Activation Mapping Research Articles

Ablation of right ventricular endocavitary arrhythmias

Abstract Background The right ventricular moderator band-papillary muscle (RV MB-PM) complex has a rich subendocardial network of purkinje fibers which can lead to ventricular arrhythmias (VA)1. Catheter ablation of such arrhythmias is procedurally challenging and there remains limited studies on long-term procedural success. Purpose We aimed to systematically study RV MB-PM complex VA. Methods A single center retrospective study from January 2013 to December 2023 identified 3249 patients who underwent VA ablation. The RV MB-PM complex was targeted in 47 patients. Acute success was defined as suppression of the targeted VA >=30 minutes post ablation. Long-term success was defined as the absence of symptoms, total PVC burden <5% or improvement in EF in patients presenting with isolated PVCs and as freedom from appropriate ICD therapy in patients presenting with malignant PVCs inducing VF, sustained VT or VT storm. Results Mean age was 51.9 ± 16.7 years and 64% were male. 40 (85%) patients had VA targeted from the MB and 7 (15%) from the PM complex (2 anterior, 2 inferior and 3 septal PM). 34 (72%) had no underlying structural heart disease, 3 (6%) ischemic cardiomyopathy and 10 (21%) non-ischemic cardiomyopathy. No patient had delayed gadolinium enhancement in the RV MB-PM complex. VA were inducible in 42 (89%) of patients and activation mapping was used in 9 (19%), pace mapping in 5 (11%) and a combination of activation and pace mapping used in 33 (70%) patients. Intracardiac echocardiography was used in all cases. Pre-QRS duration was 34.0 ± 10.4 ms. Radiofrequency ablation was used in 37 (78.7%) cases, cryotherapy in 4 (8.51%) cases and a combination of the 2 energy sources in 6 (12.8%) cases. In all cases, acute peri-procedural success was achieved. 24 (51.1%) patients presented with isolated PVCs and were followed up for 4.72 ± 3.55 years after their index procedure. 3 patients underwent repeat catheter ablation targeting the same PVC focus but all patients had long term success with a PVC burden of 16.1 ± 12.5% prior to ablation and 2.40 ± 2.44% at last follow-up. 16 patients underwent ablation for recurrent ICD shocks after initially presenting with PVC induced VF. Patients were followed up for 3.22 ± 2.09 years and 14 (88%) patients remained free from ICD therapy. 7 patients had MB ablation for sustained VT and were followed up for 4.06 ± 3.77 years. 5 (71%) patients remained free from ICD therapy. A purkinje potential was seen in 8% (2/24) cases of isolated PVCs and in 39% (9/23) cases of PVC induced VF or VT (p=0.13). Conclusions RV MB-PM VA commonly present with malignant VF and a purkinje potential is more likely to be seen in these cases suggesting different VA mechanisms to isolated PVCs and catheter ablation is an effective long-term strategy.

The connection site of the Bachmann bundle to the left atrium affects atrial electrical and anatomical remodeling

Abstract Background A common route for the propagation of the conduction system from the right atrium to the left atrium (LA) is via the Bachmann bundle (BB) which is a parallel-aligned muscular structure. It is empirically known that the site of conduction from BB to LA differs depending on the patient. Purposes To clear the clinical significance of differences in the BB conduction sites in patients with atrial fibrillation (AF). Methods We studied 182 patients (90 non-paroxysmal AF, 121 males, and 68.2 ± 10.0 years) who received catheter ablation for AF. Clinical data, echocardiographic data, and blood samples were evaluated. High-density LA mapping during high right atrial pacing was constructed. The site of conduction from BB to LA, the global activation time, LA mean voltage, the abnormal conduction zone (ACZ), and the conduction block (CBZ) were evaluated. The conduction from BB was connected to the region surrounded by the left atrial roof line, the carina of the right pulmonary vein, and the base of the left atrial appendage in all patients (Figure 1). This area was divided into four regions (right-roof, mid-roof, right-anterior, and mid-anterior area), and BB conduction sites were classified into each area by three independent observers who did not know clinical data. Global activation time for the LA was calculated by subtracting the earliest from the latest activation within the LA. Isochronal activation maps were created at a 5-ms interval setting, and ACZ (Figure 2) and CBZ were identified on the activation map by locating a site with isochronal crowding of ≥3 isochrones and ≥8 isochrones, which were calculated as ≤27 cm/s and ≤10 cm/s. Results The conduction from BB was observed in 45 patients in the right-roof area, 24 in the mid-roof, 76 in the right-anterior, 23 cases in the mid-anterior, and 14 in multiple sites. Global activation time (right-roof 108±20, mid-roof 107±16, right-anterior 105±19, and mid-anterior 117±20ms, P=0.0781) was more prolonged and left atrium diameter was larger (42.1±6.6, 42.6±8.5, 42.5±6.6, and 46.4±6.6mm, P=0.0718) in patients with mid-anterior BB connections. LA mean voltage (4.90±1.94, 5.72±2.62, 4.13±1.58, and 4.88±1.96mV, P=0.0032) was higher and plasma brain natriuretic peptide level (139±138, 67±59, 128±139, and 164±197pg/mL, P=0.0861) was lower in patients with mid-roof BB connections. ACZ (41/45 91%, 20/24 83%, 73/76 96%, and 23/23 100%, P=0.0904) and CBZ (1/45 2%, 0/24 0%, 8/76 11%, and 2/23 9%, P=0.0850) appearance rates were lower in patients with mid-roof BB connections. Conclusion The connection site of the Bachmann bundle to the left atrium may affect atrial electrical and anatomical remodeling in patients with atrial fibrillation.

