Fanis G. Kalatzis

A new methodology for accurate 3-dimensional coronary artery reconstruction using routine intravascular ultrasound and angiographic data: implications for widespread assessment of endothelial shear stress in humans.

AIMS To develop and validate a new methodology that allows accurate 3-dimensional (3-D) coronary artery reconstruction using standard, simple angiographic and intravascular ultrasound (IVUS) data acquired during routine catheterisation enabling reliable assessment of the endothelial shear stress (ESS) distribution. METHODS AND RESULTS Twenty-two patients (22 arteries: 7 LAD; 7 LCx; 8 RCA) who underwent angiography and IVUS examination were included. The acquired data were used for 3-D reconstruction using a conventional method and a new methodology that utilised the luminal 3-D centreline to place the detected IVUS borders and anatomical landmarks to estimate their orientation. The local ESS distribution was assessed by computational fluid dynamics. In corresponding consecutive 3 mm segments, lumen, plaque and ESS measurements in the 3-D models derived by the centreline approach were highly correlated to those derived from the conventional method (r>0.98 for all). The centreline methodology had a 99.5% diagnostic accuracy for identifying segments exposed to low ESS and provided similar estimations to the conventional method for the association between the change in plaque burden and ESS (centreline method: slope= -1.65%/Pa, p=0.078; conventional method: slope= -1.64%/Pa, p=0.084; p =0.69 for difference between the two methodologies). CONCLUSIONS The centreline methodology provides geometrically correct models and permits reliable ESS computation. The ability to utilise data acquired during routine coronary angiography and IVUS examination will facilitate clinical investigation of the role of local ESS patterns in the natural history of coronary atherosclerosis.

ANGIOCARE: An automated system for fast three-dimensional coronary reconstruction by integrating angiographic and intracoronary ultrasound data

The development of an automated, user‐friendly system (ANGIOCARE), for rapid three‐dimensional (3D) coronary reconstruction, integrating angiographic and, intracoronary ultrasound (ICUS) data.

Impact of local endothelial shear stress on neointima and plaque following stent implantation in patients with ST-elevation myocardial infarction: A subgroup-analysis of the COMFORTABLE AMI-IBIS 4 trial

BACKGROUND Numerous studies have demonstrated an association between endothelial shear stress (ESS) and neointimal formation after stent implantation. However, the role of ESS on the composition of neointima and underlying plaque remains unclear. METHODS Patients recruited in the Comfortable AMI-IBIS 4 study implanted with bare metal stents (BMS) or biolimus eluting stents (BES) that had biplane coronary angiography at 13 month follow-up were included in the analysis. The intravascular ultrasound virtual-histology (IVUS-VH) and the angiographic data were used to reconstruct the luminal surface, and the stent in the stented segments. Blood flow simulation was performed in the stent surface, which was assumed to represent the luminal surface at baseline, to assess the association between ESS and neointima thickness. The predominant ESS was estimated in 3-mm segments and was correlated with the amount of neointima, neointimal tissue composition, and with the changes in the underlying plaque burden and composition. RESULTS Forty three patients (18 implanted with BMS and 25 with BES) were studied. In both stent groups negative correlations were noted between ESS and neointima thickness in BMS (P < 0.001) and BES (P = 0.002). In BMS there was a negative correlation between predominant ESS and the percentage of the neointimal necrotic core component (P = 0.015). In BES group, the limited neointima formation did not allow evaluation of the effect of ESS on its tissue characteristics. ESS did not affect vessel wall remodeling and the plaque burden and composition behind BMS (P > 0.10) and BES (P > 0.45). CONCLUSIONS ESS determines neointimal formation in both BMS and BES and affects the composition of the neointima in BMS. Conversely, ESS does not impact the plaque behind struts irrespective of stent type throughout 13 months of follow-up.

Short- and long-term implications of a bioresorbable vascular scaffold implantation on the local endothelial shear stress patterns.

The angiographic and optical coherence tomographic data acquired at baseline and at 2-year follow-up from a 59-year-old patient, who had been implanted with an Absorb bioresorbable vascular scaffold (Absorb BVS, Abbott Vascular, Santa Clara, California), were fused to reconstruct the coronary

Segmentation of microarray images using pixel classification-Comparison with clustering-based methods

