Biologically Inspired Near Extinct System Reconstruction

Recovery software system operations from a state of extensive damage without human intervention is a challenging problem as it may need to be based on a different infrastructure from the one that the system was originally designed for and deployed on (i.e., computational and communication devices) and significant reorganization of system functionalities. In this paper, we introduce a bio-inspired approach for reconstructing nearly extinct complex software systems. Our approach is based on encoding a computational DNA (co-DNA) of a system and computational analogues of biological processes to enable the transmission of co-DNA over computational devices and, through it, the transformation of these devices into system cells that can realise chunks of the system functionality, and spread further its reconstruction process

Analysis of the sentiments of the participants in a clinical study to evaluate a balance rehabilitation intervention delivered by a Virtual Coach

Multiple studies for balance rehabilitation interventions have been accomplished aiming to demonstrate that sensory interventions and cognitive functionality are crucial for postural control and improvement of the quality of patient's daily life. However, none of the existing studies is filling the lack of expert physiotherapists availability. A pilot randomized study was conducted to assess the acceptability of the HOLOBalance telerehabilitation system. HOLOBalance is an interactive AR rehabilitation system which encompasses multi-sensory training program to enhance balance and cognitive coaching, for older adults at falls risk. In this work, we present a sentiment analysis of the patients participating in this study using the VADER methodology to evaluate and quantify their attitude towards the HOLOBalance system. Our results highlight the importance of findings positive polarity towards the AR interaction, which is based on the use of a holographic virtual physiotherapist. The compound score of 0.185 indicates the valuable positive feedback gained from the user experience

Deep learning for diabetic retinopathy detection and classification based on fundus images: A review.

Diabetic Retinopathy is a retina disease caused by diabetes mellitus and it is the leading cause of blindness globally. Early detection and treatment are necessary in order to delay or avoid vision deterioration and vision loss. To that end, many artificial-intelligence-powered methods have been proposed by the research community for the detection and classification of diabetic retinopathy on fundus retina images. This review article provides a thorough analysis of the use of deep learning methods at the various steps of the diabetic retinopathy detection pipeline based on fundus images. We discuss several aspects of that pipeline, ranging from the datasets that are widely used by the research community, the preprocessing techniques employed and how these accelerate and improve the models' performance, to the development of such deep learning models for the diagnosis and grading of the disease as well as the localization of the disease's lesions. We also discuss certain models that have been applied in real clinical settings. Finally, we conclude with some important insights and provide future research directions

A huge posteromedial mediastinal cyst complicated with vertebral dislodgment

BACKGROUND: Mediastinal cysts compromise almost 20% of all mediastinal masses with bronchogenic subtype accounting for 60% of all cystic lesions. Although compression of adjoining soft tissues is usual, spinal complications and neurological symptoms are outmost rare and tend to characterize almost exclusively the neuroenteric cysts. CASE PRESENTATION: A young patient with intermittent, dull pain in his back and free medical history presented in the orthopaedic department of our hospital. There, the initial clinical and radiologic evaluation revealed a mediastinal mass and the patient was referred to the thoracic surgery department for further exploration. The following computed tomography (CT) and magnetic resonance imaging (MRI) shown a huge mediastinal cyst compressing the T4-T6 vertebral bodies. The neurological symptoms of the patient were attributed to this specific pathology due to the complete agreement between the location of the cyst and the nervous rule area of the compressed thoracic vertebrae. Despite our strongly suggestions for surgery the patient denied any treatment. CONCLUSION: In controversy with the common faith that the spine plays the role of the natural barrier to the further expansion of cystic lesions, our case clearly indicates that, exceptionally, mediastinal cysts may cause severe vertebral complications. Therefore, early excision should be considered especially in young patients or where close follow up is uncertain

Mining balance disorders' data for the development of diagnostic decision support systems

In this work we present the methodology for the development of the EMBalance diagnostic Decision Support System (DSS) for balance disorders. Medical data from patients with balance disorders have been analysed using data mining techniques for the development of the diagnostic DSS. The proposed methodology uses various data, ranging from demographic characteristics to clinical examination, auditory and vestibular tests, in order to provide an accurate diagnosis. The system aims to provide decision support for general practitioners (GPs) and experts in the diagnosis of balance disorders as well as to provide recommendations for the appropriate information and data to be requested at each step of the diagnostic process. Detailed results are provided for the diagnosis of 12 balance disorders, both for GPs and experts. Overall, the reported accuracy ranges from 59.3 to 89.8% for GPs and from 74.3 to 92.1% for experts

Isolated thumb carpometacarpal joint dislocation: a case report and review of the literature

<p>Abstract</p> <p>Background</p> <p>Isolated thumb carpometacarpal dislocation is a rare injury pattern and the optimal treatment option is still controversial.</p> <p>Case Description</p> <p>We present a 27-year-old basketball player who underwent an isolated dorsal dislocation of the thumb carpometacarpal joint after a fall. The dislocation was successfully reduced by closed means but the joint was found to be grossly unstable. Due to inherent instability, repair of the ruptured dorsoradial ligament and joint capsule was performed.</p> <p>The ligament was detached from its proximal insertion into trapezium and subsequently stabilized via suture anchors. The torn capsule was repaired in an end-to-end fashion and immobilization of the joint was applied for 6 weeks.</p> <p>Results</p> <p>At 3-year follow up evaluation the patient was pain free and returned to his previous level of activity. No restriction of carpometacrpal movements or residual instability was noticed. Radiographic examination showed normal joint alignment and no signs of subluxation or early osteoarthritis.</p> <p>Conclusion</p> <p>Surgical stabilization of the dorsal capsuloligamentous complex may be considered the selected treatment option in isolated carpometacarpal joint dislocations, that remain unstable after closed reduction in young and high demand patients.</p> <p><b>Level of Clinical Evidence: </b>Level IV</p