Artificial Intelligence and Pattern Recognition Methods for the Automatic Detection and Evaluation of Neurological Disorders

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: 30 June 2024 | Viewed by 25417

Special Issue Editor


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Guest Editor
1. GITA Lab, Faculty of Engineering, University of Antioquia, Medellín, Colombia
2. Pattern Recognition Lab, Friedrich-Alexander-University Erlangen–Nuremberg, Erlangen, Germany
Interests: computer science; artificial intelligence; signals processing; biomedical engineering; speech processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

This Special Issue focuses on novel studies about neurodegeneration, which is a major problem worldwide. It is estimated that about 50 million people worldwide suffer from neurodegenerative diseases, and the number is expected to increase to 115 million by 2050. This Special Issue is focused on contributions addressing two of the main challenges that exist when studying neurodegeneration: (i) diagnosis and (ii) monitoring.

The development of modern methods in machine learning and pattern recognition has enabled the possibility of performing accurate and non-intrusive detection and monitoring of different diseases considering different sources of information, including speech production, language, movement, gait, handwriting, video, neural activity (EEG, electroenvephalography), and others. The use of information from these biosignals together with the development of classical and/or modern machine learning and deep learning algorithms will be welcome.

This Special Issue will focus on but not be limited to the following topics:

  • Classical and modern machine learning methods to detect and monitor neurodegenerative diseases;
  • Methods to classify different neurodegenerative diseases;
  • Monitoring of disease progression;
  • Evaluation of different treatment strategies, e.g., medication intake, therapy, and others;
  • Non-intrusive evaluation and monitoring of neurodegenerative disorders.

Prof. Dr. Juan Rafael Orozco-Arroyave
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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

  • machine learning
  • neurodegenerative diseases
  • diagnosis
  • detection and evaluation
  • monitoring

Published Papers (11 papers)

