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Bioengineering
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Next-Generation Diagnostic and Therapy Systems for Neurodegenerative Diseases
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Next-Generation Diagnostic and Therapy Systems for Neurodegenerative Diseases

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biomedical Engineering and Biomaterials".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 2793

Special Issue Editors

Dr. Raji Sundararajan

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Guest Editor
Department of Electrical and Computer Engineering Technology (ECET), Purdue University, 401 N. Grant St, West Lafayette, IN 47907, USA
Interests: cancer; electrochemotherapy; neurodegenerative diseases; aging; Parkinsons'; Alzhimer's
Special Issues, Collections and Topics in MDPI journals
Dr. Miad Faezipour

E-Mail Website
Guest Editor
School of Engineering Technology, Electrical and Computer Engineering Technology, Purdue University, West Lafayette, IN 47907, USA
Interests: healthcare technology with embedded intelligence; digital/biomedical-embedded systems; biomedical signal/image processing; artificial intelligence; machine/deep learning

Special Issue Information

Dear Colleagues,

Neurodegenerative diseases, such as Alzheimer's disease (AD), Parkinson's disease (AD), and others, caused by the gradual deterioration of neurons in the brain represent one of the most significant challenges in modern medicine, as conditions like AD and dementia continue to impact millions worldwide. Despite the absence of a definitive cure, recent advancements in diagnostic and therapeutic technologies have opened new avenues for managing these debilitating disorders. This Special Issue focuses on exploring the transformative potential of integrating artificial intelligence (AI) models, machine learning techniques, deep learning techniques, and advanced neurostimulation systems for neurodegenerative diseases and other types of dementia or brain atrophy. These innovations could enable smarter diagnostics through brain imaging technologies and electroencephalogram (EEG) signal analysis for early detection, while paving the way for more precise and individualized treatment plans. The application of AI-driven approaches has the potential and promise to enhance our understanding of disease progression, enabling early intervention and improving outcomes for patients.

Of particular interest is the development and application of (invasive or non-invasive) neuromodulation techniques such as Deep Brain Stimulation (DBS), Transcranial Magnetic Stimulation (TMS), and Vagal Nerve Stimulation (VNS). Combining these methods with cutting-edge AI/ML algorithms holds tremendous promise for optimizing therapy delivery and monitoring its effects in real time. These systems aim to not only slow disease progression but also enhance neural connectivity and brain function by leveraging electric stimulation. Contributions to this Special Issue are invited to advance the integration of sensing technologies, AI-driven analysis from brain signals or images, and neurostimulation therapies.

Submissions on these and related topics can include original research articles, reviews, or communications that address these areas and push the boundaries of smart diagnostics and therapeutic interventions for neurodegenerative diseases.

Dr. Raji Sundararajan
Dr. Miad Faezipour
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 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. 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

  • Alzheimer's disease
  • Parkinson's disease
  • machine learning
  • deep learning
  • neurostimulation
  • neuromodulation
  • artificial intelligence models

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Published Papers (3 papers)

