This paper investigates the spatial distribution of the electric field and of the current density in the brain tissues induced by transcranial direct current stimulation of the primary motor cortex. A numerical method was applied on a realistic human head model to calculate these field distributions in different brain structures, such as the cortex, the white matter, the cerebellum, the hippocampus, the medulla oblongata, the pons, the midbrain, and the thalamus. The influence of varying the anode area, the cathode area, and the injected current was also investigated. An electrode area as the one typically used in clinical practice (i.e., both electrodes equal to 35 cm²) resulted into complex and diffuse amplitude distributions over all the examined brain structures, with the region of maximum induced field being below or close to the anode. Variations in either the anode or cathode area corresponded to changes in the field amplitude distribution in all the brain tissues, with the former variation producing more diffuse effects. Variations in the injected current resulted, as could be expected, in linearly correlated changes in the field amplitudes.

The application of machine learning and artificial intelligence techniques in the medical world is growing, with a range of purposes: from the identification and prediction of possible diseases to patient monitoring and clinical decision support systems. Furthermore, the widespread use of remote monitoring medical devices, under the umbrella of the “Internet of Medical Things” (IoMT), has simplified the retrieval of patient information as they allow continuous monitoring and direct access to data by healthcare providers. However, due to possible issues in real-world settings, such as loss of connectivity, irregular use, misuse, or poor adherence to a monitoring program, the data collected might not be sufficient to implement accurate algorithms. For this reason, data augmentation techniques can be used to create synthetic datasets sufficiently large to train machine learning models. In this work, we apply the concept of generative adversarial networks (GANs) to perform a data augmentation from patient data obtained through IoMT sensors for Chronic Obstructive Pulmonary Disease (COPD) monitoring. We also apply an explainable AI algorithm to demonstrate the accuracy of the synthetic data by comparing it to the real data recorded by the sensors. The results obtained demonstrate how synthetic datasets created through a well-structured GAN are comparable with a real dataset, as validated by a novel approach based on machine learning.

The number and variety of eHealth services for adults and older adults who use hearing aids (HAs) are growing rapidly. This area holds promise to increase cost-efficiency, enable better access to care, and improve patient outcomes and satisfaction. Despite the increasing interest in this field, an up-to-date picture of recent research in the area of eHealth for adults with HAs is lacking. In this state-of-the-art review we assessed the literature from the past decade about eHealth use in the HA adult patient journey. Systematic searches were conducted in CINAHL, PubMed, Scopus, and Web of Science. A total of 34 peer-reviewed empirical records were identified from the searches and from the reference lists of searched records. Records were characterized based on: eHealth platform (i.e.: offline, Internet-based, or mobile-based), service [i.e.: education and information, screening and assessment, hearing rehabilitation, or general (tele-audiology)], and phase of the patient journey (i.e.: pre-fitting, fitting, or post-fitting). The review highlighted a growing interest in the field, as revealed by an increasing trend over the search period, from 2 records in 2009–2010 up to 17 records in 2015–2016. Internet-based platforms were the most frequently used (present in more than half of the included records), with a stable trend in the period. About one-third of the records introduced services over offline platforms, whereas mobile-based platforms were used only in 6 out of 34 records, suggesting that the clinical uptake of mobile services is still limited compared to more mature offline and Internet-based platforms. Most of the eHealth services observed were related to the areas of education and information (42.5%) and hearing rehabilitation (40.4%), whereas 10.7% were related to screening and assessment, and 6.4% to general tele-audiology services. Many services covered different phases of the patient journey, especially the fitting and post-fitting phases. Overall, this review showed that the field of eHealth in the context of HA rehabilitation in adults has grown in the recent past. Research is still needed to increase the uptake and efficacy of eHealth in clinical practice, especially in terms of technology developments, technical and clinical validation, and optimization of strategies for service delivery.

Purpose: Our research aims at the identification and assessment of applications (referred to as apps ) in the hearing health care domain. This research forum article presents an overview of the current availability, affordability, and variety of hearing-related apps. Method: The available apps were reviewed by searching on the leading platforms (iOS, Android, Windows Phone stores) using the keywords hearing, audiology, audio, auditory, speech, language, tinnitus, hearing loss, hearing aid, hearing system, cochlear implant, implantable device, auditory training, hearing rehabilitation, and assistive technology/tool/device. On the basis of the offered services, apps were classified into 4 application domains: (a) screening and assessment, (b) intervention and rehabilitation, (c) education and information, and (d) assistive tools. Conclusions: A large variety of apps are available in the hearing health care domain. These cover a wide range of services for people with hearing or communication problems as well as for hearing professionals, families, or informal caregivers. This evolution can potentially bring along considerable advantages and improved outcomes in the field of hearing health care. Nevertheless, potential risks and threats (e.g., safety, quality, effectiveness, privacy, and regulation) should not be overlooked. Significant research—particularly in terms of assessment and guidance—is still needed for the informed, aware, and safe adoption of hearing-related apps by patients and professionals.