Right ventricular outflow tract arrhythmias in patients with obstructive sleep apnea- electrophysiological insights

Abstract Background Obstructive sleep apnea (OSA) was associated with atrial fibrillation and heart failure. Furthermore, the arrhythmic sudden death during sleep in patients with OSA raises serious concerns. Non-ischemic ventricular arrhythmias arise frequently from the right ventricular outflow tract (RVOT). The involvement of obstructive sleep apnea in the genesis of arrhythmia (premature ventricular contraction or tachycardia) originating specifically in the RVOT is not yet coherently established. Purpose This study aims to demonstrate the possible relationship between untreated obstructive sleep apnea and the occurrence of RVOT arrhythmia and to follow the electrophysiological features and outcomes after radiofrequency catheter ablation (RFA) in patients carrying this double burden. Methods One-hundred-twelve (112) consecutive asymptomatic and symptomatic patients admitted with RVOT arrhythmia ((PVCs and VTs) underwent a sleep study (polysomnography). Apnea was set out as the cessation of breathing and hypopnea was defined as shallow, slow, and restricted breathing occurring in episodes of ten seconds or more. The patients had normal left and right ventricle ejection fractions and no structural cardiovascular disease or other known extracardiac arrhythmia predisposing conditions. No significantly elevated systolic pressure in the pulmonary artery was assessed at echocardiography. Every patient was investigated by electrophysiologic study (EPS) and RFA of the clinical ventricular arrhythmia was immediately performed. Practicing and former professional athletes were excluded. Results Seventy-eight (69 %) of the included patients had an apnea-hypopnea index > 10/h. The included patients had the following characteristics: mean age 52 ± 8 years, 62% males, 81% had a BMI >30kg/m². The cardiac electrophysiologic study did not induce sustained VTs. The distribution of the PVCs and non-sustained VTs evidenced a predilection for the superior, septal and anterior (68%) and also superior and anterolateral aspects of the RVOT (23%). Radiofrequency catheter ablation (RFA) guided by activation mapping, precocity (the site of earliest activation > 30 ms before the setting of the QRS complex) and pace-mapping were performed in all patients. Immediate success was obtained in 98 patients (88%). After a mean follow-up of 24± 6 months, 28 patients (28%) from the success arm had a recurrence of the PVCs or the non-sustained VTs, 22 patients (22%) with and 6 patients (6%) without sleep apnea. Conclusions RVOT arrhythmia genesis and relapse after successful RFA seem highly associated with sleep apnea. Furthermore, patients with sleep apnea are more prone to have recurrent arrhythmias after radiofrequency catheter ablation in the absence of appropriate control of OSA.

Editorial

Dear Readers, It gives me great pleasure to announce the tenth regular issue of 2024. I would like to thank all the authors for their sound research papers and the editorial board and our guest reviewers for their extremely valuable reviews and suggestions for improvement. These contributions and the generous support of the consortium members enable us to run our journal and maintain its quality. I would also like to thank our broader community for reading and incorporating sound J.UCS papers into their research. Still, I would like to expand our editorial board: If you are a tenured associate professor or above with a good publication record, please apply to join our editorial board. We are also interested in receiving high-quality proposals for special issues on new topics and emerging trends. In this regular issue, I am very pleased to introduce six accepted papers involving 17 authors from six different countries (Argentina, Brazil, Ecuador, Hong Kong, India, Saudi Arabia). Álex dos Santos Moura, Fábio Gomes Rocha, and Michel S. Soares from Brazil propose in their research a recommendation tool based on information retrieval to assist developers in choosing the suitable microservices pattern to solve a given problem. In a collaborative research effort between Ecuador and Argentina, Gonzalo P. Espinel-Mena, José L. Carrillo-Medina, Eddie E. Galarza, and Mario Matias Urbieta discuss a systematic mapping of configuration management activities in software product line. Edward Kai Fung Dang, Robert Wing Pong Luk, and Qing Li from Hong Kong contribute to the forum for negative results with their study on word bigrams for pseudo-relevance feedback in information retrieval. Prateek Thakral and Yugal Kumar from India present in their work an improved water flow optimizer (IWFO) algorithm for cluster analysis that can address the issues of traditional and heuristic algorithms. Samit Bhanja, Banani Ghose, and Abhishek Das from India highlight their findings on multi-step-ahead time series forecasting using a deep learning and fuzzy time series-based error correction method. And last but not least, Abdullilah A. Alotaibi and Salman A. AlQahtani from Saudi Arabia present in their research an intelligent distributed channel selection framework with hybrid mode selection for interference mitigation in D2D based 5G networks.  Enjoy Reading! Best regards,  Christian Gütl, Managing Editor Graz University of Technology, Graz, Austria

