Modular domain-to-domain translation network

Domain-to-domain translation methods map images from a source domain to corresponding images from a target domain. The two domains contain images from the same classes, but these images look different. Recent approaches use generative adversarial networks in various configurations and architectures to perform the translation. By using GANs, they inevitably inherit their problems like training instability and mode collapse. We propose a novel approach to the problem that does not use a GAN. Instead, it relies on an hierarchical architecture that encapsulates information of the target domain by using individually trained networks. This hierarchical architecture is then trained as one unified deep network. Using this approach, we show that images from the original domain are translated to the target domain both for the case when there is a one-to-one correspondence between the images of the two domains and for the case that such correspondence information is absent. We visualize and evaluate the translation from one information domain to the other and discuss the proposed model's relation to the conditional generative adversarial networks. We further argue that deep learning can benefit from the proposed hierarchical architecture

Measuring the impact of blockchain on healthcare applications

Blockchain is a technology with potential for making ground breaking steps in addressing social, economic and healthcare challenges. The global information technology scene is being overcrowded with blockchain applications with special focus on the vast healthcare market [12]. The value of information related to healthcare creates a clear path for applying blockchain as a solution for some of the challenges in the healthcare sector, in particular with the goal of creating a fair and transparent way for sharing information and patient data. It is however a fact that while blockchain technology introduces additional complexity to the implementation healthcare software, the benefit the technology actually brings still remains unclear and difficult to evaluate. This vision paper demonstrates our research focus on providing a body of knowledge and tools to help evaluate this impact of blockchain on eHealth applications. In particular, we identify that such a research effort has to explicitly consider cost of addressing challenges inherent to the eHealth domain like integration of disparate software systems (hospitals, research institutions, government agencies, health insurance and pharmaceutical companies, etc.), the potential introduction of cryptocurrencies in healthcare systems, degree of patient service improvement, transparency and compliance to laws and regulations, and others. The more traditional influencing factors, like cost of development and running, licenses for using third-party software services, and the ones inherent to blockchain like cost of computation and energy will also have to be taken into consideration in the metrics model.</p

Filter-based approach for ornamentation detection and recognition in singing folk music

This is a Conference paper presented by the authors at the CAiP 2015; 16th International Conference on Computer Analysis of Images and Patterns, held in Malta from the 2 to 4 September, 2015.Ornamentations in music play a significant role for the emotion which a performer or a composer aims to create. The automated identification of ornamentations enhances the understanding of music, which can be used as a feature for tasks such as performer identification or mood classification. Existing methods rely on a pre-processing step that performs note segmentation. We propose an alternative method by adapting the existing two-dimensional COSFIRE filter approach to onedimension (1D) for the automatic identification of ornamentations in monophonic folk songs. We construct a set of 1D COSFIRE filters that are selective for the 12 notes of the Western music theory. The response of a 1D COSFIRE filter is computed as the geometric mean of the differences between the fundamental frequency values in a local neighbourhood and the preferred values at the corresponding positions. We apply the proposed 1D COSFIRE filters to the pitch tracks of a song at every position along the entire signal, which in turn give response values in the range [0,1]. The 1D COSFIRE filters that we propose are effective to recognize meaningful musical information which can be transformed into symbolic representations and used for further analysis. We demonstrate the effectiveness of the proposed methodology in a new data set that we introduce, which comprises five monophonic Cypriot folk tunes consisting of 428 ornamentations. The proposed method is effective for the detection and recognition of ornamentations in singing folk music.This research was funded from the Republic of Cyprus through the Cyprus research promotion foundation and also supported by the University of Cyprus by the research grant ANΘPΩΠIΣTIKEΣ / ANΘPΩ / 0311(BE) / 19.peer-reviewe

Intelligent Noninvasive Diagnosis of Aneuploidy:Raw Values and Highly Imbalanced Dataset

The objective of this paper is to introduce a noninvasive diagnosis procedure for aneuploidy and to minimize the social and financial cost of prenatal diagnosis tests that are performed for fetal aneuploidies in an early stage of pregnancy. We propose a method by using artificial neural networks trained with data from singleton pregnancy cases, while undergoing first trimester screening. Three different datasets' with a total of 122 362 euploid and 967 aneuploid cases were used in this study. The data for each case contained markers collected from the mother and the fetus. This study, unlike previous studies published by the authors for a similar problem differs in three basic principles: 1) the training of the artificial neural networks is done by using the markers' values in their raw form (unprocessed), 2) a balanced training dataset is created and used by selecting only a representative number of euploids for the training phase, and 3) emphasis is given to the financials and suggest hierarchy and necessity of the available tests. The proposed artificial neural networks models were optimized in the sense of reaching a minimum false positive rate and at the same time securing a 100% detection rate for Trisomy 21. These systems correctly identify other aneuploidies (Trisomies 13&18, Turner, and Triploid syndromes) at a detection rate greater than 80%. In conclusion, we demonstrate that artificial neural network systems can contribute in providing noninvasive, effective early screening for fetal aneuploidies with results that compare favorably to other existing methods

