Achieving Widespread Impact: CTA's impact pathways based on 23 evaluation reports

This report presents an analysis of the impacts of CTA’s programmes and activities by 75 impact narratives. It looks into the types of impacts and beneficiaries reached, and the division between short- and long-term impacts. The report also includes a chapter which provides an interpretation of causal and chronological impact and another in which lessons learned are discussed. The 23 evaluation and impact studies, from which the 75 narratives were extracted, were produced in the period 2013–2015 and cover a portion of CTA’s activities from 2003–2014

Noninvasive Estimation of Epicardial Dominant High-Frequency Regions During Atrial Fibrillation

[EN] Introduction Ablation of high dominant frequency (DF) sources in patients with atrial fibrillation (AF) is an effective treatment option for paroxysmal AF. The aim of this study was to evaluate the accuracy of noninvasive estimation of DF and electrical patterns determination by solving the inverse problem of the electrocardiography. Methods Four representative AF patients with left-to-right and right-to-left atrial DF patterns were included in the study. For each patient, intracardiac electrograms from both atria were recorded simultaneously together with 67-lead body surface recordings. In addition to clinical recordings, realistic mathematical models of atria and torso anatomy with different DF patterns of AF were used. For both mathematical models and clinical recordings, inverse-computed electrograms were compared to intracardiac electrograms in terms of voltage, phase, and frequency spectrum relative errors. Results Comparison between intracardiac and inverse computed electrograms for AF patients showed 8.8 ± 4.4% errors for DF, 32 ± 4% for voltage, and 65 ± 4% for phase determination. These results were corroborated by mathematical simulations showing that the inverse problem solution was able to reconstruct the frequency spectrum and the DF maps with relative errors of 5.5 ± 4.1%, whereas the reconstruction of the electrograms or the instantaneous phase presented larger relative errors (i.e., 38 ± 15% and 48 ± 14 % respectively, P < 0.01). Conclusions Noninvasive reconstruction of atrial frequency maps can be achieved by solving the inverse problem of electrocardiography with a higher accuracy than temporal distribution patterns.Pedrón-Torrecilla, J.; Rodrigo Bort, M.; M. Climent, A.; Liberos, A.; Pérez-David E; Bermejo, J.; Arenal, A.... (2016). Noninvasive Estimation of Epicardial Dominant High-Frequency Regions During Atrial Fibrillation. Journal of Cardiovascular Electrophysiology. 27(4):435-442. doi:https://doi.org/10.1111/jce.12931S43544227

Atrial fibrillation subtypes classification using the General Fourier-family Transform

Atrial fibrillation patients can be classified into paroxysmal, persistent and permanent attending to the temporal pattern of this arrhythmia. The surface electrocardiogram hides this differentiation. A classification method to discriminate between the different subtypes of atrial fibrillation by using short segments of electrocardiograms recordings is presented. We will process the electrocardiograms (ECGs) using time-frequency techniques with a global accuracy of 80%. Real cases are evaluated showing promising results for an implementation in a semiautomated diagnostic system.This work was supported by grants MTM2010-15200, PrometeoII/2013/013 and UPV-IIS La Fe, 2012/0468.Ortigosa, N.; Cano, O.; Ayala Gallego, G.; Galbis Verdu, A.; Fernandez Rosell, C. (2014). Atrial fibrillation subtypes classification using the General Fourier-family Transform. Medical Engineering and Physics. 36(4):554-560. https://doi.org/10.1016/j.medengphy.2013.12.005S55456036

Automated detection of atrial fibrillation using long short-term memory network with RR interval signals

Atrial Fibrillation (AF), either permanent or intermittent (paroxysnal AF), increases the risk of cardioembolic stroke. Accurate diagnosis of AF is obligatory for initiation of effective treatment to prevent stroke. Long term cardiac monitoring improves the likelihood of diagnosing paroxysmal AF. We used a deep learning system to detect AF beats in Heart Rate (HR) signals. The data was partitioned with a sliding window of 100 beats. The resulting signal blocks were directly fed into a deep Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM). The system was validated and tested with data from the MIT-BIH Atrial Fibrillation Database. It achieved 98.51% accuracy with 10-fold cross-validation (20 subjects) and 99.77% with blindfold validation (3 subjects). The proposed system structure is straight forward, because there is no need for information reduction through feature extraction. All the complexity resides in the deep learning system, which gets the entire information from a signal block. This setup leads to the robust performance for unknown data, as measured with the blind fold validation. The proposed Computer-Aided Diagnosis (CAD) system can be used for long-term monitoring of the human heart. To the best of our knowledge, the proposed system is the first to incorporate deep learning for AF beat detection

Exploring Transformers and Degradation Methods in the Super Resolution Field

Super Resolution is one of the most difficult fields to explore as the real world degradations are unknown and hard to be mathematically modeled. This research project aims at exploring different approaches for improving both efficiency and results of the existing algorithms by adapting a denoising method for the Super Resolution task and implementing a new degradation pipeline which would better simulate the real scenarios. The method was evaluated on three datasets containing reference images and performs the best on average. For real images which do not contain a reference, our solution provides results with more details and textures, therefore having a more pleasant looking outcome

The use of autofluorescence for screening and early detection of oral potentially malignant disorders – A narrative review

Premalignant lesions of the oral cavity encompass a broad range of pathology and are often comorbid in a variety of patient populations. Prompt diagnosis and management of these lesions are essential to prevent patient morbidity and mortality. The purpose of this article is to summarize and review the evaluation, screening and early detection of premalignant lesions of the oral cavity and to highlight the role of the dental team in recognizing and treating patients with these conditions, that may progress to oral cancer. In addition, a review of a non-invasive detection technique that is currently being marketed to aid general dentists and other healthcare providers for early diagnosis of potential cancerous lesions is presented. Although many studies have assessed the diagnostic accuracy of autofluorescence in oral potentially malignant disorders (OPMDs), there has been a paucity of such information in high-risk populations