A Systematic Mapping of Configuration Management Activities in Software Product Line

In the software product line (SPL), configuration management (CM) is a multidimensional problem that is taking great attention in software development.  Although much research has been developed on this topic, there is no clear view of the current state of it. In this study, we used a systematic method to develop a map of configuration management across product lines to classify relevant literature. The resulting map provides an overview of this research through the identification of the main activities of the CM, the types and trends of research as well as the maturity of existing contributions. Because the CM in SPL is still in its formative stage, we believe that this work will contribute to the process of providing a more common and coherent conceptual basis for its understanding. In addition, it can help to detect important research problems and gaps.

An operational risk assessment method for petrochemical plants based on deep learning

The petrochemical industry is an important guarantee for the development of people’s lives, and the operational risk assessment method of personnel in the operation of petrochemical enterprises is the most important. Based on a deep learning algorithm, a new method based on the micro-Doppler effect and fuzzy analytic hierarchy process is proposed to evaluate the operational risk of personnel in petrochemical enterprises. The original monitoring image of petrochemical equipment is invoked, and micro-Doppler analysis is performed based on the original image to identify the activity target of the personnel on the job site and generate the activity map of the petrochemical plant operators. Based on the analysis data of the micro-Doppler effect, the fuzzy function and hierarchical analysis method are combined. The fuzzy theory is introduced into the analytic hierarchy process, and the inherent expert evaluation scores are transformed into fuzzy numbers. It makes the expert evaluation of petrochemical plants more accurate in the subsequent coupling and improves the objectivity of the traditional analytic hierarchy process. The scheme proposed in this paper can monitor real-time operation safety and provide a guarantee for the personal safety of field operators. This method plays an important role in improving the safety of petrochemical plants.

Participation of epicardial connection in transverse conduction across the crista terminalis in patients with upper and lower loop reentry atrial tachycardias

Abstract Background The crista terminalis (CT) sometimes serves as a partial or complete conduction barrier, which is rate- and site-dependent. The basis, location and extent of the transverse conduction across the CT have been controversial. The pseudo-focal activation pattern indicates passive activation from the anatomically opposite chamber or structure. We previously reported cases of transverse CT conduction exhibiting a pseudo-focal activation pattern, indicating the CT conduction utilizing an epicardial connection (EC) in patients with upper loop reentry (ULR) atrial tachycardia (AT). Purpose This study aimed to investigate the prevalence and characteristics of transverse CT conduction using a high-resolution electroanatomical mapping system during CT-dependent AT and to compare between the ULR and lower loop reentry (LLR) ATs. Methods A total of 119 consecutive patients with macroreentrant AT were studied. The diagnosis of ULR/LLR AT was made based on endocardial activation mapping and entrainment pacing obtained from various sites around the superior/inferior vena cava. Transverse CT conduction utilizing an EC was defined as follows; (1) a centrifugal activation pattern with the earliest activation (EA) site >5 mm away from the CT, (2) later activation observed between the EA and the CT, and (3) the EA site exhibiting a pseudo-focal activation pattern (multiple breakthroughs or a small r wave on unipolar electrogram). Results Eighteen (15.1%) of the 119 patients were diagnosed with CT-dependent AT (15 men; median age, 71.5 years; AT cycle length, 265 ms; left atrial diameter, 42.5 mm; left ventricular ejection fraction, 49%). The 18 patients were divided into 2 groups: 9 patients with ULR and 9 with LLR AT. There were no significant differences in clinical characteristics (sex, history of cardiac surgery and organic heat disease) between ULR and LLR groups. Transverse CT conduction utilizing EC was more frequently observed in the ULR group than in the LLR group [77.8% (7/9) vs 22.2% (2/9), P=0.05]. In all patients with transverse CT conduction utilizing EC, scars extended along the CT. AT cycle length was significantly longer in the ULR group than in the LLR group (295 vs 235 ms, p=0.001). All ATs were successfully eliminated by radiofrequency ablation at the breakthrough sites and/or along the CT in ULR patients or by cavotricuspid isthmus ablation in LLR patients. Conclusions Half (9/18) of the patients with CT-dependent AT exhibited transverse CT conduction with a pseudo-focal activation pattern, suggesting the existence of an EC between the sinus venosus and the right auricle. The high prevalence of the pseudo-focal pattern in patients with the ULR compared to those with the LLR may provide insights into mechanisms.Figure1Figure2

