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Bioengineering
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AI and Data Analysis in Neurological Disease Management
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AI and Data Analysis in Neurological Disease Management

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 3786

Special Issue Editors

Dr. Bess Lam

E-Mail Website
Guest Editor
Department of Counselling and Psychology, Hong Kong Shue Yan University, Hongkong, China
Interests: neuropsychology; neurocriminology; antisocial behavior; youth mental health; schizophrenia-spectrum disorders
Dr. Ka-Chun Wong

E-Mail Website
Guest Editor
Department of Computer Science, City University of Hong Kong, Hong Kong, China
Interests: bioinformatics; data science; machine learning; deep learning; medical informatics; cancer genomics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the transformative role of artificial intelligence (AI) and data analysis in the management of neurological diseases. We seek to highlight cutting-edge research that demonstrates how AI technologies can enhance diagnosis, treatment, and patient outcomes in various neurological conditions, such as Alzheimer's disease and Parkinson's disease.

Key objectives include the following:

  1. Innovative Applications: Showcasing novel AI methodologies and tools that improve data interpretation and decision-making in clinical settings.
  2. Interdisciplinary Approaches: Encouraging collaboration between neurologists, data scientists, and AI researchers to foster integrated solutions.
  3. Real-World Impact: Evaluating the effectiveness of AI-driven strategies in real-world clinical environments and their potential to reshape traditional practices.
  4. Ethical Considerations: Addressing ethical implications and challenges of implementing AI in healthcare, ensuring patient safety and data privacy.

By bringing together diverse perspectives and research findings, this issue aims to contribute to the advancement of neurological disease management through AI and data analytics.

Dr. Bess Lam
Dr. Ka-Chun Wong
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 submissions that pass pre-check are 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 250 words) can be sent to the Editorial Office for assessment.

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. Bioengineering 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 2700 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

  • AI technologies
  • management of neurological diseases
  • interdisciplinary approaches
  • data analysis

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Published Papers (6 papers)

