CARDIOCARE: An integrated platform for the management of elderly multimorbid patients with breast cancer therapy induced cardiac toxicity.

Breast cancer (BC) remains the most diagnosed cancer in women, accounting for 12% of new annual cancer cases in Europe and worldwide. Advances in surgery, radiotherapy and systemic treatment have resulted in improved clinical outcomes and increased survival rates in recent years. However, BC therapy-related cardiotoxicity, may severely impact short- and long-term quality of life and survival. This study presents the CARDIOCARE platform and its main components, which by integrating patient-specific data from different categories, data from patient-oriented eHealth applications and wearable devices, and by employing advanced data mining and machine learning approaches, provides the healthcare professionals with a valuable tool for effectively managing BC patients and preventing or alleviating treatment induced cardiotoxicity.Clinical Relevance- Through the adoption of CARDIOCARE platform healthcare professionals are able to stratify patients for their risk for cardiotoxicity and timely apply adequate interventions to prevent its onset.

BEM evaluation of surface octahedral strains and internal strain gradients in 3D-printed scaffolds used for bone tissue regeneration.

Most of the mechnoregulatory computational models appearing so far in tissue engineering for bone healing predictions, utilize as regulators for cell differentiation mainly the octahedral volume strains and the interstitial fluid velocity calculated at any point of the fractured bone area and controlled by empirical constants concerning these two parameters. Other stimuli like the electrical and chemical signaling of bone constituents are covered by those two regulatory fields. It is apparent that the application of the same mechnoregulatory computational models for bone healing predictions in scaffold-aided regeneration is questionable since the material of a scaffold disturbs the signaling pathways developed in the environment of bone fracture. Thus, the goal of the present work is to evaluate numerically two fields developed in the body of two different compressed scaffolds, which seem to be proper for facilitating cell sensing and improving cell viability and cell seeding efficiency. These two fields concern the surface octahedral strains that the cells attached to the scaffold can experience and the internal strain gradients that create electrical pathways due to flexoelectric phenomenon. Both fields are evaluated with the aid of the Boundary Element Method (BEM), which is ideal for evaluating with high accuracy surface strains and stresses as well as strain gradients appearing throughout the analyzed elastic domain.

On-Body Sensor Position Identification with a Simple, Robust and Accurate Method, Validated in Patients with Parkinson's Disease.

The aim of this work is to implement and validate an automated method for the localization of body-worn inertial sensors. Often, body-sensor networks with inertial measurement units (IMU) used in rehabilitation and ambient monitoring of patients with movement disorders, require specific markings or labels for the correct body placement. This introduces a burden, which, especially for ambient monitoring, could lead to errors or reduced adherence. We propose a method to automatically identify sensors attached on a predefined set of body placements, namely, wrists, shanks and torso. The method was used in a multi-site clinical trial with Parkinson's disease patients and in 45 sessions it identified sensor placement on torso, wrists and shanks with 100% accuracy, discriminated between left and right shank with 100% accuracy and between left and right wrist with 98% accuracy. This is remarkable, considering the presence of parkinsonian motor symptoms causing abnormal movement patterns, such as dyskinesia.Clinical Relevance- This method can facilitate home monitoring of patients with movement disorders.

Histological validation of the automated caries detection system (ACDS) in classifying occlusal caries with the ICDAS II system in vitro.

AIM: To compare the diagnostic performance of the automated caries detection system (ACDS) for the detection and diagnosis of occlusal caries with the histological appearance of the lesions. METHODS: Eighteen posterior permanent teeth were used, out of which 40 sections were made and 53 areas were evaluated. Teeth with hypoplastic and/or hypomineralised areas or sealants on the occlusal surfaces were excluded from the study. The teeth that were used for this study were a subgroup of the teeth used in the study that introduced ACDS system. This subgroup consisted of teeth having in their occlusal surfaces early carious lesions classified as international caries detection and scoring system (ICDAS) 0, 1, 2 and 3 after clinical examination by the examiners. Histological preparations were classified by experienced examiners based on the Ekstrand, Ricketts and Kidd (ERK) system and for the respective occlusal surfaces by the ACDS system based on ICDAS II system. There were two threshold limits considered as carious in either system ICDAS ≥ 2 or ≥ 3 and ERK index ≥ 2 or ≥ 3 and all possible combinations were analysed. Statistical methods of weighted version of kappa coefficient, Kendall's tau-b correlation coefficient and p-values using the Fisher's exact method were used at the confidence level of 0.05. RESULTS: Intra-examiner kappa coefficient agreement was 0.87 and 0.89 while the inter-examiner for the two trials were 0.87 and 0.92. The ICDAS3-ERK3 combination between the ACDS and histological sections presented the best agreement with kappa coefficient 0.76, agreement 92.5%, sensitivity 100% and specificity 91.1%. ICDAS3-ERK3 combination between the optical examination of the examiners compared to the histological preparations showed kappa coefficient 0.87, agreement 96.2%, sensitivity 100%, Specificity 95.6%. CONCLUSION: The evidence supports the view that ACDS classification of occlusal surfaces based on the ICDAS system are comparable with classification to that of an examiner and with the histology of the lesion. The use of ACDS has the distinct advantage though of removing the subjectivity of the examiner since it performs the classification without any intervention by him.