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19 pages, 4569 KB  
Article
NeuroNet-AD: A Multimodal Deep Learning Framework for Multiclass Alzheimer’s Disease Diagnosis
by Saeka Rahman, Md Motiur Rahman, Smriti Bhatt, Raji Sundararajan and Miad Faezipour
Bioengineering 2025, 12(10), 1107; https://doi.org/10.3390/bioengineering12101107 - 15 Oct 2025
Viewed by 534
Abstract
Alzheimer’s disease (AD) is the most prevalent form of dementia. This disease significantly impacts cognitive functions and daily activities. Early and accurate diagnosis of AD, including the preliminary stage of mild cognitive impairment (MCI), is critical for effective patient care and treatment development. [...] Read more.
Alzheimer’s disease (AD) is the most prevalent form of dementia. This disease significantly impacts cognitive functions and daily activities. Early and accurate diagnosis of AD, including the preliminary stage of mild cognitive impairment (MCI), is critical for effective patient care and treatment development. Although advancements in deep learning (DL) and machine learning (ML) models improve diagnostic precision, the lack of large datasets limits further enhancements, necessitating the use of complementary data. Existing convolutional neural networks (CNNs) effectively process visual features but struggle to fuse multimodal data effectively for AD diagnosis. To address these challenges, we propose NeuroNet-AD, a novel multimodal CNN framework designed to enhance AD classifcation accuracy. NeuroNet-AD integrates Magnetic Resonance Imaging (MRI) images with clinical text-based metadata, including psychological test scores, demographic information, and genetic biomarkers. In NeuroNet-AD, we incorporate Convolutional Block Attention Modules (CBAMs) within the ResNet-18 backbone, enabling the model to focus on the most informative spatial and channel-wise features. We introduce an attention computation and multimodal fusion module, named Meta Guided Cross Attention (MGCA), which facilitates effective cross-modal alignment between images and meta-features through a multi-head attention mechanism. Additionally, we employ an ensemble-based feature selection strategy to identify the most discriminative features from the textual data, improving model generalization and performance. We evaluate NeuroNet-AD on the Alzheimer’s Disease Neuroimaging Initiative (ADNI1) dataset using subject-level 5-fold cross-validation and a held-out test set to ensure robustness. NeuroNet-AD achieved 98.68% accuracy in multiclass classification of normal control (NC), MCI, and AD and 99.13% accuracy in the binary setting (NC vs. AD) on the ADNI dataset, outperforming state-of-the-art models. External validation on the OASIS-3 dataset further confirmed the model’s generalization ability, achieving 94.10% accuracy in the multiclass setting and 98.67% accuracy in the binary setting, despite variations in demographics and acquisition protocols. Further extensive evaluation studies demonstrate the effectiveness of each component of NeuroNet-AD in improving the performance. Full article
(This article belongs to the Special Issue Next-Generation Diagnostic and Therapy Systems for Neurodegenerative Diseases)
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14 pages, 920 KB  
Article
AI-Based Facial Emotion Analysis for Early and Differential Diagnosis of Dementia
by Letizia Bergamasco, Anita Coletta, Gabriella Olmo, Aurora Cermelli, Elisa Rubino and Innocenzo Rainero
Bioengineering 2025, 12(10), 1082; https://doi.org/10.3390/bioengineering12101082 - 4 Oct 2025
Viewed by 809
Abstract
Early and differential diagnosis of dementia is essential for timely and targeted care. This study investigated the feasibility of using an artificial intelligence (AI)-based system to discriminate between different stages and etiologies of dementia by analyzing facial emotions. We collected video recordings of [...] Read more.
Early and differential diagnosis of dementia is essential for timely and targeted care. This study investigated the feasibility of using an artificial intelligence (AI)-based system to discriminate between different stages and etiologies of dementia by analyzing facial emotions. We collected video recordings of 64 participants exposed to standardized audio-visual stimuli. Facial emotion features in terms of valence and arousal were extracted and used to train machine learning models on multiple classification tasks, including distinguishing individuals with mild cognitive impairment (MCI) and overt dementia from healthy controls (HCs) and differentiating Alzheimer’s disease (AD) from other types of cognitive impairment. Nested cross-validation was adopted to evaluate the performance of different tested models (K-Nearest Neighbors, Logistic Regression, and Support Vector Machine models) and optimize their hyperparameters. The system achieved a cross-validation accuracy of 76.0% for MCI vs. HCs, 73.6% for dementia vs. HCs, and 64.1% in the three-class classification (MCI vs. dementia vs. HCs). Among cognitively impaired individuals, a 75.4% accuracy was reached in distinguishing AD from other etiologies. These results demonstrated the potential of AI-driven facial emotion analysis as a non-invasive tool for early detection of cognitive impairment and for supporting differential diagnosis of AD in clinical settings. Full article
(This article belongs to the Special Issue Next-Generation Diagnostic and Therapy Systems for Neurodegenerative Diseases)
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20 pages, 360 KB  
Article
Unveiling Early Signs of Preclinical Alzheimer’s Disease Through ERP Analysis with Weighted Visibility Graphs and Ensemble Learning
by Yongshuai Liu, Jiangyi Xia, Ziwen Kan, Jesse Zhang, Sheela Toprani, James B. Brewer, Marta Kutas, Xin Liu and John Olichney
Bioengineering 2025, 12(8), 814; https://doi.org/10.3390/bioengineering12080814 - 29 Jul 2025
Viewed by 881
Abstract
The early detection of Alzheimer’s disease (AD) is important for effective therapeutic interventions and optimized enrollment for clinical trials. Recent studies have shown high accuracy in identifying mild AD by applying visibility graph and machine learning methods to electroencephalographic (EEG) data. We present [...] Read more.
The early detection of Alzheimer’s disease (AD) is important for effective therapeutic interventions and optimized enrollment for clinical trials. Recent studies have shown high accuracy in identifying mild AD by applying visibility graph and machine learning methods to electroencephalographic (EEG) data. We present a novel analytical framework combining Weighted Visibility Graphs (WVG) and ensemble learning to detect individuals in the “preclinical” stage of AD (preAD) using a word repetition EEG paradigm, where WVG is an advanced variant of natural Visibility Graph (VG), incorporating weighted edges based on the visibility degree between corresponding data points. The EEG signals were recorded from 40 cognitively unimpaired elderly participants (20 preclinical AD and 20 normal old) during a word repetition task. Event-related potential (ERP) and oscillatory signals were extracted from each EEG channel and transformed into a WVG network, from which relevant topological features were extracted. The features were selected using t-tests to reduce noise. Subsequent statistical analysis reveals significant disparities in the structure of WVG networks between preAD and normal subjects. Furthermore, Principal Component Analysis (PCA) was applied to condense the input data into its principal features. Leveraging these PCA components as input features, several machine learning algorithms are used to classify preAD vs. normal subjects. To enhance classification accuracy and robustness, an ensemble method is employed alongside the classifiers. Our framework achieved an accuracy of up to 92% discriminating preAD from normal old using both linear and non-linear classifiers, signifying the efficacy of combining WVG and ensemble learning in identifying very early AD from EEG signals. The framework can also improve clinical efficiency by reducing the amount of data required for effective classification and thus saving valuable clinical time. Full article
(This article belongs to the Special Issue Next-Generation Diagnostic and Therapy Systems for Neurodegenerative Diseases)
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