Identifying heart failure dynamics using multi-point electrocardiograms and deep learning

Abstract Background Heart failure (HF) hospitalizations are related to poor survival outcomes, emphasizing the need for early detection and intervention. Recent studies have reported the potential of deep learning algorithms in identifying heart failure through electrocardiogram (ECG) with notable accuracy. However, evidence supporting their utility in the ongoing monitoring and follow-up of HF patients remains uncertain. Advanced diagnostic tools that can effectively track heart failure progression or improvement over time are needed. Purpose To address this challenge, we developed a deep learning model analyzing 12-lead ECG waveforms at two time points, aiming to detect HF deterioration or improvement, potentially revolutionizing HF management. Methods This study analyzed 32,045 ECGs from 6,892 unique adult patients (aged 18 years and older) visiting the Department of Cardiology at Kobe University Hospital. The participants were randomly assigned to training (4,284 patients), validation (1,034 patients), and test (1,034 patients) datasets. ElecTransformer was developed to classify HF status into deteriorated, improved, and no change classes based on ECG waveform signals at two different time points and to estimate brain natriuretic peptide (BNP) levels. Performance metrics, such as the area under the receiver operating characteristic curve (AUROC) and accuracy, were calculated, and attention mapping via gradient-weighted class activation mapping was utilized to interpret the model's decision-making ability. Results The patients had an average age of 65 years (±15.4 years) and BNP levels averaged 71.5 pg/mL (25.8 to 185.2 pg/mL). For HF status classification, ElecTransformer achieved an AUROC of 0.845 and an accuracy of 0.821, improving to an AUROC of 0.885 and an accuracy of 0.833 with baseline BNP integration. The ElecTransformer accurately estimated BNP levels from ECG data, evidenced by a correlation coefficient of 0.798. Averaged attention mapping showed that the QRS complex was the most important feature for determining model outputs with lesser effects seen for P waves and T waves variance Conclusions Harnessing a Transformer-based deep learning model to analyze multiple ECGs markedly improves the accuracy in detecting changes in heart failure status. This method offers a valuable non-invasive option for monitoring heart failure progression, potentially leading to better patient management and earlier intervention in heart failure treatment.Architecture and Performance of ModelsAveraged attention mapping

A pipeline for developing digital cardiac twins integrating cardiovascular magnetic resonance and electrocardiographic imaging: results from the MyoFit46 study

Abstract Background Personalised cardiovascular medicine uses multimodal screening and diagnostic tools, spanning from organ to tissue to molecular levels, to generate a complex and detailed patient phenotype. Cardiac digital twins are powerful tools capable of merging and explore these multimodal data by developing virtual, mechanistic and predictive versions of the patient's heart. State-of-the-art pipelines are mainly based on artificial intelligence and optimization process to replicate clinical data and not used it as an input. We present a proof-of-principle pipeline for building digital heart twins based on cardiovascular magnetic resonance (CMR) imaging and integrated electrocardiographic imaging (ECGI) for diverse clinical scenarios by integrating them into the model (Fig. 1). Methods CMR at 3T was performed on participants all born in the same week in March 1946, as part of the longest-running continued surveillance birth cohort: the National Survey of Health and Development. Cine steady-state free precession end-diastolic frames provided anatomical information per participant while CMR multiparametric mapping and late gadolinium enhancement (LGE) provided tissue characteristics to the electrical myocardial model used in the finite element solver Alya. Lastly, the ECGI activation map was used as a source to define the intrinsic endocardial activation sequence by applying a case-specific correction based on wall thickness and myofibers orientation. Activation-repolarization intervals map was used to introduce the personalised repolarization heterogeneities. Results In total, 406 prospectively recruited participants (77.8 ± 0.1 years, 44% male) had multiparametric CMR with ECGI for digital twin reconstruction. The pipeline’s performance was evaluated by measuring the root mean square error (RMSE) between the ECGI activation and repolarization maps with the in-silico ones obtained without applying the proposed pipeline (Baseline) and after its application (ecgi2model). Results obtained using ecgi2model pipeline presented a significantly reduced activation and repolarization RMSE (Fig. 2b), even providing accurate results in patients with different LGE patterns (Fig. 2c). Conclusion Personalised virtual heart models can be constructed at scale using real-world myocardial structural, functional, and electrical properties, from advanced CMR and ECGI data. Proposed digital twins provide a framework for personalised arrhythmic risk assessment based on real clinical data and also its applicability for in-silico clinical trials. Fig.1 Digital twins pipeline. Fig.2 a) Flattened epicardial map. b) RMSE of activation (ACT), repolarization (RPL) and activation-repolarization intervals (ARI) for the different methods methods. c) ACT and RPL maps obtained from ECGI, baseline simulation and ecgi2model simulation in a healthy participant with normal myocardial tissue, another with subendocardial LGE, and another with subepicardial LGE.Figure 1Figure 2

