Ioannina, Ioannina, Greece
Interests: EEG signal processing; brain computer interface; machine learning; EEG wearable devices
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Neurodegenerative diseases, such as Parkinson’s disease, Alzheimer’s disease and other types of dementia, Huntington’s disease, motor neuron disease and Prion disease, are progressive brain disorders with great prevalence in the general population. It is estimated that around 10 million people live with Parkinson’s disease worldwide (Marras C., et al. 2018), and a recent report of 2018 (Prince M., et al. 2015) recorded that 33 million people suffer from Alzheimer’s disease. These disorders afflict both the individual and the caregivers, and have a great impact on the global health economy (Xu J., et al. 2017).

Several diagnostic procedures are performed for differential diagnoses, including brain imaging, EEG analysis, molecular analysis, and cognitive, psychological and physical examination (Tzimourta K.D., et al. 2019). The research focuses on novel machine learning and artificial intelligence approaches, to elucidate the pathogenesis of neurodegenerative disorders and provide early diagnosis, aiming to develop effective treatments, improve the quality of the patient’s life and increase life expectancy (Myszczynska M.A., et al. 2020).

The current Special Issue addresses the urgent need for novel strategies and advanced machine learning methods, to provide the foundation for beyond state-of-the-art diagnostic procedures for neurodegenerative diseases.

Dr. Markos G. Tsipouras
Dr. Alexandros T. Tzallas
Prof. Dr. Nikolaos Giannakeas
Dr. Katerina D. Tzimourta
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • neurodegenerative diseases
  • Alzheimer’s disease
  • Parkinson’s disease
  • dementia
  • EEG
  • neuroimaging
  • machine learning
  • deep learning
  • wearable devices
  • neurodegenerative diseases diagnosis
  • neurodegenerative diseases staging
  • neurodegenerative disease progression
  • biomarkers
  • quantitative EEG
  • mini-mental state examination

Published Papers (6 papers)