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Research

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18 pages, 2702 KiB  
Article
Innovative Strategies for Early Autism Diagnosis: Active Learning and Domain Adaptation Optimization
by Mohammad Shafiul Alam, Elfatih A. A. Elsheikh, F. M. Suliman, Muhammad Mahbubur Rashid and Ahmed Rimaz Faizabadi
Diagnostics 2024, 14(6), 629; https://doi.org/10.3390/diagnostics14060629 - 16 Mar 2024
Viewed by 587
Abstract
The early diagnosis of autism spectrum disorder (ASD) encounters challenges stemming from domain variations in facial image datasets. This study investigates the potential of active learning, particularly uncertainty-based sampling, for domain adaptation in early ASD diagnosis. Our focus is on improving model performance [...] Read more.
The early diagnosis of autism spectrum disorder (ASD) encounters challenges stemming from domain variations in facial image datasets. This study investigates the potential of active learning, particularly uncertainty-based sampling, for domain adaptation in early ASD diagnosis. Our focus is on improving model performance across diverse data sources. Utilizing the Kaggle ASD and YTUIA datasets, we meticulously analyze domain variations and assess transfer learning and active learning methodologies. Two state-of-the-art convolutional neural networks, Xception and ResNet50V2, pretrained on distinct datasets, demonstrate noteworthy accuracies of 95% on Kaggle ASD and 96% on YTUIA, respectively. However, combining datasets results in a modest decline in average accuracy, underscoring the necessity for effective domain adaptation techniques. We employ uncertainty-based active learning to address this, which significantly mitigates the accuracy drop. Xception and ResNet50V2 achieve 80% and 79% accuracy when pretrained on Kaggle ASD and applying active learning on YTUIA, respectively. Our findings highlight the efficacy of uncertainty-based active learning for domain adaptation, showcasing its potential to enhance accuracy and reduce annotation needs in early ASD diagnosis. This study contributes to the growing body of literature on ASD diagnosis methodologies. Future research should delve deeper into refining active learning strategies, ultimately paving the way for more robust and efficient ASD detection tools across diverse datasets. Full article
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31 pages, 5847 KiB  
Article
Hybrid Techniques of Facial Feature Image Analysis for Early Detection of Autism Spectrum Disorder Based on Combined CNN Features
by Bakri Awaji, Ebrahim Mohammed Senan, Fekry Olayah, Eman A. Alshari, Mohammad Alsulami, Hamad Ali Abosaq, Jarallah Alqahtani and Prachi Janrao
Diagnostics 2023, 13(18), 2948; https://doi.org/10.3390/diagnostics13182948 - 14 Sep 2023
Cited by 3 | Viewed by 1872
Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by difficulties in social communication and repetitive behaviors. The exact causes of ASD remain elusive and likely involve a combination of genetic, environmental, and neurobiological factors. Doctors often face challenges in accurately identifying [...] Read more.
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by difficulties in social communication and repetitive behaviors. The exact causes of ASD remain elusive and likely involve a combination of genetic, environmental, and neurobiological factors. Doctors often face challenges in accurately identifying ASD early due to its complex and diverse presentation. Early detection and intervention are crucial for improving outcomes for individuals with ASD. Early diagnosis allows for timely access to appropriate interventions, leading to better social and communication skills development. Artificial intelligence techniques, particularly facial feature extraction using machine learning algorithms, display promise in aiding the early detection of ASD. By analyzing facial expressions and subtle cues, AI models identify patterns associated with ASD features. This study developed various hybrid systems to diagnose facial feature images for an ASD dataset by combining convolutional neural network (CNN) features. The first approach utilized pre-trained VGG16, ResNet101, and MobileNet models. The second approach employed a hybrid technique that combined CNN models (VGG16, ResNet101, and MobileNet) with XGBoost and RF algorithms. The third strategy involved diagnosing ASD using XGBoost and an RF based on features of VGG-16-ResNet101, ResNet101-MobileNet, and VGG16-MobileNet models. Notably, the hybrid RF algorithm that utilized features from the VGG16-MobileNet models demonstrated superior performance, reached an AUC of 99.25%, an accuracy of 98.8%, a precision of 98.9%, a sensitivity of 99%, and a specificity of 99.1%. Full article
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16 pages, 1161 KiB  
Article
Deep Learning and Artificial Intelligence Applied to Model Speech and Language in Parkinson’s Disease
by Daniel Escobar-Grisales, Cristian David Ríos-Urrego and Juan Rafael Orozco-Arroyave
Diagnostics 2023, 13(13), 2163; https://doi.org/10.3390/diagnostics13132163 - 25 Jun 2023
Cited by 3 | Viewed by 1577
Abstract
Parkinson’s disease (PD) is the second most prevalent neurodegenerative disorder in the world, and it is characterized by the production of different motor and non-motor symptoms which negatively affect speech and language production. For decades, the research community has been working on methodologies [...] Read more.
Parkinson’s disease (PD) is the second most prevalent neurodegenerative disorder in the world, and it is characterized by the production of different motor and non-motor symptoms which negatively affect speech and language production. For decades, the research community has been working on methodologies to automatically model these biomarkers to detect and monitor the disease; however, although speech impairments have been widely explored, language remains underexplored despite being a valuable source of information, especially to assess cognitive impairments associated with non-motor symptoms. This study proposes the automatic assessment of PD patients using different methodologies to model speech and language biomarkers. One-dimensional and two-dimensional convolutional neural networks (CNNs), along with pre-trained models such as Wav2Vec 2.0, BERT, and BETO, were considered to classify PD patients vs. Healthy Control (HC) subjects. The first approach consisted of modeling speech and language independently. Then, the best representations from each modality were combined following early, joint, and late fusion strategies. The results show that the speech modality yielded an accuracy of up to 88%, thus outperforming all language representations, including the multi-modal approach. These results suggest that speech representations better discriminate PD patients and HC subjects than language representations. When analyzing the fusion strategies, we observed that changes in the time span of the multi-modal representation could produce a significant loss of information in the speech modality, which was likely linked to a decrease in accuracy in the multi-modal experiments. Further experiments are necessary to validate this claim with other fusion methods using different time spans. Full article