Atrial pace mapping with automatic intracardiac pattern matching for non-pulmonary vein trigger ablation: accuracy and feasibility

Abstract Atrial pace mapping (APM) is an alternative method to localize non-pulmonary vein (non-PV) triggers of atrial fibrillation(AF) that are otherwise not eligible for activation mapping due to their instability. The technique applies pattern matching of p-waves and intracardiac signals of atrial trigger and pacing from the site of the suspected origin. A new automated algorithm for intracardiac (IC) pattern matching was recently introduced into the 3D mapping system using the unipolar signal of the reference catheter. The study aims to assess the accuracy and feasibility of APM using IC-pattern matching for localizing non-PV triggers. Methods We assessed the precision of APM using a single chamber and double chamber reference in 20 patients who underwent index pulmonary vein isolation. APM was performed at the anterior and posterior wall of the left atrium (LApost, Laant) and the posterior and lateral wall of the right atrium (Rapost, Ralat). The first pacing point was declared as the origin. The IC-pattern matching algorithm was applied to calculate the similarity of the IC-pattern of the origin to the paced IC-pattern across various distances. The distribution of IC-pattern similarities was analyzed in the Python environment. The feasibility of APM was tested in patients who underwent REDO ablation of AF if all PVs were isolated and no low-voltage substrate was detected. The induction protocol for non-PV triggers included programmed and burst atrial pacing, high-dose isoproterenol, and adenosine infusion. If the IC pattern of the trigger was reproducible, the IC pattern matching was conducted. Ablation was performed at the site of the best IC-pattern matching. Results Atrial PM with IC-pattern matching and a single referent catheter showed lower precision in the RA lateral region (the median[Q1; Q3 ]area of 5% best matching score 1.48 [0.76; 2.01], 0.62[0.15; 1.06], 0.6[0.38; 1.19] and 5.68[2.89; 11.1] cm2 for LApost, LAant, RApost, RAlat accordingly, p = 0.0316). IC-pattern matching with double referent catheter showed high precision at all four anatomical regions (the median [Q1; Q3] area of 5% best matching score 0.126 [0.0879; 0.356], 0.486 [0.205; 0.717], 0.269 [0.198; 0.719], and 0.257 [0.0740; 0.572] cm2 for LApost, LAant, RApost, RAlat accordingly, Fig. 1). Seven out of 37 patients referred to REDO ablation had all PV isolated and absence of low-voltage substrate. In 3 of them, sustained tachycardias (AVNRT, focal atrial tachycardia and typical atrial flutter) were induced, mapped, and ablated. Non-sustained FAT from the RA posterior wall and PAC in the RA septum were induced under the high-dose isoproterenol infusion in two patients. Atrial pace mapping with double chamber referent and successful ablation of these non-PV triggers were performed (Fig.2). Conclusion The atrial pace mapping of non-PV triggers employing the automatic intracardiac pace mapping algorithm is feasible with high accuracy if a double-chamber referent is used.Accuracy of atrial pace mappingNon-PV trigger pace mapping

Accuracy of endocardial abnormal electrogram characterization using a 17-AHA-segmental late boundary and voltage-dependent sensitivity annotation for ventricular tachycardia ablation

