Towards Transforming Neurorehabilitation: The Impact of Artificial Intelligence on Diagnosis and Treatment of Neurological Disorders.

Neurological disorders like stroke, spinal cord injury (SCI), and Parkinson's disease (PD) significantly affect global health, requiring accurate diagnosis and long-term neurorehabilitation. Artificial intelligence (AI), such as machine learning (ML), may enhance early diagnosis, personalize treatment, and optimize rehabilitation through predictive analytics, robotic systems, and brain-computer interfaces, improving outcomes for patients. This systematic review examines how AI and ML systems influence diagnosis and treatment in neurorehabilitation among neurological disorders.

Special Issue: "Intelligent Systems for Clinical Care and Remote Patient Monitoring".

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Benefits of Telerehabilitation for Patients With Severe Acquired Brain Injury: Promising Results From a Multicenter Randomized Controlled Trial Using Nonimmersive Virtual Reality.

Background: In neurorehabilitation, the use of innovative technologies offers many opportunities to monitor and improve the health status of patients with severe acquired brain injury (SABI). Telerehabilitation allows for continuity of service through the entire rehabilitation cycle, including assessment, intervention, consultation, and education, affording early reintegration and positively enhancing the quality of life (QoL).

Objective: The main purpose of this multicenter randomized controlled trial was to test the effectiveness of advanced training provided using a nonimmersive virtual reality rehabilitation system (ie, the VRRS HomeKit device) in improving functional outcomes in patients with SABI.

Deep learning applications in telerehabilitation speech therapy scenarios.

Nowadays, many application scenarios benefit from automatic speech recognition (ASR) technology. Within the field of speech therapy, in some cases ASR is exploited in the treatment of dysarthria with the aim of supporting articulation output. However, in presence of atypical speech, standard ASR approaches do not provide any reliable result in terms of voice recognition due to main issues, including: (i) the extreme intra and inter-speakers variability of the speech in presence of speech impairments, such as dysarthria; (ii) the absence of dedicated corpora containing voice samples from users with a speech disability to train a state-of-the-art speech model, particularly in non-English languages.

Recent Considerations on Gaming Console Based Training for Multiple Sclerosis Rehabilitation.

Multiple Sclerosis (MS) is a well-known, chronic demyelinating disease of the Central Nervous System (CNS) and one of the most common causes of disability in young adults. In this context, one of the major challenges in patients' rehabilitation is to maintain the gained motor abilities in terms of functional independence. This could be partially obtained by applying new emerging and cutting-edge virtual/augmented reality and serious game technologies for a playful, noninvasive treatment that was demonstrated to be quite efficient and effective in enhancing the clinical status of patients and their (re)integration into society.

Infrared Monitoring of Oxygenation Process Generated by Robotic Verticalization in Bedridden People.

Bedridden people, especially at home, suffer from diverse pathologies beyond the main one that brings them to a specific position. Long-term cares are suitable at home to avoid congestions within hospital facilities. There are different technologies available to improve such people's conditions in their daily life.

Blockchain-Based Healthcare Workflow for Tele-Medical Laboratory in Federated Hospital IoT Clouds.

In a pandemic situation such as that we are living at the time of writing of this paper due to the Covid-19 virus, the need of tele-healthcare service becomes dramatically fundamental to reduce the movement of patients, thence reducing the risk of infection. Leveraging the recent Cloud computing and Internet of Things (IoT) technologies, this paper aims at proposing a tele-medical laboratory service where clinical exams are performed on patients directly in a hospital by technicians through IoT medical devices and results are automatically sent via the hospital Cloud to doctors of federated hospitals for validation and/or consultation. In particular, we discuss a distributed scenario where nurses, technicians and medical doctors belonging to different hospitals cooperate through their federated hospital Clouds to form a virtual health team able to carry out a healthcare workflow in secure fashion leveraging the intrinsic security features of the Blockchain technology.

Telerehabilitation in individuals with severe acquired brain injury: Rationale, study design, and methodology.

Background: Severe acquired brain injury (SABI) rehabilitation should be as intensive and long as to allow the patients get the best independence and quality of life (QoL), but facing with the rehabilitation socioeconomic burden. Telerehabilitation (TR) could supply frail subjects requiring long-term rehabilitation.

Methods: A multicenter, prospective, parallel design, single-blind trial will be conducted at the IRCCS Neurolesi Bonino Pulejo (Messina, Italy) and IRCCS Hospital San Camillo (Venice, Italy) involving patients suffering from SABI and requiring home motor and cognitive rehabilitation.

Why Deep Learning Is Changing the Way to Approach NGS Data Processing: A Review.

Nowadays, big data analytics in genomics is an emerging research topic. In fact, the large amount of genomics data originated by emerging next-generation sequencing (NGS) techniques requires more and more fast and sophisticated algorithms. In this context, deep learning is re-emerging as a possible approach to speed up the DNA sequencing process.

Are Next-Generation Sequencing Tools Ready for the Cloud?

Cloud-based next-generation sequencing (NGS) tools are currently at an early stage. In this Forum article, we provide a clear picture of the current cloud-based NGS solutions and highlight what is still missing, along with future challenges for the achievement of an ecosystem of biotechnology clouds.

An OAIS-Based Hospital Information System on the Cloud: Analysis of a NoSQL Column-Oriented Approach.

The Open Archive Information System (OAIS) is a reference model for organizing people and resources in a system, and it is already adopted in care centers and medical systems to efficiently manage clinical data, medical personnel, and patients. Archival storage systems are typically implemented using traditional relational database systems, but the relation-oriented technology strongly limits the efficiency in the management of huge amount of patients' clinical data, especially in emerging cloud-based, that are distributed. In this paper, we present an OAIS healthcare architecture useful to manage a huge amount of HL7 clinical documents in a scalable way.

Enabling Secure XMPP Communications in Federated IoT Clouds Through XEP 0027 and SAML/SASL SSO.

Nowadays, in the panorama of Internet of Things (IoT), finding a right compromise between interactivity and security is not trivial at all. Currently, most of pervasive communication technologies are designed to work locally. As a consequence, the development of large-scale Internet services and applications is not so easy for IoT Cloud providers.

Embedded systems for supporting computer accessibility.

Nowadays, customized AT software solutions allow their users to interact with various kinds of computer systems. Such tools are generally available on personal devices (e.g.

Providing Assistive Technology Applications as a Service Through Cloud Computing.

Users with disabilities interact with Personal Computers (PCs) using Assistive Technology (AT) software solutions. Such applications run on a PC that a person with a disability commonly uses. However the configuration of AT applications is not trivial at all, especially whenever the user needs to work on a PC that does not allow him/her to rely on his / her AT tools (e.