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.

Middle and inner ear modelling: from microCT images to 3D reconstruction and coupling of models.

We present finite element (FE) modeling approaches of ear mechanics including 3-dimensional (3D) reconstruction of the human middle and inner ear. Specifically, we demonstrate a semi-automatic methodology for the 3D reconstruction of the inner ear structures, a FE harmonic response model of the middle ear to predict the stapes footplate frequency response, a 2D FE slice model of the cochlea for the coupled response at the micromechanical level for either acoustic or electrical excitation and a coupled FE middle ear model with a simplified cochlea box model to simulate the basilar membrane velocity in response to acoustic excitation. The proposed methodologies are validated against experimental and literature data and the results are in good agreement.

Nonparametric network design and analysis of disease genes in oral cancer progression.

Biological networks in living organisms can be seen as the ultimate means of understanding the underlying mechanisms in complex diseases, such as oral cancer. During the last decade, many algorithms based on high-throughput genomic data have been developed to unravel the complexity of gene network construction and their progression in time. However, the small size of samples compared to the number of observed genes makes the inference of the network structure quite challenging. In this study, we propose a framework for constructing and analyzing gene networks from sparse experimental temporal data and investigate its potential in oral cancer. We use two network models based on partial correlations and kernel density estimation, in order to capture the genetic interactions. Using this network construction framework on real clinical data of the tissue and blood at different time stages, we identified common disease-related structures that may decipher the association between disease state and biological processes in oral cancer. Our study emphasizes an altered MET (hepatocyte growth factor receptor) network during oral cancer progression. In addition, we demonstrate that the functional changes of gene interactions during oral cancer progression might be particularly useful for patient categorization at the time of diagnosis and/or at follow-up periods.

A decision support system for the treatment of patients with ventricular assist device support.

BACKGROUND: Heart failure (HF) is affecting millions of people every year and it is characterized by impaired ventricular performance, exercise intolerance and shortened life expectancy. Despite significant advancements in drug therapy, mortality of the disease remains excessively high, as heart transplant remains the gold standard treatment for end-stage HF when no contraindications subsist. Traditionally, implanted Ventricular Assist Devices (VADs) have been employed in order to provide circulatory support to patients who cannot survive the waiting time to transplantation, reducing the workload imposed on the heart. In many cases that process could recover its contractility performance. OBJECTIVES: The SensorART platform focuses on the management and remote treatment of patients suffering from HF. It provides an interoperable, extendable and VAD-independent solution, which incorporates various hardware and software components in a holistic approach, in order to improve the quality of the patients' treatment and the workflow of the specialists. This paper focuses on the description and analysis of Specialist's Decision Support System (SDSS), an innovative component of the SensorART platform. METHODS: The SDSS is a Web-based tool that assists specialists on designing the therapy plan for their patients before and after VAD implantation, analyzing patients' data, extracting new knowledge, and making informative decisions. RESULTS: SDSS offers support to medical and VAD experts through the different phases of VAD therapy, incorporating several tools covering all related fields; Statistics, Association Rules, Monitoring, Treatment, Weaning, Speed and Suction Detection. CONCLUSIONS: SDSS and its modules have been tested in a number of patients and the results are encouraging.

Identification of altered MET network in oral cancer progression based on nonparametric network design.

Oral cancer is characterized by multiple genetic events such as alterations of a number of oncogenes and tumour suppressor genes. The aim of this study is to identify genes and their functional interactions that may play a crucial role on a specific disease-state, especially during oral cancer progression. We examine gene interaction networks on blood genomic data, obtained from twenty three oral cancer patients at four different time stages. We generate the gene-gene networks from sparse experimental temporal data using two methods, Partial Correlations and Kernel Density Estimation, in order to capture genetic interactions. The network study reveals an altered MET (hepatocyte growth factor receptor) network during oral cancer progression, which is further analyzed in relation to other studies.

