Diagnostic yield of implantable loop recorders: results from the hellenic registry.

OBJECTIVE: Implantable loop recorders (ILRs) are increasingly being used for long-term cardiac monitoring in different clinical settings. The aim of this study was to investigate the real-world performance of ILRs-including the time to diagnosis-in unselected patients with different ILR indications. METHODS AND RESULTS: In this multicenter, observational study, 871 patients with an indication of pre-syncope/syncope (61.9%), unexplained palpitations (10.4%), and atrial fibrillation (AF) detection with a history of cryptogenic stroke (CS) (27.7%) underwent ILR implantation. The median follow-up was 28.8 ± 12.9 months. In the presyncope/syncope group, 167 (31%) received a diagnosis established by the device. Kaplan-Meier estimates indicated that 16.9% of patients had a diagnosis at 6 months, and the proportion increased to 22.5% at 1 year. Of 91 patients with palpitations, 20 (22%) received a diagnosis based on the device. The diagnosis was established in 12.2% of patients at 6 months, and the proportion increased to 13.3% at 1 year. Among 241 patients with CS, 47 (19.5%) were diagnosed with AF. The diagnostic yield of the device was 10.4% at 6 months and 12.4% at 1 year. In all cases, oral anticoagulation was initiated. Overall, ILR diagnosis altered the therapeutic strategy in 26.1% of the presyncope/syncope group, 2.2% of the palpitations group, and 3.7% of the CS group in addition to oral anticoagulation initiation. CONCLUSION: In this real-world patient population, ILR determines diagnosis and initiates new therapeutic management for nearly one-fourth of patients. ILR implantation is valuable in the evaluation of patients with unexplained presyncope/syncope, CS, and palpitations.

ChatGPT and Patients With Heart Failure.

ChatGPT (Generative Pre-trained Transformer) is a large-scale language processing model, with possibilities for professional patient support in a patient-friendly way. The aim of the study was to examine the accuracy and reproducibility of ChatGPT in answering questions about knowledge and management of heart failure (HF). First, we recorded 47 most frequently asked questions by patients about HF. The answers of ChatGPT to these questions were independently assessed by two researchers. ChatGPT was able to render the definition of the disease in a very simple and explanatory way. It listed a number of the most important causes of HF and the most important risk factors for its occurrence. It provided correct answers about the most important diagnostic tests and why they are recommended. In addition, it answered health and dietary questions, such as the daily fluid and the alcohol intake. ChatGPT listed the most important classes of drugs in HF and their mechanism of action. It also answered with arguments to questions about patient's sex life, whether they could work, drive, or travel by plane. The performance of ChatGPT was described as very good as it was able to adequately answer all questions posed to it.

The Utility of Low-Dose-Dobutamine Stress Echocardiography in Patients with Heart Failure with Reduced Ejection Fraction: An Update.

Despite significant advancements in medical therapy, heart failure with reduced ejection fraction (HFrEF) continues to be a significant cause of death and disability. Reversible ischaemic left ventricular dysfunction due to viable myocardium is one such contributing factor. In these cases, coronary revascularization has shown promise in improving left ventricular function and prognosis. For patients with HFrEF and wide QRS, cardiac resynchronization therapy (CRT) is an effective option to address electromechanical dyssynchrony. However, approximately 30% of patients do not respond positively to CRT, highlighting the need to refine candidate selection for this treatment. In some patients with reduced HFrEF, there is a condition known as classical low-flow, low-gradient aortic stenosis (AS) that may be observed. This condition is characterized by a low transaortic flow, which leads to reductions in both the transaortic mean gradient and aortic valve area. Decision-making regarding revascularization, CRT, and pharmacological treatment play a crucial role in managing HFrEF. Cardiac imaging can be valuable in guiding decision-making processes and assessing the prognosis of patients with HFrEF. Among the imaging modalities, dobutamine stress echocardiography has come a long way in establishing itself as a feasible, safe, effective, relatively cheap non-invasive technique. The aim of this review is to explore the current literature on the utility of low-dose stress echocardiography in diagnosing and prognosticating patients with HFrEF.

Hand-Crafted and Learned Feature Aggregation for Visual Marble Tiles Screening.