Proposal for an eHealth Based Ecosystem Serving National Healthcare

The European Union (EU)'s keen concern about citizens' health and well-being advancement has been expressed at all levels. It has been understood that at present, these can only be achieved through coordinated actions at the individual member states' level based on EU directives, as well as through promoting and funding R&D and expanding the use of eHealth technologies. Despite the diversities and particularities among member states, common values such as universal access to good quality healthcare, equity, and solidarity have been widely accepted across EU. That demanded the adoption of policies and follow directives, which streamlined actions to bridge healthcare gaps, and facilitate cross-border healthcare. This paper articulates a framework for deriving a national healthcare system, based on interoperable Electronic Health Record (EHR) with safeguarding healthcare quality, enabling quadruple helix (Public, Academia, Industry, NGOs) driven R&D and guided by a patient-centered approach. A methodology to develop an integrated EHR at National level is proposed as a prerequisite for eHealth and put into perspective. Recommendations are given for the steps needed, from the managerial, legal, technical, and financial concerns in developing an open access, patient-centered national healthcare system based on the context and constraints of a country. The example of a small country to apply the proposed methodology is demonstrated. Stakeholders, including citizens, healthcare professionals, academia, and the industry are mobilized, enabled, and incentivized for implementing the methodology. Experiences are aspired to be offered as lessons learned for other countries to adapt on their environment

Risk factors analysis concerning infections in general surgery

Σκοπός:Oι λοιμώξεις που ακολουθούν τις χειρουργικές επεμβάσεις αποτελούν σημαντική πηγή νοσηρότητας και θνητότητας στους ασθενείς. Με σειρά συχνότητας καταγράφονται η λοίμωξη του χειρουργικού πεδίου, η πνευμονία, η βακτηριαιμία που σχετίζεται με τον φλεβικό καθετήρα και οι ουρολοιμώξεις. Σε δύο τμήματα γενικής χειρουργικής τριτοβάθμιου νοσοκομείου διεξήχθη μελέτη ασθενών-μαρτύρων προκειμένου να εκτμηθούν οι παράγοντες κινδύνου για τις μετεγχειρητικές λοιμώξεις.Υλικά-Μέθοδοι: Ως παράγοντες κινδύνου καθορίσθηκαν: το γένος, η ηλικία, η συννοσηρότητα (σακχαρώδης διαβήτης, ηπατική ανεπάρκεια, καρδιακή ανεπάρκεια, αναπνευστική ανεπάρκεια, νόσος κολλαγόνου, νεοπλασία), η χρήση κορτικοστεροειδών, η χρήση αντινεοπλασματικών, η παχυσαρκία (>30 kg/m2), η υποθρεψία, ο χρόνος της επέμβασης (επείγουσα ή προγραμματισμένη), η ταξινόμηση της επέμβασης (καθαρή, καθαρή-μολυσμένη, μολυσμένη, ρυπαρή), η διάρκεια της επέμβασης, η φυσική κατάσταση του ασθενούς όπως καθορίζεται από το ASA score, το είδος της αναισθησίας (γενική, ραχιαία, επισκληρίδιος), η χρήση καπνού ή/και αλκοόλ.Αποτελέσματα:Η λοίμωξη του χειρουργικού πεδίου ήταν η συχνότερη μετεγχειρητική λοίμωξη στη μελέτη. Χρησιμοποιώντας μοντέλο μονοπαραγοντικής λογιστικής παλινδρόμησης οι ακόλουθοι παράγοντες βρέθηκαν στατιστικά σημαντικοί για την πρόκληση λοίμωξης (p3, και το άρρεν φύλο.Συμπεράσματα: Η λοίμωξη του χειρουργικού πεδίου είναι η συχνότερη μετεγχειρητική λοίμωξη. Παράγοντες κινδύνου στατιστικά σημαντικοί για λοίμωξη είναι ο σακχαρώδης διαβήτης, ο χρόνος της επέμβασης, ASA score >3, και το άρρεν φύλο.Background: Postoperative infectious complications are important source of morbidity and mortality in surgical patients. Surgical Site Infection (SSI), is the most common followed by pneumonia, Central Venous Catheter (CVC) bloodstream infection and Urinary Tract Infection (UTI).Methods: A case-control study was conducted in two general surgery departments trying to assess the risk factors for postsurgical infections. Gender, age, co-morbidities (diabetes mellitus, liver failure, heart failure, respiratory failure, connective tissue disease, neoplasia), use of corticosteroids, use of chemotherapeutic agents, obesity (>30 kg/m2), malnutrition, time of operation (elective or scheduled), wound classification (clean, clean-contaminated, contaminated, dirty), duration of surgical procedure, ASA score, type of anesthesia (general, epidural, spinal), smoke abuse and alcohol abuse were defined as risk factors.Results: SSI was the most common postsurgical infection in our study. The univariable logistic regression model revealed the following significant predictors (p3 and male sex, retained statistical significance (p3 and emergency procedure, are considered statistically significant

First Trimester Noninvasive Prenatal Diagnosis:A Computational Intelligence Approach

