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Tsipouras M.G.,Biomedical Research Institute FORTH
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference | Year: 2012

The SensorART project focus on the management of heart failure (HF) patients which are treated with implantable ventricular assist devices (VADs). This work presents the way that crisp models are transformed into fuzzy in the weaning module, which is one of the core modules of the specialist's decision support system (DSS) in SensorART. The weaning module is a DSS that supports the medical expert on the weaning and remove VAD from the patient decision. Weaning module has been developed following a "mixture of experts" philosophy, with the experts being fuzzy knowledge-based models, automatically generated from initial crisp knowledge-based set of rules and criteria for weaning. Source


Karvounis E.C.,Biomedical Research Institute FORTH
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference | Year: 2011

The scope of this paper is to present the Specialist's Decision Support System (SDSS), part of the overall Decision Support Framework that is developed under the SensorART platform. The SensorART platform focuses on the management and remote treatment of patients suffering from end-stage heart failure. The SDSS assists specialists on designing the best treatment plan for their patients before and after VAD implantation, analyzing patients' data, extracting new knowledge, and making informative decisions. It creates a hallmark in the field, supporting medical and VAD experts through the different phases of VAD therapy. Source


Athanasiou L.S.,University of Ioannina | Karvelis P.S.,University of Ioannina | Tsakanikas V.D.,Biomedical Research Institute FORTH | Naka K.K.,University of Ioannina | And 3 more authors.
IEEE Transactions on Information Technology in Biomedicine | Year: 2012

Intravascular ultrasound (IVUS) virtual histology (VH-IVUS) is a new technique, which provides automated plaque characterization in IVUS frames, using the ultrasound backscattered RF-signals. However, its computation can only be performed once per cardiac cycle (ECG-gated technique), which significantly decreases the number of characterized IVUS frames. Also atherosclerotic plaques in images that have been acquired by machines, which are not equipped with the VH software, cannot be characterized. To address these limitations, we have developed a plaque characterization technique that can be applied in grayscale IVUS images. Our semiautomated method is based on a three-step approach. In the first step, the plaque area region of interest (ROI) is detected semiautomatically. In the second step, a set of features is extracted for each pixel of the ROI and in the third step, a random forest classifier is used to classify these pixels into four classes: dense calcium, necrotic core, fibrotic tissue, and fibro-fatty tissue. In order to train and validate our method, we used 300 IVUS frames acquired from virtual histology examinations from ten patients. The overall accuracy of the proposed method was 85.65 suggesting that our approach is reliable and may be further investigated in the clinical and research arena. © 2012 IEEE. Source


Karvounis E.C.,Biomedical Research Institute FORTH
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference | Year: 2012

In this work, the weaning module of the SensorART specialist decision support system (SDSS) is presented. SensorART focuses on the treatment of patients suffering from end-stage heart failure (HF). The use of a ventricular assist device (VAD) is the main treatment for HF patients. However in certain cases, myocardial function recovers and VADs can be explanted after the patient is weaned. In that framework an efficient module is developed responsible for the selection of the most suitable candidates for VAD weaning. In this study we describe all technical specifications concerning its two main sub-modules of the weaning module, of the Clinical Knowledge Editor and the Knowledge Execution Engine. Source


Athanasiou L.S.,University of Ioannina | Exarchos T.P.,Biomedical Research Institute FORTH | Naka K.K.,University of Ioannina | Michalis L.K.,University of Ioannina | And 2 more authors.
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS | Year: 2011

Optical Coherence Tomography (OCT) is a fiber optic imaging modality which produces high resolution tomographic images of the coronary lumen and outer vessel wall. While OCT images present morphological information in highly resolved detail, the characterization of the various plaque components relies on trained readers. The aim of this study is to extract a set of features in grayscale OCT images and to use them in order to classify the atherosclerotic plaque. Intensity and texture based features we used in order to classify the plaque in four plaque types: Calcium (C), Lipid Pool (LP), Fibrous Tissue (FT) and Mixed Plaque (MP). 50 OCT annotated images from 3 patients were used to train and test the proposed plaque characterization method. Using a Random Forests classifier overall classification accuracy 80.41% is reported. © 2011 IEEE. Source

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