ART-ML: a new markup language for modelling and representation of biological processes in cardiovascular diseases.

BACKGROUND: With an ever increasing number of biological models available on the internet, a standardized modelling framework is required to allow information to be accessed and visualized. OBJECTIVE: In this paper we propose a novel Extensible Markup Language (XML) based format called ART-ML that aims at supporting the interoperability and the reuse of models of geometry, blood flow, plaque progression and stent modelling, exported by any cardiovascular disease modelling software. ART-ML has been developed and tested using ARTool. ARTool is a platform for the automatic processing of various image modalities of coronary and carotid arteries. METHODS: The images and their content are fused to develop morphological models of the arteries in 3D representations. All the above described procedures integrate disparate data formats, protocols and tools. ART-ML proposes a representation way, expanding ARTool, for interpretability of the individual resources, creating a standard unified model for the description of data and, consequently, a format for their exchange and representation that is machine independent. More specifically, ARTool platform incorporates efficient algorithms which are able to perform blood flow simulations and atherosclerotic plaque evolution modelling. Integration of data layers between different modules within ARTool are based upon the interchange of information included in the ART-ML model repository. ART-ML provides a markup representation that enables the representation and management of embedded models within the cardiovascular disease modelling platform, the storage and interchange of well-defined information. RESULTS: The corresponding ART-ML model incorporates all relevant information regarding geometry, blood flow, plaque progression and stent modelling procedures. All created models are stored in a model repository database which is accessible to the research community using efficient web interfaces, enabling the interoperability of any cardiovascular disease modelling software models. CONCLUSIONS: ART-ML can be used as a reference ML model in multiscale simulations of plaque formation and progression, incorporating all scales of the biological processes.

Multivariate prediction of subcutaneous glucose concentration in type 1 diabetes patients based on support vector regression.

Data-driven techniques have recently drawn significant interest in the predictive modeling of subcutaneous (s.c.) glucose concentration in type 1 diabetes. In this study, the s.c. glucose prediction is treated as a multivariate regression problem, which is addressed using support vector regression (SVR). The proposed method is based on variables concerning: (i) the s.c. glucose profile, (ii) the plasma insulin concentration, (iii) the appearance of meal-derived glucose in the systemic circulation, and (iv) the energy expenditure during physical activities. Six cases corresponding to different combinations of the aforementioned variables are used to investigate the influence of the input on the daily glucose prediction. The proposed method is evaluated using a dataset of 27 patients in free-living conditions. 10-fold cross validation is applied to each dataset individually to both optimize and test the SVR model. In the case where all the input variables are considered, the average prediction errors are 5.21, 6.03, 7.14 and 7.62 mg/dl for 15, 30, 60 and 120 min prediction horizons, respectively. The results clearly indicate that the availability of multivariable data and their effective combination can significantly increase the accuracy of both short-term and long-term predictions.

3D reconstruction of coronary arteries using frequency domain optical coherence tomography images and biplane angiography.

The aim of this study is to describe a new method for three-dimensional (3D) reconstruction of coronary arteries using Frequency Domain Optical Coherence Tomography (FD-OCT) images. The rationale is to fuse the information about the curvature of the artery, derived from biplane angiographies, with the information regarding the lumen wall, which is produced from the FD-OCT examination. The method is based on a three step approach. In the first step the lumen borders in FD-OCT images are detected. In the second step a 3D curve is produced using the center line of the vessel from the two biplane projections. Finally in the third step the detected lumen borders are placed perpendicularly onto the path based on the centroid of each lumen border. The result is a 3D reconstructed artery produced by all the lumen borders of the FD-OCT pullback representing the 3D arterial geometry of the vessel.

Patient specific multiscale modelling for plaque formation and progression.

We present a three-dimensional model of plaque formation and progression that was tested in a set of patients who underwent coronary Computed Tomography angiography (CTA) for anginal symptoms. The 3D blood flow is described by the Navier-Stokes equations, together with the continuity equation. Mass transfer within the blood lumen and through the arterial wall is coupled with the blood flow and is modeled by a convection-diffusion equation. The Low Density Lipoprotein (LDL) transports in lumen of the vessel and through the vessel tissue (which has a mass consumption term) are coupled by Kedem-Katchalsky equations. The inflammatory process is modeled using three additional reaction-diffusion partial differential equations. A full three-dimensional model was created. Furthermore, features potentially affecting plaque growth, such as patient risk score, circulating biomarkers, localization and composition of the initial plaque, and coronary vasodilating capability were also investigated. The proof of concept of the model effectiveness was assessed 6 months after the baseline evaluation.