The objective of this study is to examine the potential value of using machine learning techniques such as artificial neural network (ANN) schemes for the noninvasive estimation, at 11-13 weeks of gestation, the risk for euploidy, trisomy 21 (T21), and other chromosomal aneuploidies (O.C.A.), from suitable sonographic, biochemical markers, and other relevant data. A database(1) consisted of 51,208 singleton pregnancy cases, while undergoing first trimester screening for aneuploidies has been used for the building, training, and verification of the proposed method. From all the data collected for each case from the mother and the fetus, the following 9 are considered by the collaborating obstetricians as the most relevant to the problem in question: maternal age, previous pregnancy with T21, fetal crown-rump length, serum free beta-hCG in multiples of the median (MoM), pregnancy-associated plasma protein-A in MoM, nuchal translucency thickness, nasal bone, tricuspid flow, and ductus venosus flow. The dataset was randomly divided into a training set that was used to guide the development of various ANN schemes, support vector machines, and k-nearest neighbor models. An evaluation set used to determine the performance of the developed systems. The evaluation set, totally unknown to the proposed system, contained 16,898 cases of euploidy fetuses, 129 cases of T21, and 76 cases of O.C.A. The best results were obtained by the ANN system, which identified correctly all T21 cases, i.e., 0% false negative rate (FNR) and 96.1% of euploidies, i.e., 3.9% false positive rate (FPR), meaning that no child would have been born with T21 if only that 3.9% of all pregnancies had been sent for invasive testing. The aim of this work is to produce a practical tool for the obstetrician which will ideally provide 0% FNR and to recommend the minimum possible number of cases for further testing such as invasive. In conclusion, it was demonstrated that ANN schemes can provide an effective early screening for fetal aneuploidies at a low FPR with results that compare favorably to those of existing systems

Perspective Chapter: Moderate Aortic Stenosis and Heart Failure With Reduced Ejection Fraction; Early Replacement or Conservative Treatment?

Aortic stenosis (AS) is the most common valve lesion among the continuously aging population with serious effect on the left ventricular ejection fraction (LVEF). If left untreated, it is associated with serious complications such as heart failure (HF), pulmonary hypertension, thromboembolic events, and even sudden death. Early diagnosis and treatment is of outmost importance to avoid the above complications but also to maintain the patient’s normal heart function. Echocardiography is the key examination that assesses the severity of the stenosis, valve calcification, left ventricular (LV) function, and wall thickness. Also new imaging methods such as cardiac computed tomography (CT) and cardiac magnetic resonance imaging (MRI) help in assessing the severity of aortic valve stenosis when echocardiography has limitations. Based on the categorization of the severity of the stenosis, its treatment is determined. Although things are clear in cases of asymptomatic disease and severe stenosis, this is not the case in moderate disease. Experts and clinical trials do not define clearly which cases can be treated conservatively and which need surgical or transcatheter intervention. The purpose of this article is to gather all the latest data on the treatment of moderate aortic stenosis, especially in patients with heart failure and low ejection fraction

Revascularization approaches in patients with radiation-induced carotid stenosis: an updated systematic review and meta-analysis

Background: Ionizing radiation remains a well-known risk factor of carotid artery stenosis. The survival rates of head and neck cancer patients undergoing radiotherapy have risen owing to medical advancements in the field. As a consequence, the incidence of carotid artery stenosis in these high-risk patients has increased.Aims: In this study we sought to compare the outcomes of carotid endarterectomy (CEA) vs carotid artery stenting (CAS) for radiation-induced carotid artery stenosis.Methods: This study was performed according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines. Eligible studies were identified through a comprehensive search of PubMed, Scopus and Cochrane Central until July 2020. A random-effects model meta-analysis was conducted, and odds ratios (ORs) were calculated. The I-square statistic was used to assess for heterogeneity.Results: Seven studies and 201 patients were included. Periprocedural stroke, myocardial infarction (MI), and death rates were similar between the two revascularization approaches. However, the risk for cranial nerve (CN) injury was higher in the CEA group (OR, 7.40; 95% CI, 1.58–34.59; I2 = 0%). Analysis revealed no significant difference in terms of long-term mortality (OR, 0.41; 95% CI, 0.14–1.16; I2 = 0%) and restenosis rates (OR, 0.69; 95% CI, 0.29–1.66; I2 = 0%) between CEA and CAS after a mean follow-up of 40.5 months.Conclusions: CAS and CEA appear to have a similar safety and efficacy profile in patients with radiation-induced carotid artery stenosis. Patients treated with CEA have a higher risk for periprocedural CN injuries. Future prospective studies are warranted to validate these results

Α Generic Tool for Building Fuzzy Cognitive Map Systems

Abstract. Α generic system for simulating complex dynamical systems along the paradigm of fuzzy cognitive maps (FCM) has been created and tested. The proposed system enables a user to design appropriate FCM structures, by specifying the desired concepts and the various parameters such as sensitivities, as well as a variety of shaping functions. The user is able to see the results, change the parameters, modify the functions, and rerun the system using an alteration of the final results and make new conclusions. The system is introduced and demonstrated using a simple real case. The results of a usability test of the system suggest that the system is capable of simulating complicated FCM structures in an effective manner, helping the user to reduce the degree of risks during decision making