Special Issue "Application of Artificial Intelligence in Neurological Diseases"

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Methodology, Drug and Device Discovery".

Deadline for manuscript submissions: 25 August 2023 | Viewed by 1349

Special Issue Editors

Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy
Interests: interpretable machine learning; explainable artificial intelligence; computer aided diagnosis; neuroimaging; neuroscience; neurodegenerative diseases prediction; brain MRI; tractography
Special Issues, Collections and Topics in MDPI journals
Department of Medical and Surgical Sciences, University of Catanzaro "Magna Graecia", 88100 Catanzaro, Italy
Interests: biomedical engineering; rehabilitation; biomechanics; bioimaging
Special Issues, Collections and Topics in MDPI journals
Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Catanzaro, Italy
Interests: biomedical signal processing; neuroprosthetics; neuroscience; biosensors; EEG; EMG; MRI imaging; TMS

Special Issue Information

Dear Colleagues,

The wide application of artificial intelligence (AI) and machine learning (ML) on neuroscience data represents an unprecedented way for understanding neurological diseases. The AI and ML could, indeed, deal with multi-modal, multi-dimensional, and multi-source data, which can help to extract new knowledge about the pathological mechanisms that affect the human brain and, more generally, the nervous system. This Special Issue of the Journal of Personalized Medicine is devoted to collect original scientific articles that explore neurological diseases through the use of AI and ML approaches. In particular, we accept works that apply supervised and unsupervised learning, reinforcement learning, deep learning, and the more recent explainable and interpretable ML methodologies. Moreover, we seek to collect studies using and exploring different source of data, such as neuroimaging (structural MRI, functional MRI and Nirs), neurophysiology (TMS, EMG, EEG, MEG), biorobotics, and biomechanics (inertial, wearable, IoT sensors) applied to neurodegenerative diseases.

Contributions such as systematic reviews or meta-analyses on the above-mentioned topics are also welcome.

Dr. Alessia Sarica
Dr. Vera Gramigna
Dr. Maria Giovanna Bianco
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. Journal of Personalized Medicine 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 2000 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
  • machine learning
  • neuroimaging
  • neural and rehabilitation engineering
  • biorobotics and biomechanics
  • biomedical sensors and wearable systems
  • movement’s analysis
  • biomedical signal processing

Published Papers (1 paper)

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Research

Article
Real-World Testing of a Machine Learning–Derived Visual Scale for Tc99m TRODAT-1 for Diagnosing Lewy Body Disease: Comparison with a Traditional Approach Using Semiquantification
J. Pers. Med. 2022, 12(9), 1369; https://doi.org/10.3390/jpm12091369 - 25 Aug 2022
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Abstract
Objectives: Abnormal dopamine transporter (DAT) uptake is an important biomarker for diagnosing Lewy body disease (LBD), including Parkinson’s disease (PD) and dementia with Lewy bodies (DLB). We evaluated a machine learning-derived visual scale (ML-VS) for Tc99m TRODAT-1 from one center and compared it [...] Read more.
Objectives: Abnormal dopamine transporter (DAT) uptake is an important biomarker for diagnosing Lewy body disease (LBD), including Parkinson’s disease (PD) and dementia with Lewy bodies (DLB). We evaluated a machine learning-derived visual scale (ML-VS) for Tc99m TRODAT-1 from one center and compared it with the striatal/background ratio (SBR) using semiquantification for diagnosing LBD in two other centers. Patients and Methods: This was a retrospective analysis of data from a history-based computerized dementia diagnostic system. MT-VS and SBR among normal controls (NCs) and patients with PD, PD with dementia (PDD), DLB, or Alzheimer’s disease (AD) were compared. Results: We included 715 individuals, including 122 NCs, 286 patients with PD, 40 with AD, 179 with DLB, and 88 with PDD. Compared with NCs, patients with PD exhibited a significantly higher prevalence of abnormal DAT uptake using all methods. Compared with the AD group, PDD and DLB groups exhibited a significantly higher prevalence of abnormal DAT uptake using all methods. The distribution of ML-VS was significantly different between PD and NC, DLB and AD, and PDD and AD groups (all p < 0.001). The correlation coefficient of ML-VS/SBR in all participants was 0.679. Conclusions: The ML-VS designed in one center is useful for differentiating PD from NC, DLB from AD, and PDD from AD in other centers. Its correlation with traditional approaches using different scanning machines is also acceptable. Future studies should develop models using data pools from multiple centers for increasing diagnostic accuracy. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Neurological Diseases)
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