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Research

Article
Assessing Parkinson’s Disease at Scale Using Telephone-Recorded Speech: Insights from the Parkinson’s Voice Initiative
Diagnostics 2021, 11(10), 1892; https://doi.org/10.3390/diagnostics11101892 - 14 Oct 2021
Viewed by 282
Abstract
Numerous studies have reported on the high accuracy of using voice tasks for the remote detection and monitoring of Parkinson’s Disease (PD). Most of these studies, however, report findings on a small number of voice recordings, often collected under acoustically controlled conditions, and [...] Read more.
Numerous studies have reported on the high accuracy of using voice tasks for the remote detection and monitoring of Parkinson’s Disease (PD). Most of these studies, however, report findings on a small number of voice recordings, often collected under acoustically controlled conditions, and therefore cannot scale at large without specialized equipment. In this study, we aimed to evaluate the potential of using voice as a population-based PD screening tool in resource-constrained settings. Using the standard telephone network, we processed 11,942 sustained vowel /a/ phonations from a US-English cohort comprising 1078 PD and 5453 control participants. We characterized each phonation using 304 dysphonia measures to quantify a range of vocal impairments. Given that this is a highly unbalanced problem, we used the following strategy: we selected a balanced subset (n = 3000 samples) for training and testing using 10-fold cross-validation (CV), and the remaining (unbalanced held-out dataset, n = 8942) samples for further model validation. Using robust feature selection methods we selected 27 dysphonia measures to present into a radial-basis-function support vector machine and demonstrated differentiation of PD participants from controls with 67.43% sensitivity and 67.25% specificity. These findings could help pave the way forward toward the development of an inexpensive, remote, and reliable diagnostic support tool for PD using voice as a digital biomarker. Full article
(This article belongs to the Special Issue Machine Learning Approaches for Neurodegenerative Diseases Diagnosis)
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Article
Alzheimer’s Disease and Frontotemporal Dementia: A Robust Classification Method of EEG Signals and a Comparison of Validation Methods
Diagnostics 2021, 11(8), 1437; https://doi.org/10.3390/diagnostics11081437 - 09 Aug 2021
Viewed by 723
Abstract
Dementia is the clinical syndrome characterized by progressive loss of cognitive and emotional abilities to a degree severe enough to interfere with daily functioning. Alzheimer’s disease (AD) is the most common neurogenerative disorder, making up 50–70% of total dementia cases. Another dementia type [...] Read more.
Dementia is the clinical syndrome characterized by progressive loss of cognitive and emotional abilities to a degree severe enough to interfere with daily functioning. Alzheimer’s disease (AD) is the most common neurogenerative disorder, making up 50–70% of total dementia cases. Another dementia type is frontotemporal dementia (FTD), which is associated with circumscribed degeneration of the prefrontal and anterior temporal cortex and mainly affects personality and social skills. With the rapid advancement in electroencephalogram (EEG) sensors, the EEG has become a suitable, accurate, and highly sensitive biomarker for the identification of neuronal and cognitive dynamics in most cases of dementia, such as AD and FTD, through EEG signal analysis and processing techniques. In this study, six supervised machine-learning techniques were compared on categorizing processed EEG signals of AD and FTD cases, to provide an insight for future methods on early dementia diagnosis. K-fold cross validation and leave-one-patient-out cross validation were also compared as validation methods to evaluate their performance for this classification problem. The proposed methodology accuracy scores were 78.5% for AD detection with decision trees and 86.3% for FTD detection with random forests. Full article
(This article belongs to the Special Issue Machine Learning Approaches for Neurodegenerative Diseases Diagnosis)
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Article
Local Pattern Transformation Based Feature Extraction for Recognition of Parkinson’s Disease Based on Gait Signals
Diagnostics 2021, 11(8), 1395; https://doi.org/10.3390/diagnostics11081395 - 01 Aug 2021
Cited by 1 | Viewed by 549
Abstract
Parkinson’s disease (PD) is a neuro-degenerative disorder primarily triggered due to the deterioration of dopamine-producing neurons in the substantia nigra of the human brain. The early detection of Parkinson’s disease can assist in preventing deteriorating health. This paper analyzes human gait signals using [...] Read more.
Parkinson’s disease (PD) is a neuro-degenerative disorder primarily triggered due to the deterioration of dopamine-producing neurons in the substantia nigra of the human brain. The early detection of Parkinson’s disease can assist in preventing deteriorating health. This paper analyzes human gait signals using Local Binary Pattern (LBP) techniques during feature extraction before classification. Supplementary to the LBP techniques, Local Gradient Pattern (LGP), Local Neighbour Descriptive Pattern (LNDP), and Local Neighbour Gradient Pattern (LNGP) were utilized to extract features from gait signals. The statistical features were derived and analyzed, and the statistical Kruskal–Wallis test was carried out for the selection of an optimal feature set. The classification was then carried out by an Artificial Neural Network (ANN) for the identified feature set. The proposed Symmetrically Weighted Local Neighbour Gradient Pattern (SWLNGP) method achieves a better performance, with 96.28% accuracy, 96.57% sensitivity, and 95.94% specificity. This study suggests that SWLNGP could be an effective feature extraction technique for the recognition of Parkinsonian gait. Full article
(This article belongs to the Special Issue Machine Learning Approaches for Neurodegenerative Diseases Diagnosis)
