Improved visuospatial neglect after tDCS and computer-assisted cognitive training in Posterior Cortical Atrophy: a single-case study.

Visuospatial neglect (VN) frequently occurs in Posterior Cortical Atrophy (PCA) and requires specific rehabilitation. In this single-case study, we investigated the efficacy of a computer-assisted cognitive training (CCT) alone or coupled with multiple sessions of anodal transcranial direct-current stimulation (A-tDCS) over the right posterior parietal cortex in improving left VN symptoms in a patient with PCA. The digital ReMoVES platform was used for both VN assessment and training. We found a significant improvement after CCT combined with A-tDCS within the limits of a single-case, our results suggest, for the first time, the usefulness of this combined approach in a neurodegenerative disorder.

A Solution for the Remote Care of Frail Elderly Individuals via Exergames.

Internet of Things (IoT) solutions are a concrete answer to many needs in the healthcare framework since they enable remote support for patients and foster continuity of care. Currently, frail elderly people are among end users who most need and would benefit from IoT solutions from both a social and a healthcare point of view. Indeed, IoT technologies can provide a set of services to monitor the healthcare of the elderly or support them in order to reduce the risk of injuries, and preserve their motor and cognitive abilities. The main feature of IoT solutions for the elderly population is ease of use. Indeed, to fully exploit the potential of an IoT solution, patients should be able to autonomously deal with it. The remote-monitoring validation engineering system (ReMoVES) described here is an IoT solution that caters to the specific needs of frail elderly individuals. Its architecture was designed for use at rehabilitation centers and at patients' homes. The system is user-friendly and comfortably usable by persons who are not familiar with technology. In addition, exergames enhance patient engagement in order to curb therapy abandonment. Along with the technical presentation of the solution, a real-life scenario application is described referring to sit-to-stand activity.

A Study Protocol for Occupational Rehabilitation in Multiple Sclerosis.

Digital medical solutions can be very helpful in restorative neurology, as they allow the patients to practice their rehabilitation activities remotely. This work discloses ReMoVES, an IoMT system providing telemedicine services, in the context of Multiple Sclerosis rehabilitation, within the frame of the project STORMS. A rehabilitative protocol of exercises can be provided as ReMoVES services and integrated into the Individual Rehabilitation Project as designed by a remote multidimensional medical team. In the present manuscript, the first phase of the study is described, including the definition of the needs to be addressed, the employed technology, the design and the development of the exergames, and the possible practical/professional and academic consequences. The STORMS project has been implemented with the aim to act as a starting point for the development of digital telerehabilitation solutions that support Multiple Sclerosis patients, improving their living conditions. This paper introduces a study protocol and it addresses pre-clinical research needs, where system issues can be studied and better understood how they might be addressed. It also includes tools to favor remote patient monitoring and to support the clinical staff.

Use of superpixels for improvement of inter-rater and intra-rater reliability during annotation of medical images.

In the context of automatic medical image segmentation based on statistical learning, raters' variability of ground truth segmentations in training datasets is a widely recognized issue. Indeed, the reference information is provided by experts but bias due to their knowledge may affect the quality of the ground truth data, thus hindering creation of robust and reliable datasets employed in segmentation, classification or detection tasks. In such a framework, automatic medical image segmentation would significantly benefit from utilizing some form of presegmentation during training data preparation process, which could lower the impact of experts' knowledge and reduce time-consuming labeling efforts. The present manuscript proposes a superpixels-driven procedure for annotating medical images. Three different superpixeling methods with two different number of superpixels were evaluated on three different medical segmentation tasks and compared with manual annotations. Within the superpixels-based annotation procedure medical experts interactively select superpixels of interest, apply manual corrections, when necessary, and then the accuracy of the annotations, the time needed to prepare them, and the number of manual corrections are assessed. In this study, it is proven that the proposed procedure reduces inter- and intra-rater variability leading to more reliable annotations datasets which, in turn, may be beneficial for the development of more robust classification or segmentation models. In addition, the proposed approach reduces time needed to prepare the annotations.

Hepatic Elastometry and Glissonian Line in the Assessment of Liver Fibrosis.

The aim of this study was to identify a method for staging hepatic fibrosis using a non-invasive, rapid and inexpensive technique based on ultrasound morphologic hepatic features. A total of 215 patients with different liver diseases underwent B-mode (2-D brightness mode) ultrasonography, vibration-controlled transient elastography, 2-D shear wave elastography and measurement of the controlled attenuation parameter with transient elastography. B-Mode images of the anterior margin of the left lobe were obtained and processed with automatic Genoa Line Quantification (GLQ) software based on a neural network for staging liver fibrosis. The accuracy of GLQ was 90.6% during model training and 78.9% in 38 different patients with concordant elastometric measures. Receiver operating characteristic curve analysis of GLQ performance using vibration-controlled transient elastography as a reference yielded areas under the curves of 0.851 for F ≥ F1, 0.793 for F ≥ F2, 0.784 for F ≥ F3 and 0.789 for F ≥ F4. GLQ has the potential to be a rapid, easy-to-perform and tolerable method in the staging of liver fibrosis.

On the performance assessment during the practice of an exergame for cerebellar ataxia patients.