Multiscale-patient-specific artery and atherogenesis models.

In this work, we present a platform for the development of multiscale patient-specific artery and atherogenesis models. The platform, called ARTool, integrates technologies of 3-D image reconstruction from various image modalities, blood flow and biological models of mass transfer, plaque characterization, and plaque growth. Patient images are acquired for the development of the 3-D model of the patient specific arteries. Then, blood flow is modeled within the arterial models for the calculation of the wall shear stress distribution (WSS). WSS is combined with other patient-specific parameters for the development of the plaque progression models. Real-time simulation can be performed for same cases in grid environment. The platform is evaluated using both animal and human data.

Atherosclerotic plaque characterization in Optical Coherence Tomography images.

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.

ART-ML - a novel XML format for the biological procedures modeling and the representation of blood flow simulation.

The paper proposes a novel Extensible Markup Language (XML) based format called ART-ML that aims at supporting the interoperability and the reuse of models of blood flow, mass transport and plaque formation, exported by ARTool. ARTool is a platform for the automatic processing of various image modalities of coronary and carotid arteries. The images and their content are fused to develop morphological models of the arteries in easy to handle 3D representations. The platform incorporates efficient algorithms which are able to perform blood flow simulation. In addition atherosclerotic plaque development is estimated taking into account morphological, flow and genetic factors. ART-ML provides a XML format that enables the representation and management of embedded models within the ARTool platform and the storage and interchange of well-defined information. This approach influences in the model creation, model exchange, model reuse and result evaluation.

A non-invasive methodology for fetal monitoring during pregnancy.

OBJECTIVES: This paper describes a methodology for the monitoring of the fetal cardiac health status during pregnancy, through the effective and non-invasive monitoring of the abdominal ECG signals (abdECG) of the mother. METHODS: For this purpose, a three-stage methodology has been developed. In the first stage, the fetal heart rate (fHR) is extracted from the abdECG signals, using nonlinear analysis. Also, the eliminated ECG (eECG) is calculated, which is the abdECG after the maternal QRSs elimination. In the second stage, a blind source separation technique is applied to the eECG signals and the fetal ECG (fECG) is obtained. Finally, monitoring of the fetus is implemented using features extracted from the fHR and fECG, such as the T/QRS ratio and the characterization of the fetal ST waveforms. RESULTS: The methodology is evaluated using a dataset of simulated multichannel abdECG signals: 94.79% accuracy for fHR extraction, 92.49% accuracy in T/QRS ratio calculation and 79.87% in ST waveform classification. CONCLUSIONS: The novel non-invasive proposed methodology is advantageous since it offers automated identification of fHR and fECG and automated ST waveform analysis, exhibiting a high diagnostic accuracy.

Enhancement of the classification of multichannel chromosome images using support vector machines.

Color chromosome classification (karyotyping) allows simultaneous analysis of numerical and structural chromosome abnormalities. The success of the technique largely depends on the accuracy of pixel classification. In this paper we present a method for multichannel chromosome image classification based on support vector machines. First, the image is segmented using a multichannel watershed segmentation method. Classification of the pixels of the segmented regions using support vector machines is then employed. The method has been tested on images from normal cells, showing the improvement in classification accuracy by 10.16% when compared to a Bayesian classifier. The increased classification improves the reliability of the M-FISH imaging technique in identifying subtle and cryptic chromosomal abnormalities for cancer diagnosis and genetic disorders research.

Improvement of microcalcification cluster detection in mammography utilizing image enhancement techniques.

In this work, the effect of an image enhancement processing stage and the parameter tuning of a computer-aided detection (CAD) system for the detection of microcalcifications in mammograms is assessed. Five (5) image enhancement algorithms were tested introducing the contrast-limited adaptive histogram equalization (CLAHE), the local range modification (LRM) and the redundant discrete wavelet (RDW) linear stretching and shrinkage algorithms. CAD tuning optimization was targeted to the percentage of the most contrasted pixels and the size of the minimum detectable object which could satisfactorily represent a microcalcification. The highest performance in two mammographic datasets, were achieved for LRM (A(Z)=0.932) and the wavelet-based linear stretching (A(Z)=0.926) methodology.