Advancements in Artificial Intelligence for Neurodegenerative Diseases Assessment

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

Deadline for manuscript submissions: closed (30 November 2020) | Viewed by 10104

Special Issue Editors


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Guest Editor
Department of Computer Science, University of Bari, Bari, Italy
Interests: machine learning; deep learning; pattern recognition; computer vision; health informatics; biometrics
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Physics, University of Bari, Bari, Italy
Interests: machine learning; pattern recognition; neuroscience; complex networks; brain connectivity

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Guest Editor
Department of Electrical and Information Engineering and Applied Mathematics (DIEM), University of Salerno, Salerno, Italy
Interests: e-health; explainable artificial intelligence; Parkinson disease; machine learning; evolutionary computation; neurocomputational models and pattern recognition
Special Issues, Collections and Topics in MDPI journals

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

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Keywords

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

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Published Papers (1 paper)

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13 pages, 8936 KiB  
Article
Just Don’t Fall: An AI Agent’s Learning Journey Towards Posture Stabilisation
by Mohammed Hossny and Julie Iskander
AI 2020, 1(2), 286-298; https://doi.org/10.3390/ai1020019 - 15 Jun 2020
Cited by 5 | Viewed by 7233
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
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