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Application of Artificial Intelligence in Neurological Diseases
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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: closed (25 August 2023) | Viewed by 3232

Special Issue Editors

Dr. Alessia Sarica

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Guest Editor
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
Dr. Vera Gramigna

E-Mail Website
Guest Editor
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
Dr. Maria Giovanna Bianco

E-Mail Website
Guest Editor
Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Viale Europa, 88100 Catanzaro, Italy
Interests: biomedical signal processing (EMG, EEG, ECoG, and LFP); wearable medical devices; machine learning; structural MRI analysis
Special Issues, Collections and Topics in MDPI journals

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 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
  • machine learning
  • neuroimaging
  • neural and rehabilitation engineering
  • biorobotics and biomechanics
  • biomedical sensors and wearable systems
  • movement’s analysis
  • biomedical signal processing

Published Papers (2 papers)

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10 pages, 650 KiB  
Article
Paving the Way for Predicting the Progression of Cognitive Decline: The Potential Role of Machine Learning Algorithms in the Clinical Management of Neurodegenerative Disorders
by Caterina Formica, Lilla Bonanno, Fabio Mauro Giambò, Giuseppa Maresca, Desiree Latella, Angela Marra, Fabio Cucinotta, Carmen Bonanno, Marco Lombardo, Orazio Tomarchio, Angelo Quartarone, Silvia Marino, Rocco Salvatore Calabrò and Viviana Lo Buono
J. Pers. Med. 2023, 13(9), 1386; https://doi.org/10.3390/jpm13091386 - 15 Sep 2023
Cited by 3 | Viewed by 932
Abstract
Alzheimer’s disease (AD) is the most common form of neurodegenerative disorder. The prodromal phase of AD is mild cognitive impairment (MCI). The capacity to predict the transitional phase from MCI to AD represents a challenge for the scientific community. The adoption of artificial [...] Read more.
Alzheimer’s disease (AD) is the most common form of neurodegenerative disorder. The prodromal phase of AD is mild cognitive impairment (MCI). The capacity to predict the transitional phase from MCI to AD represents a challenge for the scientific community. The adoption of artificial intelligence (AI) is useful for diagnostic, predictive analysis starting from the clinical epidemiology of neurodegenerative disorders. We propose a Machine Learning Model (MLM) where the algorithms were trained on a set of neuropsychological, neurophysiological, and clinical data to predict the diagnosis of cognitive decline in both MCI and AD patients. Methods: We built a dataset with clinical and neuropsychological data of 4848 patients, of which 2156 had a diagnosis of AD, and 2684 of MCI, for the Machine Learning Model, and 60 patients were enrolled for the test dataset. We trained an ML algorithm using RoboMate software based on the training dataset, and then calculated its accuracy using the test dataset. Results: The Receiver Operating Characteristic (ROC) analysis revealed that diagnostic accuracy was 86%, with an appropriate cutoff value of 1.5; sensitivity was 72%; and specificity reached a value of 91% for clinical data prediction with MMSE. Conclusion: This method may support clinicians to provide a second opinion concerning high prognostic power in the progression of cognitive impairment. The MLM used in this study is based on big data that were confirmed in enrolled patients and given a credibility about the presence of determinant risk factors also supported by a cognitive test score. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Neurological Diseases)
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10 pages, 1175 KiB  
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
by Pai-Yi Chiu, Po-Nien Hou, Guang-Uei Hung, Te-Chun Hsieh, Pak-Ki Chan and Chia-Hung Kao
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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