Serious games for Parkinson's Disease management as implemented in PROPHETIC platform.

In this paper, we describe the serious games, integrated into PROPHETIC which is an innovating personal healthcare service for a holistic remote management of Parkinson's disease (PD) patients. The main objective of the three developed serious games is to allow health professionals to remotely monitor and appraise the overall physical status of their patients. The significant benefits for the patients, making use of this platform, is the improvement of their engagement, empowerment and, consequently, the provision of education about their condition and its management. The design of the serious games was based on the clinical needs derived from the literature and their primary target is to assess and record specific physical capabilities of the patient. All the games scores and the recorded parameters are gathered and also presented to the clinicians, offering them a precise overview of the patient's motor status and the possibility to modify the therapeutic plan, if required.

Bioinformatics Analysis Reveals Ki-67 Specific microRNA Functions in Pediatric Embryonal Tumors.

Pediatric Central Nervous System (CNS) neoplasms are the second most prevalent tumors of childhood. CNS malignancies are considered as the most notorious type of tumors, due to their anatomic position manifesting an imminent threat to the patients' life. miRNAs are molecules that play a significant role in CNS tumor biology. At the same time diagnostic markers such as Ki-67 have played an important role in CNS tumor diagnosis. In a previous study we have identified several miRNAs, common to different subtypes of pediatric embryonal CNS malignancies as well as, we have identified miRNAs that manifest significant dynamics with respect to their expression and the neoplasmatic subtype. Among the previously reported miRNAs, several have manifested significant differences with respect to Ki-67 expression. Those miRNAs, were further analyzed bioinformatically and related functions were revealed, where some of them confirmed Ki-67 role as a proliferation marker but also predicted novel miRNAs functions in pediatric embryonal tumors.

Bioinformatics and Regression Analyses Manifest Tumor-Specific miRNA Expression Dynamics in Pediatric Embryonal Malignancies.

Pediatric Central Nervous System (CNS) neoplasms are the second most prevalent tumors of childhood. Further on, prognosis of this type of neoplasms still remain poor and the comprehension of the etiology and pathogenesis of the disease still remains scarce. Several reports have identified microRNAs as significant molecules in the development of central nervous system tumors and propose that they might compose key molecules underlying oncogenesis. In a previous study we have identified several miRNAs, common to different subtypes of pediatric embryonal CNS malignancies as well as, we have identified miRNAs that manifest significant dynamics with respect to their expression and the neoplasmatic subtype. Overall, 19 tumor cases from children diagnosed with embryonal brain tumors were investigated. As controls, children who suffered a sudden death underwent autopsy and were not present with any brain malignancy were used (13 samples of varying localization). Our experimental approach included microarrays covering 1211 miRNAs, which appeared to manifest tumor-specific dynamics. In conclusion, it appeared that certain miRNAs are neoplasm specific and in particular, their expression manifests linear dynamics. Thus, the investigation of miRNA expression in pediatric embryonal brain tumors might contribute towards the discovery of tumor-specific miRNA signatures, which could potentially afford the identification of gene-specific biomarkers related to diagnosis, prognosis and patient targeted therapy, as well as help us understand oncogenetic dynamics.

A computer-aided automated methodology for the detection and classification of occlusal caries from photographic color images.

The aim of this work is to present a computer-aided automated methodology for the assessment of carious lesions, according to the International Caries Detection and Assessment System (ICDAS II), which are located on the occlusal surfaces of posterior permanent teeth from photographic color tooth images. The proposed methodology consists of two stages: (a) the detection of regions of interest and (b) the classification of the detected regions according to ICDAS ΙΙ. In the first stage, pre-processing, segmentation and post-processing mechanisms were employed. For each pixel of the detected regions, a 15×15 neighborhood is used and a set of intensity-based and texture-based features were extracted. A correlation based technique was applied to select a subset of 36 features which were given as input into the classification stage, where five classifiers (J48, Random Tree, Random Forests, Support Vector Machines and Naïve Bayes) were compared to conclude to the best one, in our case, to Random Forests. The methodology was evaluated on a set of 103 digital color images where 425 regions of interest from occlusal surfaces of extracted permanent teeth were manually segmented and classified, based on visual assessments by two experts. The methodology correctly detected 337 out of 340 regions in the detection stage with accuracy of detection 80%. For the classification stage an overall accuracy 83% is achieved. The proposed methodology provides an objective and fully automated caries diagnostic system for occlusal carious lesions with similar or better performance of a trained dentist taking into consideration the available medical knowledge.

Occlusal caries detection using random walker algorithm: a graph approach.

The aim of this work is to present a modification of the Random Walker algorithm for the segmentation of occlusal caries from photographic color images. The modification improves the detection and time execution performance of the classical Random Walker algorithm and also deals with the limitations and difficulties that the specific type of images impose to the algorithm. The proposed modification consists of eight steps: 1) definition of the seed points, 2) conversion of the image to gray scale, 3) application of watershed transformation, 4) computation of the centroid of each region, 5) construction of the graph, 6) application of the Random Walker algorithm, 7) smoothing and extraction of the perimeter of the regions of interest and 8) overlay of the results. The algorithm was evaluated using a set of 96 images where 339 areas of interest were manually segmented by an expert. The obtained segmentation accuracy is 93%.

Smart adaptable system for older adults' Daily Life Activities Management - The ABLE platform.

In this paper we propose a system (ABLE) that will act as the main platform for a number of low-cost, mature technologies that will be integrated in order to create a dynamically adaptive Daily Life Activities Management environment in order to facilitate the everyday life of senior (but not exclusively) citizens at home. While the main target group of ABLE's users is the ageing population its use can be extended to all people that are vulnerable or atypical in body, intellect or emotions and are categorized by society as disabled. The classes of assistive products that are well defined in the international standard, ISO9999 such as assistive products for personal medical treatment, personal care and protection, communication, information and reaction and for personal mobility, will be easily incorporated in our proposed platform. Furthermore, our platform could integrate and implement the above classes under several service models that will be analyzed further.

A hybrid plaque characterization method using intravascular ultrasound images.

BACKGROUND: Intravascular ultrasound (IVUS) is an invasive imaging modality that provides high resolution cross-sectional images permitting detailed evaluation of the lumen, outer vessel wall and plaque morphology and evaluation of its composition. Over the last years several methodologies have been proposed which allow automated processing of the IVUS data and reliable segmentation of the regions of interest or characterization of the type of the plaque. OBJECTIVE: In this paper we present a novel methodology for the automated identification of different plaque components in grayscale IVUS images. METHODS: The proposed method is based on a hybrid approach that incorporates both image processing techniques and classification algorithms and allows classification of the plaque into three different categories: Hard Calcified, Hard-Non Calcified and Soft plaque. Annotations by two experts on 8 IVUS examinations were used to train and test our method. RESULTS: The combination of an automatic thresholding technique and active contours coupled with a Random Forest classifier provided reliable results with an overall classification accuracy of 86.14%. CONCLUSIONS: The proposed method can accurately detect the plaque using grayscale IVUS images and can be used to assess plaque composition for both clinical and research purposes.