Cerebellar ataxias are a large family of movement disorders that generally follow a stroke. The clinical picture is very complicated and normal activities become difficult for ataxic patients. For instance, dynamic ataxia involves both walk and upper-limbs movement, thus affecting the possibility to fulfill daily life tasks. Rehabilitation treatments and strategies for cerebellar ataxia are nowadays controversial, since different opinions on the several approaches are spread among the clinical community. The purpose of the present work is not to shed some light on such disagreements. Indeed, here a solution for delivering rehabilitation activities in the form of videogame is presented. Data related to patient's performance are collected and analyzed in order to provide the clinical staff with objective indicators that properly describe the activity. Such information can also be used to discuss the effectiveness and the incidence of some strategy adopted for fulfilling some task. The experimental phase is conducted on two case-studies with regards to the upper-limb rehabilitation. The adoption of the strategy of weighting the limb when performing the movement is discussed. The indicators computed in both sessions with and without strategy are compared, also referring to the practice of some healthy subjects. The present work introduces the preliminary phase of a wider study and foretells its future development conducted on a larger population.

Unilateral spatial neglect rehabilitation supported by a digital solution: two case-studies.

Digital solutions for unilateral spatial neglect (USN) assessment and treatment are nowadays of great interest, because of both the possibility of combining them with other rehabilitation practices, and the easy-to-understand data and indicators they collect. The ReMoVES platform, developed in DITEN laboratories, is conceived in the Assistive Technologies framework and provides motor and cognitive exergames and activities to be performed in conjunction with traditional rehabilitation. In this work, two case-studies, related to the USN rehabilitation, are presented. The combination of cognitive therapy, delivered by the ReMoVES platform, and transcranial direct-current stimulation (tDCS) technique was used as rehabilitation treatment for both the patients. Data collected at the beginning or at the end of the rehabilitation process, or also during the treatment sessions, are shown and discussed in this paper. This work is a preliminary part of a wider one, that will be conducted involving many different rehabilitation centers, aimed at proving the validity of such an approach to USN treatments.

Quantification of ultrasound imaging in the staging of hepatic fibrosis.

BACKGROUND: The need for a staging of hepatic fibrosis has become particularly urgent in the last few years in order to start new therapeutic treatments. The objective of this study is to identify ultrasound descriptors and achieve a staging of hepatic fibrosis with non-invasive, rapid and inexpensive methods, both as an alternative and a support to the ultrasound elastography examination. METHODS: This study evaluated 196 patients under treatment at the Alcohological Regional Center. An image regarding a scanning of the epigastrium with a high-frequency linear probe (7.5-12 MHz) has been selected for quantification. The hyperechogenic line corresponding to the Glisson capsule on the hepatic segment III has been evaluated with the Genoa Line Quantification (GLQ) software. These data have been compared with the shearwave ultrasound elastography. RESULTS: The best discrimination between patients with medium-advanced fibrosis (F3-F4) and healthy patients or patients with no fibrosis or mild fibrosis (F0-F1) has been achieved using the three parameters of variance and mean of gradient and line continuity. In particular, a sensitivity of 74%, a specificity of 82%, positive predictive value 80.4%, negative predictive value 75.9% and an accuracy of 78% has been obtained. CONCLUSIONS: GQL has allowed a classification, which is well concordant with the ultrasound elastography data. The use of the GQL may also be developed within centers, which are not provided with ultrasound elastography techniques, when an ultrasound elastography examination cannot be performed due to technical problems and as a support to elastography, if the outcome of this examination is not sufficiently clear.

Multitemporal Volume Registration for the Analysis of Rheumatoid Arthritis Evolution in the Wrist.

This paper describes a method based on an automatic segmentation process to coregister carpal bones of the same patient imaged at different time points. A rigid registration was chosen to avoid artificial bone deformations and to allow finding eventual differences in the bone shape due to erosion, disease regression, or other eventual pathological signs. The actual registration step is performed on the basis of principal inertial axes of each carpal bone volume, as estimated from the inertia matrix. In contrast to already published approaches, the proposed method suggests splitting the 3D rotation into successive rotations about one axis at a time (the so-called basic or elemental rotations). In such a way, singularity and ambiguity drawbacks affecting other classical methods, for instance, the Euler angles method, are addressed. The proposed method was quantitatively evaluated using a set of real magnetic resonance imaging (MRI) sequences acquired at two different times from healthy wrists and by choosing a direct volumetric comparison as a cost function. Both the segmentation and registration steps are not based on a priori models, and they are therefore able to obtain good results even in pathological cases, as proven by the visual evaluation of actual pathological cases.

Automatic MPST-cut for segmentation of carpal bones from MR volumes.

In the context of rheumatic diseases, several studies suggest that Magnetic Resonance Imaging (MRI) allows the detection of the three main signs of Rheumatoid Arthritis (RA) at higher sensitivities than available through conventional radiology. The rapid, accurate segmentation of bones is an essential preliminary step for quantitative diagnosis, erosion evaluation, and multi-temporal data fusion. In the present paper, a new, semi-automatic, 3D graph-based segmentation method to extract carpal bone data is proposed. The method is unsupervised, does not employ any a priori model or knowledge, and is adaptive to the individual variability of the acquired data. After selecting one source point inside the Region of Interest (ROI), a segmentation process is initiated, which consists of two automatic stages: a cost-labeling phase and a graph-cutting phase. The algorithm finds optimal paths based on a new cost function by creating a Minimum Path Spanning Tree (MPST). To extract the region, a cut of the obtained tree is necessary. A new criterion of the MPST-cut based on compactness shape factor was conceived and developed. The proposed approach is applied to a large database of 96 T1-weighted MR bone volumes. Performance quality is evaluated by comparing the results with gold-standard bone volumes manually defined by rheumatologists through the computation of metrics extracted from the confusion matrix. Furthermore, comparisons with the existing literature are carried out. The results show that this method is efficient and provides satisfactory performance for bone segmentation on low-field MR volumes.