Abstract Background Substrate-based catheter ablation of ventricular tachycardia (VT) has become the mainstay therapy for patients with scar-related VT. Correct identification and timing of abnormal near-field EGMs and discerning it from far-field EGMs, is key for procedural success. The novel CARTO™ Late Activation Mapping (LAM) module provides fast and accurate annotation of near-field EGMs including late abnormal ventricular activation. For the algorithm to succeed, a late boundary (LB) annotation line should be positioned immediately behind the normal near-field EGM, however, since the timing of normal local near-field activation varies greatly throughout the endocardium, there is currently no consensus for the optimal LB positioning. We hypothesized that 17-AHA segmental annotation of the endocardial LB followed by scar-dependent sensitivity settings, will improve the accuracy of the near-field EGM characterization. Purpose To evaluate the accuracy of endocardial abnormal EGM characterization using a 17-AHA-segmental late boundary and voltage-dependent sensitivity annotation as compared to conventional mid-QRS LB positioning. Methods Five high-resolution substrate voltage maps were obtained from 5 patients (80% male, 68±7 years old, 80% ischemic cardiomyopathy) that underwent a catheter-based ablation for scar-related VT. A personalized CT-derived anatomical reconstruction of the 17-segment AHA model was co-registered offline with the electroanatomic voltage map, using ADAS3D software. Per segment, individual LB timings were set at the highest sensitivity. A gold standard annotation map was created by two independent experts after manual scrutiny of individual EGMs. Next, the expert map was compared to automatic maps created from segmental LB and mid-QRS LB positions at normal, high, highest or mixed (normal/high or normal/highest) sensitivities, focusing on EGM voltage and activation accuracy. Results A mean of 2041±1028 points per map were analyzed. Correct correlation of EGM annotation was found on average in 91% (activation) and 81% (voltage) for all segmental LB positioned maps (segLB), and in 83% (activation) and 77% (voltage) for all mid-QRS LB positioned maps (mid-QRSLB). The highest correlation with expert map for both activation and voltage annotation was found in the mixed sensitivity normal/highest map with segmental LB positioning (activation 98%, voltage 88%). The correlation on activation was significantly superior in the segLB map when compared to the normal sensitivity maps (p=0.0008 for mid-QRSLB and p=0.02 for segLB). For correlation on voltage, the segLBmap with mixed sensitivity was significantly superior compared to three mid-QRSLB maps at normal (p=0.04), high and highest (p=0.01) sensitivity. Conclusion Individualized, 17-AHA-segmental late boundary and voltage-dependent sensitivity annotation provides a possible accurate and clinically-relevant framework for substrate assessment. Prospective validation is needed.

Prevalance of incessant form of atrial fibrillation by superior vena cava (svc) firing:simplified svc and sinus node mapping(sss map) to avoid sinus node injury

Abstract Background The superior vena cava (SVC) is known as a major source of atrial fibrillation(AF), non-pulmonary vein AF foci (NPVAF). In some cases, an incessant form of AF (iAF) by SVC firing prevents obtaining the sinus node location, and anatomical SVC isolation has a risk of sinus node injury (SNI). Objective This study was aimed to explore the frequency of iAF by SVC firing and the efficacy of the simplified mapping strategy to avoid SNI. Methods Consecutive AF ablation procedures in the 3 centers were retrospectively investigated including the NPVAF prevalence. Simplified SVC/SN mapping (SSS-map: Obtaining the top-end of the SVC potential and >4points with 5-10mm spacing in the upper lateral side of the right atria (RA) for roughly locating the earliest activation site in sinus rhythm) was performed before PV isolation in the last 138 patients. In case of the initial rhythm at the procedure was atrial fibrillation, cardioversion was performed for obtaining the SSS-map. In the cases in which the high-density mapping catheters were available, the high-density activation maps of the RA in sinus rhythm were obtained to investigate the dislocation of the earliest activation site of the SSS map from the SN location. When SVC isolation was required due to iAF, SVC isolation line was designed based on the location of the top-end of the SVC potential during AF and ablative linear lesions were created to keep the distance of 5-10 mm above the second earliest activation site. Results A total of 1160 procedures in 1089 patients (male: 736[63.4%], age: 69.9, paroxysmal AF: 544[49.9%], first session: 997[85.9%]) was investigated. Prevalence of NPVAF was revealed in 160 of the all procedures (13.7%) (the SVC: 64, the posterior wall of the left atria: 24, the coronary sinus: 19, and others: 53). 16 of 160 NPVAFs (10.0%) developed to iAF by SVC firing. The SSS-map was successfully obtained in all of the last 138 patients (Initial rhythm at the procedure: AF 43.7%, Mapping time: 1.3±0.6 min, number of obtained points: 8.4±1.3). In 21 patients in which the high-density RA map was obtained, the SN dislocation distance was 5.1±2.2mm between the SSS map and the high-density RA map. Five of 138 developed to iAF by SVC firing, and SVC isolation based on the SSS-map was successfully achieved without SNI in the cases. Conclusion This study indicated iAF driven by SVC firing after PV isolation occurred in certain number of patients. The SSS-map before PV isolation was practical and showed the potential to be the mapping strategy avoid the SN injury in such cases.

