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Sensors and Artificial Intelligence Technologies in Neurodegenerative Disease Diagnosis
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Sensors and Artificial Intelligence Technologies in Neurodegenerative Disease Diagnosis

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (31 May 2025) | Viewed by 10365

Special Issue Editors

Dr. Sofia Zaira Otin Mallada

E-Mail Website
Guest Editor
Department of Applied Physics, University of Zaragoza, Pedro Cerbuna 12, 50009 Zaragoza, Spain
Interests: neurodegenerative disease; ophthalmology and visual science; artificial intelligence technologies; algorithm; sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Sergio G Rodrigo

E-Mail Website
Guest Editor
1. Department of Applied Physics, University of Zaragoza, Pedro Cerbuna 12, 50009 Zaragoza, Spain
2. Institute of Nanoscience and Materials of Aragon (INMA), CSIC—University of Zaragoza, 50009 Zaragoza, Spain
Interests: nonlinear plasmonics; 2D metamaterials; condensed matter nanophotonics; numerical methods
Dr. Jorge Ares

E-Mail Website
Guest Editor
Department of Applied Physics, University of Zaragoza, Pedro Cerbuna 12, 50009 Zaragoza, Spain
Interests: artificial intelligence; neurodegenerative disease; retinal

Special Issue Information

Dear Colleagues,

In recent years, research groups have focused on developing new early-diagnosis and monitoring strategies for neurodegenerative disease based on objective biomarkers. They have especially concentrated on alternative non-invasive techniques that are less expensive, safer, and more comfortable for patients than a lumbar puncture to remove cerebrospinal fluid or MRI with intravenous contrast. Artificial intelligence has demonstrated its ability to process large quantities of data for creating diagnosis algorithms, which are able to predict or detect these pathologies. These algorithms can be developed using raw data from different diagnosis devices and to improve their diagnostic capacity.

This Special Issue focuses on the application of data-processing algorithms obtained from diagnostic sensor equipment applied to diagnosing and monitoring neurodegenerative diseases with the aim of helping in early detection.

Dr. Sofia Zaira Otin Mallada
Dr. Sergio G Rodrigo
Dr. Jorge Ares
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. Sensors is an international peer-reviewed open access semimonthly 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 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

  • artificial intelligence
  • neurodegenerative disease
  • Alzheimer disease
  • Parkinson disease
  • multiple sclerosis
  • biomarker
  • algorithm
  • sensing

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Published Papers (5 papers)