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Research

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13 pages, 1036 KB  
Article
Predicting Conversion from Mild Cognitive Impairment to Alzheimer’s Disease Using a Vision Transformer and Hippocampal MRI Slices
by René Seiger and Peter Fierlinger
Bioengineering 2026, 13(2), 163; https://doi.org/10.3390/bioengineering13020163 - 29 Jan 2026
Viewed by 412
Abstract
Convolutional neural networks (CNNs) have been the standard for computer vision tasks including applications in Alzheimer’s disease (AD). Recently, Vision Transformers (ViTs) have been introduced, which have emerged as a strong alternative to CNNs. A common precursor stage of AD is a syndrome [...] Read more.
Convolutional neural networks (CNNs) have been the standard for computer vision tasks including applications in Alzheimer’s disease (AD). Recently, Vision Transformers (ViTs) have been introduced, which have emerged as a strong alternative to CNNs. A common precursor stage of AD is a syndrome called mild cognitive impairment (MCI). However, not all individuals diagnosed with MCI progress to AD. In this exploratory investigation, we aimed to assess whether a ViT can reliably classify converters versus non-converters. A transfer learning approach was used for model training by applying a pretrained ViT model, fine-tuned on the ADNI dataset. The cohort comprised 575 individuals (299 stable MCIs; 276 progressive MCIs who converted within 36 months) from whom axial T1-weighted MRI slices covering the hippocampal region were used as model inputs. Results showed an average area under the receiver operating characteristic curve (AUC-ROC) on the test set of 0.74 ± 0.02 (mean ± SD), an accuracy of 0.69 ± 0.03, a sensitivity of 0.65 ± 0.07, a specificity of 0.72 ± 0.06, and an F1-score for the progressive MCI class of 0.67 ± 0.04. These findings demonstrate that a ViT approach achieves reasonable accuracy for classifying AD converters vs. non-converters, though its generalizability and clinical utility require further validation. Full article
(This article belongs to the Special Issue AI and Data Analysis in Neurological Disease Management)
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13 pages, 1267 KB  
Article
Can Machines Identify Pain Effects? A Machine Learning Proof of Concept to Identify EMG Pain Signature
by Klaus Becker, Franciele Parolini, Venicius de Paula Silva, João Paulo Vilas-Boas, Thomas Graven-Nielsen, Ulysses Ervilha and Márcio Goethel
Bioengineering 2026, 13(2), 141; https://doi.org/10.3390/bioengineering13020141 - 26 Jan 2026
Viewed by 294
Abstract
This study introduces a machine-learning-based approach for identifying “pain signatures” using electromyography data from volunteers undergoing acute pain. Leveraging the XGBoost algorithm, our method analyzes electromyography features (variance, mean absolute deviation, integral, peak, and entropy) to classify muscle contractions as painful or non-painful. [...] Read more.
This study introduces a machine-learning-based approach for identifying “pain signatures” using electromyography data from volunteers undergoing acute pain. Leveraging the XGBoost algorithm, our method analyzes electromyography features (variance, mean absolute deviation, integral, peak, and entropy) to classify muscle contractions as painful or non-painful. Fifteen participants performed controlled elbow flexion tasks under three conditions: during painful and painless conditions. The results revealed that electromyographic peak and integral activity were key predictors of pain states, with the model achieving 73% sensitivity in distinguishing painful from painless conditions. Interestingly, placebo-induced responses with less intense pain exhibited muscular adaptations similar to, but less extensive than, those observed under actual pain. These findings underscore the potential of machine learning to enhance pain assessment by providing a non-verbal, objective method for analyzing neuromuscular adaptations, paving the way for personalized pain management and more accurate monitoring of musculoskeletal health. Full article
(This article belongs to the Special Issue AI and Data Analysis in Neurological Disease Management)
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22 pages, 1461 KB  
Article
Implementation of a Stress Biomarker and Development of a Deep Neural Network-Based Multi-Mental State Classification Model
by Sangsik Lee, Jaehyun Jo, Sohyeon Bang and Jinhyoung Jeong
Bioengineering 2025, 12(12), 1352; https://doi.org/10.3390/bioengineering12121352 - 11 Dec 2025
Viewed by 543
Abstract
The purpose of this study was to develop a model capable of predicting stress levels and interpreting the underlying physiological patterns using large-scale, real-life biosignal data. To achieve this, we utilized approximately 137,000 longitudinal measurements voluntarily collected from residents of Sejong Special Self-Governing [...] Read more.
The purpose of this study was to develop a model capable of predicting stress levels and interpreting the underlying physiological patterns using large-scale, real-life biosignal data. To achieve this, we utilized approximately 137,000 longitudinal measurements voluntarily collected from residents of Sejong Special Self-Governing City over a two-year period (February 2023–December 2024). Based on these data, we constructed a stress prediction framework that integrates both static machine-learning models—such as Random Forest and LightGBM—and time-series deep learning models, including LSTM and Transformer architectures. Model interpretability was further enhanced through SHapley Additive exPlanations (SHAP), which quantified the contribution of key biomarkers, and through visualization of Transformer attention weights to reveal temporal interactions within the biosignal sequences. The central objective of this study was to evaluate how accurately a deep learning model can learn and reproduce stress indices generated by existing heart rate variability (HRV)-based algorithms embedded in K-FDA-approved wearable devices. Accordingly, the ground truth used in this work reflects algorithmic outputs rather than clinically validated assessments such as salivary cortisol or psychological scales. Thus, rather than identifying independent clinical stress markers, the present work focuses on determining whether a Transformer-based model can effectively approximate device-derived physiological stress levels over time, thereby providing a methodological foundation for future applications using clinically validated stress labels. Experimental results demonstrated that the Transformer model achieved approximately 98% classification accuracy across this large dataset, indicating that it successfully captures short-term biosignal fluctuations as well as long-term temporal structure. These findings collectively demonstrate the engineering feasibility of developing a large-scale, wearable-based stress monitoring system. Full article
(This article belongs to the Special Issue AI and Data Analysis in Neurological Disease Management)
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20 pages, 2950 KB  
Article
The Role of MER Processing Pipelines for STN Functional Identification During DBS Surgery: A Feature-Based Machine Learning Approach
by Vincenzo Levi, Stefania Coelli, Chiara Gorlini, Federica Forzanini, Sara Rinaldo, Nico Golfrè Andreasi, Luigi Romito, Roberto Eleopra and Anna Maria Bianchi
Bioengineering 2025, 12(12), 1300; https://doi.org/10.3390/bioengineering12121300 - 26 Nov 2025
Cited by 1 | Viewed by 564
Abstract