Efficient and Compressed Deep Learning Model for Brain Tumour Classification With Explainable AI for Smart Healthcare and Information Communication Systems

ABSTRACTThe detection of brain tumours presents a significant challenge in the medical domain, where prompt and precise diagnosis is crucial as patient outcomes depend on it. Conventional deep neural networks perform well in carrying out various imaging tasks within the healthcare sector; however, their effectiveness often falls short of expectations in practical applications due to the substantial computational resources required and issues with reliability. In this research, an optimised and effective deep learning model founded on the DenseNet‐169 architecture is introduced for the classification of magnetic resonance imaging brain tumours, which is particularly advantageous for smart healthcare systems and information and communication technology (ICT) settings with limited computational capabilities. The model compression methodologies, including pruning and quantization, have been employed to significantly diminish the dimensions and intricacy of the model while achieving a classification accuracy of 97.07%. Furthermore, this endeavour necessitates the enhancement of the model's interpretability through the utilisation of explainable artificial intelligence methodologies such as Gradient‐weighted Class Activation Mapping (Grad‐CAM) and SHapley Additive exPlanations (SHAP), which will aid clinicians in highlighting crucial areas of the images and validating feature importance concerning the decisions rendered by the model. A comparative performance evaluation is conducted against DenseNet‐169, ResNet‐50 and various other models to delineate the superior efficacy of our model, rendering it exceptionally adept for knowledge‐driven, real‐time brain tumour diagnosis within smart healthcare and ICT systems where resources are constrained.

Fatigue life prediction of composite materials using strain distribution images and a deep convolution neural network

The damage process of composite materials, such as short fiber-reinforced plastics (SFRP), is complex. Therefore, it is necessary to accurately represent the damage process in fatigue life prediction. Herein, fatigue life prediction was conducted by combining the digital image correlation method, which is a non-destructive testing technique, with a convolutional neural network (CNN), using Xception as the network architecture. High prediction accuracy was obtained when training and testing were performed on the same SFRP specimens. In contrast, using different specimens for training and testing resulted in lower accuracy. This issue may be improved by increasing the number of specimens. The regions of interest in the model were visualized by Gradient-weighted Class Activation Mapping. Notably, the model indicated the breaking point as the region of interest from the early stages of the test. The breaking point was identified at an earlier stage by the CNN than by visual inspection, demonstrating the potential for a new method of damage observation.

Developing a European framework for the prevention of communicable diseases: three points for attention.

The capacity to deliver programmes that prevent and control infectious diseases is a key public health function. The European Centre for Disease Prevention and Control (ECDC) aims to support and strengthen this capacity in European Union/ European Economic Area (EU/EEA) countries as part of its 2021-27 strategy which includes explicit attention to social and behavioural aspects of disease prevention. To achieve its strategic goals, it is important that ECDC improves its knowledge of prevention strategies, actors and activities in EU/EEA countries. In this Perspective, we summarise three challenges to implementing the prevention framework proposed by ECDC: (i) defining, recognising and identifying with 'prevention', (ii) integrating new understandings into established ways of thinking, and (iii) the need for more attention to prevention in governance. These challenges are derived from the findings of a project which conducted a preliminary mapping of prevention actors, networks and activities in four EU countries to support the development of a community of practice within the new ECDC prevention framework. This Perspective serves to draw attention to this prevention framework and the three identified challenges for those working on its implementation.

Brain-movement relationship during upper-limb functional movements in chronic post-stroke patients

BackgroundFollowing a stroke, brain activation reorganisation, movement compensatory strategies, motor performance and their evolution through rehabilitation are matters of importance for clinicians. Two non-invasive neuroimaging methods allow for recording task-related brain activation: functional near-infrared spectroscopy (fNIRS) and electroencephalography (fEEG), respectively based on hemodynamic response and neuronal electrical activity. Their simultaneous measurement during movements could allow a better spatiotemporal mapping of brain activation, and when associated to kinematic parameters could unveil underlying mechanisms of functional upper limb (UL) recovery. This study aims to depict the motor cortical activity patterns using combined fNIRS-fEEG and their relationship to motor performance and strategies during UL functional tasks in chronic post-stroke patients.MethodsTwenty-one healthy old adults and 21 chronic post-stroke patients were recruited and completed two standardised functional tasks of the UL: a paced-reaching task where they had to reach a target in front of them and a circular steering task where they had to displace a target using a hand-held stylus, as fast as possible inside a circular track projected on a computer screen. The activity of the bilateral motor cortices and motor performance were recorded simultaneously utilizing a fNIRS-fEEG and kinematics platform.Results and conclusionsKinematic analysis revealed that post-stroke patients performed worse in the circular steering task and used more trunk compensation in both tasks. Brain analysis of bilateral motor cortices revealed that stroke individuals over-activated during the paretic UL reaching task, which was associated with more trunk usage and a higher level of impairment (clinical scores). This work opens up avenues for using such combined methods to better track and understand brain-movement evolution through stroke rehabilitation.