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Research

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9 pages, 949 KiB  
Article
A Superpixel-Based Algorithm for Detecting Optical Density Changes in Choroidal Optical Coherence Tomography Images of Diabetic Patients
by Sofia Otin, Victor Mallen-Gracia, Luis Perez-Maña, Francisco J. Ávila and Elena Garcia-Martin
Sensors 2025, 25(12), 3619; https://doi.org/10.3390/s25123619 - 9 Jun 2025
Viewed by 60
Abstract
Background: This study explored the diagnostic potential of image-processing analysis in optical coherence tomography (OCT) images to detect systemic vascular changes in individuals with systemic diseases. Methods: Ocular OCT images from two cohorts diabetic patients and healthy control subjects were analyzed. A novel [...] Read more.
Background: This study explored the diagnostic potential of image-processing analysis in optical coherence tomography (OCT) images to detect systemic vascular changes in individuals with systemic diseases. Methods: Ocular OCT images from two cohorts diabetic patients and healthy control subjects were analyzed. A novel Superpixel Segmentation (SpS) algorithm was used to process these images and extract optical image density information from ocular vascular tissue. The algorithm was applied to isolate the choroid layer for analysis of its optical properties. The procedure was performed by separate examiners, and both inter- and intra-observer repeatability were assessed. Choroidal area (CA) and choroidal optical image density (COID) metrics were used to assess structural changes in the vascular tissue and predict alterations in the choroidal parameters. Results: A total of 110 diabetic patient eye images and 92 healthy control images were processed. The results showed significant differences in CA and COID between diabetic and healthy eyes, indicating that these parameters could serve as valuable biomarkers for early vascular damage. Conclusions: The use of the SpS algorithm on OCT B-scan images allows for the identification of new parameters linked to ocular vascular damage. These findings suggest that digital image-processing techniques can reveal differences in vascular tissue, offering potential new indicators of pathology. Full article
(This article belongs to the Special Issue Sensors and Artificial Intelligence Technologies in Neurodegenerative Disease Diagnosis)
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16 pages, 2191 KiB  
Article
Retrospective Frailty Assessment in Older Adults Using Inertial Measurement Unit-Based Deep Learning on Gait Spectrograms
by Julius Griškevičius, Kristina Daunoravičienė, Liudvikas Petrauskas, Andrius Apšega and Vidmantas Alekna
Sensors 2025, 25(11), 3351; https://doi.org/10.3390/s25113351 - 26 May 2025
Viewed by 352
Abstract
Frailty is a common syndrome in the elderly, marked by an increased risk of negative health outcomes such as falls, disability and death. It is important to detect frailty early and accurately to apply timely interventions that can improve health results in older [...] Read more.
Frailty is a common syndrome in the elderly, marked by an increased risk of negative health outcomes such as falls, disability and death. It is important to detect frailty early and accurately to apply timely interventions that can improve health results in older adults. Traditional evaluation methods often depend on subjective evaluations and clinical opinions, which might lack consistency. This research uses deep learning to classify frailty from spectrograms based on IMU data collected during gait analysis. The study retrospectively analyzed an existing IMU dataset. Gait data were categorized into Frail, PreFrail, and NoFrail groups based on clinical criteria. Six IMUs were placed on lower extremity segments to collect motion data during walking activities. The raw signals from accelerometers and gyroscopes were converted into time–frequency spectrograms. A convolutional neural network (CNN) trained solely on raw IMU-derived spectrograms achieved 71.4 % subject-wise accuracy in distinguishing frailty levels. Minimal preprocessing did not improve subject-wise performance, suggesting that the raw time–frequency representation retains the most salient gait cues. These findings suggest that wearable sensor technology combined with deep learning provides a robust, objective tool for frailty assessment, offering potential for clinical and remote health monitoring applications. Full article
(This article belongs to the Special Issue Sensors and Artificial Intelligence Technologies in Neurodegenerative Disease Diagnosis)
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27 pages, 4633 KiB  
Article
Assistance Device Based on SSVEP-BCI Online to Control a 6-DOF Robotic Arm
by Maritza Albán-Escobar, Pablo Navarrete-Arroyo, Danni Rodrigo De la Cruz-Guevara and Johanna Tobar-Quevedo
Sensors 2024, 24(6), 1922; https://doi.org/10.3390/s24061922 - 17 Mar 2024
Viewed by 2401
Abstract
This paper explores the potential benefits of integrating a brain–computer interface (BCI) utilizing the visual-evoked potential paradigm (SSVEP) with a six-degrees-of-freedom (6-DOF) robotic arm to enhance rehabilitation tools. The SSVEP-BCI employs electroencephalography (EEG) as a method of measuring neural responses inside the occipital [...] Read more.
This paper explores the potential benefits of integrating a brain–computer interface (BCI) utilizing the visual-evoked potential paradigm (SSVEP) with a six-degrees-of-freedom (6-DOF) robotic arm to enhance rehabilitation tools. The SSVEP-BCI employs electroencephalography (EEG) as a method of measuring neural responses inside the occipital lobe in reaction to pre-established visual stimulus frequencies. The BCI offline and online studies yielded accuracy rates of 75% and 83%, respectively, indicating the efficacy of the system in accurately detecting and capturing user intent. The robotic arm achieves planar motion by utilizing a total of five control frequencies. The results of this experiment exhibited a high level of precision and consistency, as indicated by the recorded values of ±0.85 and ±1.49 cm for accuracy and repeatability, respectively. Moreover, during the performance tests conducted with the task of constructing a square within each plane, the system demonstrated accuracy of 79% and 83%. The use of SSVEP-BCI and a robotic arm together shows promise and sets a solid foundation for the development of assistive technologies that aim to improve the health of people with amyotrophic lateral sclerosis, spina bifida, and other related diseases. Full article
(This article belongs to the Special Issue Sensors and Artificial Intelligence Technologies in Neurodegenerative Disease Diagnosis)
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14 pages, 1202 KiB  
Article
Deep Multi-Branch CNN Architecture for Early Alzheimer’s Detection from Brain MRIs
by Paul K. Mandal and Rakeshkumar V. Mahto
Sensors 2023, 23(19), 8192; https://doi.org/10.3390/s23198192 - 30 Sep 2023
Cited by 5 | Viewed by 2921
Abstract
Alzheimer’s disease (AD) is a neurodegenerative disease that can cause dementia and result in a severe reduction in brain function, inhibiting simple tasks, especially if no preventative care is taken. Over 1 in 9 Americans suffer from AD-induced dementia, and unpaid care for [...] Read more.
Alzheimer’s disease (AD) is a neurodegenerative disease that can cause dementia and result in a severe reduction in brain function, inhibiting simple tasks, especially if no preventative care is taken. Over 1 in 9 Americans suffer from AD-induced dementia, and unpaid care for people with AD-related dementia is valued at USD 271.6 billion. Hence, various approaches have been developed for early AD diagnosis to prevent its further progression. In this paper, we first review other approaches that could be used for the early detection of AD. We then give an overview of our dataset and propose a deep convolutional neural network (CNN) architecture consisting of 7,866,819 parameters. This model comprises three different convolutional branches, each having a different length. Each branch is comprised of different kernel sizes. This model can predict whether a patient is non-demented, mild-demented, or moderately demented with a 99.05% three-class accuracy. In summary, the deep CNN model demonstrated exceptional accuracy in the early diagnosis of AD, offering a significant advancement in the field and the potential to improve patient care. Full article
(This article belongs to the Special Issue Sensors and Artificial Intelligence Technologies in Neurodegenerative Disease Diagnosis)
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Review