OBJECTIVE DNA microarray technology yields expression profiles for thousands of genes, in a single hybridization experiment. The quantification of the expression level is performed using image analysis. In this paper we introduce a supervised method for the segmentation of microarray images using classification techniques. The method is able to characterize the pixels of the image as signal, background and artefact. METHODS AND MATERIAL The proposed method includes five steps: (a) an automated gridding method which provides a cell of the image for each spot. (b) Three multichannel vector filters are employed to preprocess the raw image. (c) Features are extracted from each pixel of the image. (d) The dimension of the feature set is reduced. (e) Support vector machines are used for the classification of pixels as signal, background, artefacts. The proposed method is evaluated using both real images from the Stanford microarray database and simulated images generated by a microarray data simulator. The signal and the background pixels, which are responsible for the quantification of the expression levels, are efficiently detected. RESULTS A quality measure (qindex) and the pixel-by-pixel accuracy are used for the evaluation of the proposed method. The obtained qindex varies from 0.742 to 0.836. The obtained accuracy for the real images is about 98%, while the accuracies for the good, normal and bad quality simulated images are 96, 93 and 71%, respectively. The proposed classification method is compared to clustering-based techniques, which have been proposed for microarray image segmentation. This comparison shows that the classification-based method reports better results, improving the performance by up to 20%. CONCLUSIONS The proposed method can be used for segmentation of microarray images with high accuracy, indicating that segmentation can be improved using classification instead of clustering. The proposed method is supervised and it can only be used when training data are available.

Spot addressing for microarray images structured in hexagonal grids

In this work, an efficient method for spot addressing in images, which are generated by the scanning of hexagonal structured microarrays, is proposed. Initially, the blocks of the image are separated using the projections of the image. Next, all the blocks of the image are processed separately for the detection of each spot. The spot addressing procedure begins with the detection of the high intensity objects, which are probably the spots of the image. Next, the Growing Concentric Hexagon algorithm, which uses the properties of the hexagonal grid, is introduced for the detection of the non-hybridized spots. Finally, the Voronoi diagram is applied to the centers of the detected spots for the gridding of the image. The method is evaluated using spots generated from the scanning of the Beadchip of Illumina, which is used for the detection of Single Nucleotide Polymorphisms in the human genome, and uses hexagonal structure for the location of the spots. For the evaluation, the detected centers for each of the spot in the image are compared to the centers of the annotation, obtaining up to 98% accuracy for the spot addressing procedure.

Developing a genomic-based point-of-care diagnostic system for rheumatoid arthritis and multiple sclerosis

In this paper the methodology of designing a genomic-based point-of-care diagnostic system composed of a microfluidic Lab-On-Chip, algorithms for microarray image information extraction and knowledge modeling of clinico-genomic patient data is presented. The data are processed by genome wide association studies for two complex diseases: rheumatoid arthritis and multiple sclerosis. Respecting current technological limitations of autonomous molecular-based Lab-On-Chip systems the approach proposed in this work aims to enhance the diagnostic accuracy of the miniaturized LOC system. By providing a decision support system based on the data mining technologies, a robust portable integrated point-of-care diagnostic assay will be implemented. Initially, the gene discovery process is described followed by the detection of the most informative SNPs associated with the diseases. The clinical data and the selected associated SNPs are modeled using data mining techniques to allow the knowledge modeling framework to provide the diagnosis for new patients performing the point-of-care examination. The microfluidic LOC device supplies the diagnostic component of the platform with a set of SNPs associated with the diseases and the ruled-based decision support system combines this genomic information with the clinical data of the patient to outcome the final diagnostic result.

A computational approach for the estimation of heart failure patients status using saliva biomarkers

The aim of this work is to present a computational approach for the estimation of the severity of heart failure (HF) in terms of New York Heart Association (NYHA) class and the characterization of the status of the HF patients, during hospitalization, as acute, progressive or stable. The proposed method employs feature selection and classification techniques. However, it is differentiated from the methods reported in the literature since it exploits information that biomarkers fetch. The method is evaluated on a dataset of 29 patients, through a 10-fold-cross-validation approach. The accuracy is 94 and 77% for the estimation of HF severity and the status of HF patients during hospitalization, respectively.

Estimation of New York Heart Association class in heart failure patients based on machine learning techniques

The aim of this work is to present an automated method for the early identification of New York Heart Association (NYHA) class change in patients with heart failure using classification techniques. The proposed method consists of three main steps: a) data processing, b) feature selection, and c) classification. The estimation of the severity of heart failure in terms of NYHA class is addressed as two, three and, for the first time, as four class classification problem. Eleven classifiers are employed and combined with resampling techniques. The proposed method is evaluated on a dataset of 378 patients, through a 10-fold-cross-validation approach. The highest detection accuracy is 97, 87 and 67% for the two, three and the four class classification problem, respectively.

Assessing pediatrics patients’ psychological states from biomedical signals in a cloud of social robots

This paper describes an on-going research aiming to design and deploy a robotic-pet based intervention integrated to the Child Life program in a paediatric hospital. The purpose is to provide in the personalized health-care network a supplement of smart company to alleviate feelings of anxiety, loneliness and stress of long-term inpatient and their bystanders. The state of the art on companion robots for health related purposes in the long run, ethical concerns in the context of paediatric care and social and technological issues are addressed. A description of the first implementation phases, findings, lessons learned and future work are discussed under a critical multidisciplinary approach confronting perspectives from social science and technology studies, engineering, psychology and nursery. The overall research questions addressed are: can a cloud of social assistive robotic-pets join the network of well-being supply in a paediatric hospital? Under which technical and social conditions this innovation could be appropriate by the organization and -more importantly- could improve the service?