An important factor in the successful marketing of natural ornamental rocks is providing sets of tiles with matching textures. The market price of the tiles is based on the aesthetics of the different quality classes and can change according to the varying needs of the market. The classification of the marble tiles is mainly performed manually by experienced workers. This can lead to misclassifications due to the subjectiveness of such a procedure, causing subsequent problems with the marketing of the product. In this paper, 24 hand-crafted texture descriptors and 20 Convolution Neural Networks were evaluated towards creating aggregated descriptors resulting from the combination of one hand-crafted and one Convolutional Neural Network at a time. A marble tile dataset designed for this study was used for the evaluation process, which was also released publicly to further enable the research for similar studies (both on texture and dolomitic ornamental marble tile analysis). This was done to automate the classification of the marble tiles. The best performing feature descriptors were aggregated together in order to achieve an objective classification. The resulting model was embodied into an automatic screening machine designed and constructed as a part of this study. The experiments showed that the aggregation of the VGG16 and SILTP provided the best results, with an AUC score of 0.9944.

Behavioral Data Analysis of Robot-Assisted Autism Spectrum Disorder (ASD) Interventions Based on Lattice Computing Techniques.

Recent years have witnessed the proliferation of social robots in various domains including special education. However, specialized tools to assess their effect on human behavior, as well as to holistically design social robot applications, are often missing. In response, this work presents novel tools for analysis of human behavior data regarding robot-assisted special education. The objectives include, first, an understanding of human behavior in response to an array of robot actions and, second, an improved intervention design based on suitable mathematical instruments. To achieve these objectives, Lattice Computing (LC) models in conjunction with machine learning techniques have been employed to construct a representation of a child's behavioral state. Using data collected during real-world robot-assisted interventions with children diagnosed with Autism Spectrum Disorder (ASD) and the aforementioned behavioral state representation, time series of behavioral states were constructed. The paper then investigates the causal relationship between specific robot actions and the observed child behavioral states in order to determine how the different interaction modalities of the social robot affected the child's behavior.

Feature Extraction for Finger-Vein-Based Identity Recognition.

This paper aims to provide a brief review of the feature extraction methods applied for finger vein recognition. The presented study is designed in a systematic way in order to bring light to the scientific interest for biometric systems based on finger vein biometric features. The analysis spans over a period of 13 years (from 2008 to 2020). The examined feature extraction algorithms are clustered into five categories and are presented in a qualitative manner by focusing mainly on the techniques applied to represent the features of the finger veins that uniquely prove a human's identity. In addition, the case of non-handcrafted features learned in a deep learning framework is also examined. The conducted literature analysis revealed the increased interest in finger vein biometric systems as well as the high diversity of different feature extraction methods proposed over the past several years. However, last year this interest shifted to the application of Convolutional Neural Networks following the general trend of applying deep learning models in a range of disciplines. Finally, yet importantly, this work highlights the limitations of the existing feature extraction methods and describes the research actions needed to face the identified challenges.

Poor stroke risk perception despite moderate public stroke awareness: insight from a cross-sectional national survey in Greece.

BACKGROUND: Although stroke is the fourth cause of death in Western societies, public stroke awareness remains suboptimal. The aim of this study was to estimate stroke risk perception and stroke awareness in Greece through a cross-sectional telephone survey. METHODS: A trained interview team conducted this cross-sectional telephone survey between February and April 2014 using an online structured questionnaire. Participants were selected using random digit dialing of landline and mobile telephone numbers with quota sampling weighted for geographical region based on the most recent General Population Census (2011). RESULTS: Between February and April 2014, 723 individuals (418 women [58%], 47.4 ± 17.8 years) agreed to respond. Among all respondents, 642 (88.8%) were able to provide at least 1 stroke risk factor; 673 respondents (93.08%) were able to provide correctly at least 1 stroke symptom or sign. When asked what would they do in case of acute onset of stroke symptoms, 497 (68.7%) responded that they would either call the ambulance or visit the closest emergency department. Only 35.3%, 18.9%, 17.2%, 20.7%, and 15.0% of respondents with atrial fibrillation, arterial hypertension, dyslipidemia, diabetes mellitus, and current smoking, respectively, considered themselves as being in high risk for stroke. CONCLUSIONS: Stroke risk perception in Greece is low despite moderate public stroke awareness.