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19 pages, 2104 KiB  
Article
KCS-FCnet: Kernel Cross-Spectral Functional Connectivity Network for EEG-Based Motor Imagery Classification
by Daniel Guillermo García-Murillo, Andrés Marino Álvarez-Meza and Cesar German Castellanos-Dominguez
Diagnostics 2023, 13(6), 1122; https://doi.org/10.3390/diagnostics13061122 - 16 Mar 2023
Cited by 3 | Viewed by 1696
Abstract
This paper uses EEG data to introduce an approach for classifying right and left-hand classes in Motor Imagery (MI) tasks. The Kernel Cross-Spectral Functional Connectivity Network (KCS-FCnet) method addresses these limitations by providing richer spatial-temporal-spectral feature maps, a simpler architecture, and a more [...] Read more.
This paper uses EEG data to introduce an approach for classifying right and left-hand classes in Motor Imagery (MI) tasks. The Kernel Cross-Spectral Functional Connectivity Network (KCS-FCnet) method addresses these limitations by providing richer spatial-temporal-spectral feature maps, a simpler architecture, and a more interpretable approach for EEG-driven MI discrimination. In particular, KCS-FCnet uses a single 1D-convolutional-based neural network to extract temporal-frequency features from raw EEG data and a cross-spectral Gaussian kernel connectivity layer to model channel functional relationships. As a result, the functional connectivity feature map reduces the number of parameters, improving interpretability by extracting meaningful patterns related to MI tasks. These patterns can be adapted to the subject’s unique characteristics. The validation results prove that introducing KCS-FCnet shallow architecture is a promising approach for EEG-based MI classification with the potential for real-world use in brain–computer interface systems. Full article
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22 pages, 5377 KiB  
Article
Syndrome Pattern Recognition Method Using Sensed Patient Data for Neurodegenerative Disease Progression Identification
by Mohd Anjum, Sana Shahab and Yang Yu
Diagnostics 2023, 13(5), 887; https://doi.org/10.3390/diagnostics13050887 - 26 Feb 2023
Viewed by 1647
Abstract
Neurodegenerative diseases are a group of conditions that involve the progressive loss of function of neurons in the brain and spinal cord. These conditions can result in a wide range of symptoms, such as difficulty with movement, speech, and cognition. The causes of [...] Read more.
Neurodegenerative diseases are a group of conditions that involve the progressive loss of function of neurons in the brain and spinal cord. These conditions can result in a wide range of symptoms, such as difficulty with movement, speech, and cognition. The causes of neurodegenerative diseases are poorly understood, but many factors are believed to contribute to the development of these conditions. The most important risk factors include ageing, genetics, abnormal medical conditions, toxins, and environmental exposures. A slow decline in visible cognitive functions characterises the progression of these diseases. If left unattended or unnoticed, disease progression can result in serious issues such as the cessation of motor function or even paralysis. Therefore, early recognition of neurodegenerative diseases is becoming increasingly important in modern healthcare. Many sophisticated artificial intelligence technologies are incorporated into modern healthcare systems for the early recognition of these diseases. This research article introduces a Syndrome-dependent Pattern Recognition Method for the early detection and progression monitoring of neurodegenerative diseases. The proposed method determines the variance between normal and abnormal intrinsic neural connectivity data. The observed data is combined with previous and healthy function examination data to identify the variance. In this combined analysis, deep recurrent learning is exploited by tuning the analysis layer based on variance suppressed by identifying normal and abnormal patterns in the combined analysis. This variance from different patterns is recurrently used to train the learning model for maximising of recognition accuracy. The proposed method achieves 16.77% high accuracy, 10.55% high precision, and 7.69% high pattern verification. It reduces the variance and verification time by 12.08% and 12.02%, respectively. Full article
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31 pages, 15660 KiB  
Article
Clinical Spasticity Assessment Assisted by Machine Learning Methods and Rule-Based Decision
by Jingye Yee, Cheng Yee Low, Natiara Mohamad Hashim, Noor Ayuni Che Zakaria, Khairunnisa Johar, Nurul Atiqah Othman, Hock Hung Chieng and Fazah Akhtar Hanapiah
Diagnostics 2023, 13(4), 739; https://doi.org/10.3390/diagnostics13040739 - 15 Feb 2023
Cited by 1 | Viewed by 1539
Abstract
The Modified Ashworth Scale (MAS) is commonly used to assess spasticity in clinics. The qualitative description of MAS has resulted in ambiguity during spasticity assessment. This work supports spasticity assessment by providing measurement data acquired from wireless wearable sensors, i.e., goniometers, myometers, and [...] Read more.
The Modified Ashworth Scale (MAS) is commonly used to assess spasticity in clinics. The qualitative description of MAS has resulted in ambiguity during spasticity assessment. This work supports spasticity assessment by providing measurement data acquired from wireless wearable sensors, i.e., goniometers, myometers, and surface electromyography sensors. Based on in-depth discussions with consultant rehabilitation physicians, eight (8) kinematic, six (6) kinetic, and four (4) physiological features were extracted from the collected clinical data from fifty (50) subjects. These features were used to train and evaluate the conventional machine learning classifiers, including but not limited to Support Vector Machine (SVM) and Random Forest (RF). Subsequently, a spasticity classification approach combining the decision-making logic of the consultant rehabilitation physicians, SVM, and RF was developed. The empirical results on the unknown test set show that the proposed Logical–SVM–RF classifier outperforms each individual classifier, reporting an accuracy of 91% compared to 56–81% achieved by SVM and RF. A data-driven diagnosis decision contributing to interrater reliability is enabled via the availability of quantitative clinical data and a MAS prediction. Full article
