Special Issue "Advancements in Artificial Intelligence for Neurodegenerative Diseases Assessment"

A special issue of AI (ISSN 2673-2688).

Deadline for manuscript submissions: 30 November 2020.

Special Issue Editors

Dr. Gennaro Vessio
Website
Guest Editor
Department of Computer Science, University of Bari, Italy
Interests: machine learning; deep learning; pattern recognition; computer vision; health informatics; biometrics
Special Issues and Collections in MDPI journals
Dr. Eufemia Lella
Website
Guest Editor
Department of Physics, University of Bari, Italy
Interests: Machine Learning; Pattern Recognition; Neuroscience; Complex Networks; Brain Connectivity
Dr. Rosa Senatore

Guest Editor
Department of Electrical and Information Engineering and Applied Mathematics, Università degli Studi di Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano, Italy
Interests: e-Health; explainable artificial intelligence; Parkinson Disease; Machine Learning; evolutionary computation; neurocomputational models and pattern recognition

Special Issue Information

Dear Colleagues,

Artificial intelligence and machine learning can change the way we think of health care from many perspectives. One application, in particular, concerns developing computer-aided diagnosis systems to provide clinicians with novel non-invasive and low-cost support tools. These systems can have a crucial role, especially if we consider degenerative brain disorders, such as Parkinson’s disease and Alzheimer’s disease, which represent a really growing health problem. At the early stages of these diseases, the patient may be characterised by minimal changes, not enough to meet the standard criteria for a specific pathology. Predictive models come in handy as they can detect subtle but meaningful patterns, which may be overlooked by the human expert.

Significant advancements in this context have been obtained, in the last years, in neuroimaging, particularly functional magnetic resonance imaging and diffusion weighted imaging. More recently, a growing research interest has arisen towards the application of behavioural biometric traits. Examples include handwriting, speech and gait. The aim of this Special Issue is to bring together researchers from neuroscience and biometrics, improving the relationship between these two research communities. This Special Issue calls for original manuscripts proposing artificial intelligence and machine learning methods, based on neuroimaging as well as biometric traits, for neurodegenerative diseases assessment. Therefore, research proposing multimodal approaches, exploring data of diverse nature, is especially welcome.

Dr. Gennaro Vessio
Dr. Eufemia Lella
Dr. Rosa Senatore
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.ynsqex.icu 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 papers will be 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. AI is an international peer-reviewed open access quarterly 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 1000 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

  • Neurodegeneration
  • Biomarkers
  • Machine learning
  • Deep learning
  • Computer-aided diagnosis
  • Neuroimaging
  • Handwriting analysis
  • Speech analysis
  • Gait analysis
  • Multimodal approaches

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
Just Don’t Fall: An AI Agent’s Learning Journey Towards Posture Stabilisation
AI 2020, 1(2), 286-298; https://doi.org/10.3390/ai1020019 - 15 Jun 2020
Abstract
Learning to maintain postural balance while standing requires a significant, fine coordination effort between the neuromuscular system and the sensory system. It is one of the key contributing factors towards fall prevention, especially in the older population. Using artificial intelligence (AI), we can [...] Read more.
Learning to maintain postural balance while standing requires a significant, fine coordination effort between the neuromuscular system and the sensory system. It is one of the key contributing factors towards fall prevention, especially in the older population. Using artificial intelligence (AI), we can similarly teach an agent to maintain a standing posture, and thus teach the agent not to fall. In this paper, we investigate the learning progress of an AI agent and how it maintains a stable standing posture through reinforcement learning. We used the Deep Deterministic Policy Gradient method (DDPG) and the OpenSim musculoskeletal simulation environment based on OpenAI Gym. During training, the AI agent learnt three policies. First, it learnt to maintain the Centre-of-Gravity and Zero-Moment-Point in front of the body. Then, it learnt to shift the load of the entire body on one leg while using the other leg for fine tuning the balancing action. Finally, it started to learn the coordination between the two pre-trained policies. This study shows the potentials of using deep reinforcement learning in human movement studies. The learnt AI behaviour also exhibited attempts to achieve an unplanned goal because it correlated with the set goal (e.g., walking in order to prevent falling). The failed attempts to maintain a standing posture is an interesting by-product which can enrich the fall detection and prevention research efforts. Full article
Show Figures

Graphical abstract

Back to TopTop 宝马游戏手机网站 网上在线配资炒股公司 江苏11选5网上购买 内蒙古快3综合走势图 江西十一选五稳定计划 北京11选5综合走势图 山西十一选五推荐号码推荐 全国最大彩票论坛 股票交易软件排行榜 河北快三走势图牛 浙江11选五最大遗漏数据 河北快3号码统计表 吉林十一选五分布基本走势 上海快3走势图 大乐透2020年春节休市时间 赌场扑克牌有什么玩法 股票的交易规则