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Article
Analysis of Features of Alzheimer’s Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 Network
Diagnostics 2021, 11(6), 1071; https://doi.org/10.3390/diagnostics11061071 - 10 Jun 2021
Cited by 4 | Viewed by 6247
Abstract
One of the first signs of Alzheimer’s disease (AD) is mild cognitive impairment (MCI), in which there are small variants of brain changes among the intermediate stages. Although there has been an increase in research into the diagnosis of AD in its early [...] Read more.
One of the first signs of Alzheimer’s disease (AD) is mild cognitive impairment (MCI), in which there are small variants of brain changes among the intermediate stages. Although there has been an increase in research into the diagnosis of AD in its early levels of developments lately, brain changes, and their complexity for functional magnetic resonance imaging (fMRI), makes early detection of AD difficult. This paper proposes a deep learning-based method that can predict MCI, early MCI (EMCI), late MCI (LMCI), and AD. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) fMRI dataset consisting of 138 subjects was used for evaluation. The finetuned ResNet18 network achieved a classification accuracy of 99.99%, 99.95%, and 99.95% on EMCI vs. AD, LMCI vs. AD, and MCI vs. EMCI classification scenarios, respectively. The proposed model performed better than other known models in terms of accuracy, sensitivity, and specificity. Full article
(This article belongs to the Special Issue Machine Learning Approaches for Neurodegenerative Diseases Diagnosis)
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Article
A Comprehensive Machine Learning Framework for the Exact Prediction of the Age of Onset in Familial and Sporadic Alzheimer’s Disease
Diagnostics 2021, 11(5), 887; https://doi.org/10.3390/diagnostics11050887 - 17 May 2021
Cited by 1 | Viewed by 778
Abstract
Machine learning (ML) algorithms are widely used to develop predictive frameworks. Accurate prediction of Alzheimer’s disease (AD) age of onset (ADAOO) is crucial to investigate potential treatments, follow-up, and therapeutic interventions. Although genetic and non-genetic factors affecting ADAOO were elucidated by other research [...] Read more.
Machine learning (ML) algorithms are widely used to develop predictive frameworks. Accurate prediction of Alzheimer’s disease (AD) age of onset (ADAOO) is crucial to investigate potential treatments, follow-up, and therapeutic interventions. Although genetic and non-genetic factors affecting ADAOO were elucidated by other research groups and ours, the comprehensive and sequential application of ML to provide an exact estimation of the actual ADAOO, instead of a high-confidence-interval ADAOO that may fall, remains to be explored. Here, we assessed the performance of ML algorithms for predicting ADAOO using two AD cohorts with early-onset familial AD and with late-onset sporadic AD, combining genetic and demographic variables. Performance of ML algorithms was assessed using the root mean squared error (RMSE), the R-squared (R2), and the mean absolute error (MAE) with a 10-fold cross-validation procedure. For predicting ADAOO in familial AD, boosting-based ML algorithms performed the best. In the sporadic cohort, boosting-based ML algorithms performed best in the training data set, while regularization methods best performed for unseen data. ML algorithms represent a feasible alternative to accurately predict ADAOO with little human intervention. Future studies may include predicting the speed of cognitive decline in our cohorts using ML. Full article
(This article belongs to the Special Issue Machine Learning Approaches for Neurodegenerative Diseases Diagnosis)
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Article
Ethical Implications of Alzheimer’s Disease Prediction in Asymptomatic Individuals through Artificial Intelligence
Diagnostics 2021, 11(3), 440; https://doi.org/10.3390/diagnostics11030440 - 04 Mar 2021
Viewed by 852
Abstract
Biomarker-based predictive tests for subjectively asymptomatic Alzheimer’s disease (AD) are utilized in research today. Novel applications of artificial intelligence (AI) promise to predict the onset of AD several years in advance without determining biomarker thresholds. Until now, little attention has been paid to [...] Read more.
Biomarker-based predictive tests for subjectively asymptomatic Alzheimer’s disease (AD) are utilized in research today. Novel applications of artificial intelligence (AI) promise to predict the onset of AD several years in advance without determining biomarker thresholds. Until now, little attention has been paid to the new ethical challenges that AI brings to the early diagnosis in asymptomatic individuals, beyond contributing to research purposes, when we still lack adequate treatment. The aim of this paper is to explore the ethical arguments put forward for AI aided AD prediction in subjectively asymptomatic individuals and their ethical implications. The ethical assessment is based on a systematic literature search. Thematic analysis was conducted inductively of 18 included publications. The ethical framework includes the principles of autonomy, beneficence, non-maleficence, and justice. Reasons for offering predictive tests to asymptomatic individuals are the right to know, a positive balance of the risk-benefit assessment, and the opportunity for future planning. Reasons against are the lack of disease modifying treatment, the accuracy and explicability of AI aided prediction, the right not to know, and threats to social rights. We conclude that there are serious ethical concerns in offering early diagnosis to asymptomatic individuals and the issues raised by the application of AI add to the already known issues. Nevertheless, pre-symptomatic testing should only be offered on request to avoid inflicted harm. We recommend developing training for physicians in communicating AI aided prediction. Full article
(This article belongs to the Special Issue Machine Learning Approaches for Neurodegenerative Diseases Diagnosis)
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