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16 pages, 3795 KiB  
Article
Automated Detection of Seizure Types from the Higher-Order Moments of Maximal Overlap Wavelet Distribution
by Joseph Mathew, Natarajan Sivakumaran and P. A. Karthick
Diagnostics 2023, 13(4), 621; https://doi.org/10.3390/diagnostics13040621 - 08 Feb 2023
Cited by 2 | Viewed by 1260
Abstract
In this work, an attempt has been made to develop an automated system for detecting electroclinical seizures such as tonic-clonic seizures, complex partial seizures, and electrographic seizures (EGSZ) using higher-order moments of scalp electroencephalography (EEG). The scalp EEGs of the publicly available Temple [...] Read more.
In this work, an attempt has been made to develop an automated system for detecting electroclinical seizures such as tonic-clonic seizures, complex partial seizures, and electrographic seizures (EGSZ) using higher-order moments of scalp electroencephalography (EEG). The scalp EEGs of the publicly available Temple University database are utilized in this study. The higher-order moments, namely skewness and kurtosis, are extracted from the temporal, spectral, and maximal overlap wavelet distributions of EEG. The features are computed from overlapping and non-overlapping moving windowing functions. The results show that the wavelet and spectral skewness of EEG is higher in EGSZ than in other types. All the extracted features are found to have significant differences (p < 0.05), except for temporal kurtosis and skewness. A support vector machine with a radial basis kernel designed using maximal overlap wavelet skewness yields a maximum accuracy of 87%. In order to improve the performance, the Bayesian optimization technique is utilized to determine the suitable kernel parameters. The optimized model achieves the highest accuracy of 96% and an MCC of 91% in three-class classification. The study is found to be promising, and it could facilitate the rapid identification process of life-threatening seizures. Full article
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16 pages, 3116 KiB  
Article
A Machine Learning-Based Severity Prediction Tool for the Michigan Neuropathy Screening Instrument
by Fahmida Haque, Mamun B. I. Reaz, Muhammad E. H. Chowdhury, Mohd Ibrahim bin Shapiai, Rayaz A. Malik, Mohammed Alhatou, Syoji Kobashi, Iffat Ara, Sawal H. M. Ali, Ahmad A. A. Bakar and Mohammad Arif Sobhan Bhuiyan
Diagnostics 2023, 13(2), 264; https://doi.org/10.3390/diagnostics13020264 - 11 Jan 2023
Cited by 2 | Viewed by 2211
Abstract
Diabetic sensorimotor polyneuropathy (DSPN) is a serious long-term complication of diabetes, which may lead to foot ulceration and amputation. Among the screening tools for DSPN, the Michigan neuropathy screening instrument (MNSI) is frequently deployed, but it lacks a straightforward rating of severity. A [...] Read more.
Diabetic sensorimotor polyneuropathy (DSPN) is a serious long-term complication of diabetes, which may lead to foot ulceration and amputation. Among the screening tools for DSPN, the Michigan neuropathy screening instrument (MNSI) is frequently deployed, but it lacks a straightforward rating of severity. A DSPN severity grading system has been built and simulated for the MNSI, utilizing longitudinal data captured over 19 years from the Epidemiology of Diabetes Interventions and Complications (EDIC) trial. Machine learning algorithms were used to establish the MNSI factors and patient outcomes to characterise the features with the best ability to detect DSPN severity. A nomogram based on multivariable logistic regression was designed, developed and validated. The extra tree model was applied to identify the top seven ranked MNSI features that identified DSPN, namely vibration perception (R), 10-gm filament, previous diabetic neuropathy, vibration perception (L), presence of callus, deformities and fissure. The nomogram’s area under the curve (AUC) was 0.9421 and 0.946 for the internal and external datasets, respectively. The probability of DSPN was predicted from the nomogram and a DSPN severity grading system for MNSI was created using the probability score. An independent dataset was used to validate the model’s performance. The patients were divided into four different severity levels, i.e., absent, mild, moderate, and severe, with cut-off values of 10.50, 12.70 and 15.00 for a DSPN probability of less than 50, 75 and 100%, respectively. We provide an easy-to-use, straightforward and reproducible approach to determine prognosis in patients with DSPN. Full article
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9 pages, 1293 KiB  
Article
Detecting Parkinson’s Disease through Gait Measures Using Machine Learning
by Alex Li and Chenyu Li
Diagnostics 2022, 12(10), 2404; https://doi.org/10.3390/diagnostics12102404 - 03 Oct 2022
Cited by 9 | Viewed by 2188
Abstract
Parkinson’s disease (PD) is one of the most common long-term degenerative movement disorders that affects the motor system. This progressive nervous system disorder affects nearly one million Americans, and more than 20,000 new cases are diagnosed each year. PD is a chronic and [...] Read more.
Parkinson’s disease (PD) is one of the most common long-term degenerative movement disorders that affects the motor system. This progressive nervous system disorder affects nearly one million Americans, and more than 20,000 new cases are diagnosed each year. PD is a chronic and progressive painful neurological disorder and usually people with PD live 10 to 20 years after being diagnosed. PD is diagnosed based on the identification of motor signs of bradykinesia, rigidity, tremor, and postural instability. Though several attempts have been made to develop explicit diagnostic criteria, this is still largely unrevealed. In this manuscript, we aim to build a classifier with gait data from Parkinson patients and healthy controls using machine learning methods. The classifier could help facilitate a more accurate and cost-effective diagnostic method. The input to our algorithm is the Gait in Parkinson’s Disease dataset published on PhysioNet containing force sensor data as the measurement of gait from 92 healthy subjects and 214 patients with idiopathic Parkinson’s Disease. Different machine learning methods, including logistic regression, SVM, decision tree, KNN were tested to output a predicted classification of Parkinson patients and healthy controls. Baseline models including frequency domain method can reach similar performance and may be another good approach for the PD diagnostics. Full article
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Review