Microelectrode recording (MER) is commonly used to validate preoperative targeting during subthalamic nucleus (STN) deep brain stimulation (DBS) surgery for Parkinson’s Disease (PD). Although machine learning (ML) has been used to improve STN localization using MER data, the impact of preprocessing steps on [...] Read more.
Microelectrode recording (MER) is commonly used to validate preoperative targeting during subthalamic nucleus (STN) deep brain stimulation (DBS) surgery for Parkinson’s Disease (PD). Although machine learning (ML) has been used to improve STN localization using MER data, the impact of preprocessing steps on the accuracy of classifiers has received little attention. We evaluated 24 distinct preprocessing pipelines combining four artifact removal strategies, three outlier handling methods, and optional feature normalization. The effect of each data processing procedure’s component of interest was evaluated in function of the performance obtained using three ML models. Artifact rejection methods (i.e., unsupervised variance-based algorithm (COV) and background noise estimation (BCK)), combined with optimized outlier management (i.e., statistical outlier identification per hemisphere (ORH)) consistently improved classification performance. In contrast, applying hemisphere-specific feature normalization prior to classification led to performance degradation across all metrics. SHAP (SHapley Additive exPlanations) analysis, performed to determine feature importance across pipelines, revealed stable agreement with regard to influential features across diverse preprocessing configurations. In conclusion, optimal artifact rejection and outlier treatment are essential in preprocessing MER for STN identification in DBS, whereas preliminary feature normalization strategies may impair model performance. Overall, the best classification performance was obtained by applying the Random Forest model to the dataset treated using COV artifact rejection and ORH outlier management (accuracy = 0.945). SHAP-based interpretability offers valuable guidance for refining ML pipelines. These insights can inform robust protocol development for MER-guided DBS targeting. Full article
(This article belongs to the Special Issue AI and Data Analysis in Neurological Disease Management)
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24 pages, 608 KB  
Article
Evaluating the Severity of Autism Spectrum Disorder from EEG: A Multidisciplinary Approach Using Statistical and Artificial Intelligence Frameworks
by Noor Kamal Al-Qazzaz, Sawal Hamid Bin Mohd Ali and Siti Anom Ahmad
Bioengineering 2025, 12(11), 1225; https://doi.org/10.3390/bioengineering12111225 - 10 Nov 2025
Viewed by 1080
Abstract
A developmental impairment known as autism spectrum disorder (ASD) impacts youngsters and is characterized by impaired social communication and limited behavioral expression. In this study, electroencephalography (EEG) is used to obtain the brain electrical activity of typically developing children and of mild, moderate, [...] Read more.
A developmental impairment known as autism spectrum disorder (ASD) impacts youngsters and is characterized by impaired social communication and limited behavioral expression. In this study, electroencephalography (EEG) is used to obtain the brain electrical activity of typically developing children and of mild, moderate, and severe ASD patients using relative powers. This study investigates ASD patients using a multidisciplinary approach involving two-way ANOVA and Pearson’s correlation statistical analyses to better understand the multistage severity of ASD from EEG by providing a spectro-spatial profile of ASD severity. Artificial intelligence frameworks, including a decision tree (DT) machine learning classifier and a long short-term memory (LSTM) neural network, are applied to discriminate mild, moderate, and severe ASD patients from typically developing children. The statistical results revealed that with increasing severity compared to the control, faster frequencies decreased and slower frequencies increased, indicating a distinct correlation between the severity of ASD and neurophysiological activity. Moreover, the DT classifier achieved a classification accuracy of 65%, and the LSTM classifier achieved a classification accuracy of 73.3%. This approach highlights the potential for statistical and artificial intelligence techniques to reliably identify EEG abnormalities associated with ASD, which could lead to earlier treatment and improved prospects for patients. Full article
(This article belongs to the Special Issue AI and Data Analysis in Neurological Disease Management)
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26 pages, 2946 KB  
Systematic Review
Digital and Intelligent Rehabilitation Technologies in Stroke and Neurological Disorders: A Systematic Review of Artificial Intelligence, Virtual Reality, Gamification, and Emerging Therapeutic Platforms in Neurorehabilitation
by Majeda M. El-Banna, Moattar Raza Rizvi, Waqas Sami, Ankita Sharma and Rushdy R. Atyeh
Bioengineering 2026, 13(2), 195; https://doi.org/10.3390/bioengineering13020195 - 9 Feb 2026
Viewed by 507
Abstract
Artificial intelligence (AI), virtual reality (VR), gamification, and telerehabilitation are increasingly incorporated into neurorehabilitation to deliver adaptive, personalized, and remotely accessible interventions for individuals with stroke and other neurological disorders. These technologies aim to address key limitations in conventional rehabilitation by enhancing training [...] Read more.
Artificial intelligence (AI), virtual reality (VR), gamification, and telerehabilitation are increasingly incorporated into neurorehabilitation to deliver adaptive, personalized, and remotely accessible interventions for individuals with stroke and other neurological disorders. These technologies aim to address key limitations in conventional rehabilitation by enhancing training intensity, patient engagement, accessibility, and real-time monitoring. This systematic review synthesizes evidence from clinical and simulation-based studies evaluating AI-assisted systems, non-AI gamified platforms, VR/exergames, telerehabilitation models, and simulation-driven architectures across neurological populations. A comprehensive search of PubMed, Scopus, Embase, CINAHL, and Web of Science (2010–2025) identified randomized controlled trials, pilot and quasi-experimental studies, telerehabilitation systems, VR/exergame interventions, AI-based adaptive tools, and computational or model-driven investigations, guided by a revised PICO framework. Data were extracted using a standardized template, with studies categorized by design, population, technological modality, and outcome domain. Risk of bias was assessed using validated tools, and GRADE was applied to stroke-specific clinical outcomes. Twenty-two studies met the inclusion criteria, encompassing both clinical trials and simulation/modeling research. Clinical studies reported improvements in motor function, balance, gait, swallowing, cognition, and psychosocial well-being, often accompanied by high usability and adherence. AI-enabled systems facilitated adaptive difficulty adjustment, automated feedback, and individualized progression, while non-AI platforms demonstrated strong engagement and meaningful functional gains. Simulation studies provided valuable insights into algorithm behavior, sensor-based modeling, and system optimization. Despite promising multi-domain benefits, methodological heterogeneity, limited long-term follow-up, and inconsistent AI transparency remain key challenges, underscoring the need for standardized outcomes, explainable AI, inclusive design, and robust multicenter trials. Full article
(This article belongs to the Special Issue AI and Data Analysis in Neurological Disease Management)
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