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32 pages, 411 KiB  
Review
Breaking Barriers: Exploring Neurotransmitters through In Vivo vs. In Vitro Rivalry
by Gabriel Philippe Lachance, Dominic Gauvreau, Élodie Boisselier, Mounir Boukadoum and Amine Miled
Sensors 2024, 24(2), 647; https://doi.org/10.3390/s24020647 - 19 Jan 2024
Cited by 3 | Viewed by 3650
Abstract
Neurotransmitter analysis plays a pivotal role in diagnosing and managing neurodegenerative diseases, often characterized by disturbances in neurotransmitter systems. However, prevailing methods for quantifying neurotransmitters involve invasive procedures or require bulky imaging equipment, therefore restricting accessibility and posing potential risks to patients. The [...] Read more.
Neurotransmitter analysis plays a pivotal role in diagnosing and managing neurodegenerative diseases, often characterized by disturbances in neurotransmitter systems. However, prevailing methods for quantifying neurotransmitters involve invasive procedures or require bulky imaging equipment, therefore restricting accessibility and posing potential risks to patients. The innovation of compact, in vivo instruments for neurotransmission analysis holds the potential to reshape disease management. This innovation can facilitate non-invasive and uninterrupted monitoring of neurotransmitter levels and their activity. Recent strides in microfabrication have led to the emergence of diminutive instruments that also find applicability in in vitro investigations. By harnessing the synergistic potential of microfluidics, micro-optics, and microelectronics, this nascent realm of research holds substantial promise. This review offers an overarching view of the current neurotransmitter sensing techniques, the advances towards in vitro microsensors tailored for monitoring neurotransmission, and the state-of-the-art fabrication techniques that can be used to fabricate those microsensors. Full article
(This article belongs to the Special Issue Sensors and Artificial Intelligence Technologies in Neurodegenerative Disease Diagnosis)
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