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39 pages, 1385 KiB  
Review
Exploring Huntington’s Disease Diagnosis via Artificial Intelligence Models: A Comprehensive Review
by Sowmiyalakshmi Ganesh, Thillai Chithambaram, Nadesh Ramu Krishnan, Durai Raj Vincent, Jayakumar Kaliappan and Kathiravan Srinivasan
Diagnostics 2023, 13(23), 3592; https://doi.org/10.3390/diagnostics13233592 - 03 Dec 2023
Cited by 1 | Viewed by 1772
Abstract
Huntington’s Disease (HD) is a devastating neurodegenerative disorder characterized by progressive motor dysfunction, cognitive impairment, and psychiatric symptoms. The early and accurate diagnosis of HD is crucial for effective intervention and patient care. This comprehensive review provides a comprehensive overview of the utilization [...] Read more.
Huntington’s Disease (HD) is a devastating neurodegenerative disorder characterized by progressive motor dysfunction, cognitive impairment, and psychiatric symptoms. The early and accurate diagnosis of HD is crucial for effective intervention and patient care. This comprehensive review provides a comprehensive overview of the utilization of Artificial Intelligence (AI) powered algorithms in the diagnosis of HD. This review systematically analyses the existing literature to identify key trends, methodologies, and challenges in this emerging field. It also highlights the potential of ML and DL approaches in automating HD diagnosis through the analysis of clinical, genetic, and neuroimaging data. This review also discusses the limitations and ethical considerations associated with these models and suggests future research directions aimed at improving the early detection and management of Huntington’s disease. It also serves as a valuable resource for researchers, clinicians, and healthcare professionals interested in the intersection of machine learning and neurodegenerative disease diagnosis. Full article
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22 pages, 1899 KiB  
Review
Epileptic Seizure Detection Using Machine Learning: Taxonomy, Opportunities, and Challenges
by Muhammad Shoaib Farooq, Aimen Zulfiqar and Shamyla Riaz
Diagnostics 2023, 13(6), 1058; https://doi.org/10.3390/diagnostics13061058 - 10 Mar 2023
Cited by 14 | Viewed by 7352
Abstract
Epilepsy is a life-threatening neurological brain disorder that gives rise to recurrent unprovoked seizures. It occurs due to abnormal chemical changes in our brains. For many years, studies have been conducted to support the automatic diagnosis of epileptic seizures for clinicians’ ease. For [...] Read more.
Epilepsy is a life-threatening neurological brain disorder that gives rise to recurrent unprovoked seizures. It occurs due to abnormal chemical changes in our brains. For many years, studies have been conducted to support the automatic diagnosis of epileptic seizures for clinicians’ ease. For that, several studies entail machine learning methods for early predicting epileptic seizures. Mainly, feature extraction methods have been used to extract the right features from the EEG data generated by the EEG machine. Then various machine learning classifiers are used for the classification process. This study provides a systematic literature review of the feature selection process and classification performance. This review was limited to finding the most used feature extraction methods and the classifiers used for accurate classification of normal to epileptic seizures. The existing literature was examined from well-known repositories such as MDPI, IEEE Xplore, Wiley, Elsevier, ACM, Springer link, and others. Furthermore, a taxonomy was created that recapitulates the state-of-the-art used solutions for this problem. We also studied the nature of different benchmark and unbiased datasets and gave a rigorous analysis of the working of classifiers. Finally, we concluded the research by presenting the gaps, challenges, and opportunities that can further help researchers predict epileptic seizures. Full article
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