MaskAppendix: Backbone-Enriched Mask R-CNN Based on Grad-CAM for Automatic Appendix Segmentation

Background: A leading cause of emergency abdominal surgery, appendicitis is a common condition affecting millions of people worldwide. Automatic and accurate segmentation of the appendix from medical imaging is a challenging task, due to its small size, variability in shape, and proximity to other anatomical structures. Methods: In this study, we propose a backbone-enriched Mask R-CNN architecture (MaskAppendix) on the Detectron platform, enhanced with Gradient-weighted Class Activation Mapping (Grad-CAM), for precise appendix segmentation on computed tomography (CT) scans. In the proposed MaskAppendix deep learning model, ResNet101 network is used as the backbone. By integrating Grad-CAM into the MaskAppendix network, our model improves feature localization, allowing it to better capture subtle variations in appendix morphology. Results: We conduct extensive experiments on a dataset of abdominal CT scans, demonstrating that our method achieves state-of-the-art performance in appendix segmentation, outperforming traditional segmentation techniques in terms of both accuracy and robustness. In the automatic segmentation of the appendix region in CT slices, a DSC score of 87.17% was achieved with the proposed approach, and the results obtained have the potential to improve clinical diagnostic accuracy. Conclusions: This framework provides an effective tool for aiding clinicians in the diagnosis of appendicitis and other related conditions, reducing the potential for diagnostic errors and enhancing clinical workflow efficiency.

An One-step Triple Enhanced weakly supervised semantic segmentation using image-level labels.

Weakly supervised semantic segmentation, based on image-level labels, abandons the pixel-level labels relied upon by traditional semantic segmentation algorithms. It only utilizes images as supervision information, thereby reducing the time cost and human resources required for marking pixel data. The prevailing approach in weakly supervised segmentation involves two-step method, introducing an additional network and numerous parameters, thereby complicating the model structure. Furthermore, image-level labels typically furnishes only category information for the entire image, lacking specific location details and accurate target boundaries during model training. We propose an innovative One-Step Triple Enhanced weakly supervised semantic segmentation network(OSTE). OSTE streamlines the model structure, which can accomplish both pseudo-labels generation and semantic segmentation tasks in just one step. Furthermore, we augment the weakly supervised semantic segmentation network in three key aspects based on the class activation map construction method, thereby enhancing segmentation accuracy: Firstly, by integrating local information from the activation map with the image, we can enhance the network's localization and expansion capabilities to obtain more accurate and rich location information. Then, we refine the seed areas of the class activation map by exploiting the correlation between multi-level feature. Finally, we incorporate conditional random field theory to generate pseudo-labels with higher confidence and richer boundary information. In comparison to the prevailing two-step weakly supervised semantic segmentation schemes, the segmentation network proposed in this paper achieves a more competitive mean Intersection over Union (mIoU) score of 58.47% on Pascal VOC. Additionally, it enhances the mIoU score by at least 5.03% when compared to existing end-to-end schemes.

Unsupervised generative model for simulating post-operative double eyelid image.

Simulating the outcome of double eyelid surgery is a challenging task. Many existing approaches rely on complex and time-consuming 3D digital models to reconstruct facial features for simulating facial plastic surgery outcomes. Some recent research performed a simple affine transformation approach based on 2D images to simulate double eyelid surgery outcomes. However, these methods have faced challenges, such as generating unnatural simulation outcomes and requiring manual removal of masks from images. To address these issues, we have pioneered the use of an unsupervised generative model to generate post-operative double eyelid images. Firstly, we created a dataset involving pre- and post-operative 2D images of double eyelid surgery. Secondly, we proposed a novel attention-class activation map module, which was embedded in a generative adversarial model to facilitate translating a single eyelid image to a double eyelid image. This innovative module enables the generator to selectively focus on the eyelid region that differentiates between the source and target domain, while enhancing the discriminator's ability to discern differences between real and generated images. Finally, we have adjusted the adversarial consistency loss to guide the generator in preserving essential features from the source image and eliminating any masks when generating the double eyelid image. Experimental results have demonstrated the superiority of our approach over existing state-of-the-art techniques.

Mango leaf disease diagnosis using Total Variation Filter Based Variational Mode Decomposition

Mango leaf diseases significantly threaten mango cultivation, impacting both yield and quality. Accurate and early diagnosis is essential for effectively managing and controlling these diseases. This study introduces a novel approach for diagnosing mango leaf diseases, leveraging Total Variation Filter-based Variational Mode Decomposition. The proposed method enhances the extraction of disease-specific features from leaf images by decomposing them into intrinsic mode functions while simultaneously reducing noise and preserving important edge information. Experimental results demonstrate that the proposed method effectively isolates relevant patterns associated with various mango leaf diseases, improving diagnostic accuracy compared to traditional methods. Deep learning models, DenseNet121 and VGG-19, are used for feature extraction from sub-band images, and extracted features are concatenated and fed to Random Forest for classification. Utilizing tenfold cross-validation, our model demonstrated enhanced classification accuracy (98.85 %), specificity (99.37 %), and sensitivity (98.0 %) in detecting diseases from Mango leaf images. Feature maps and Gradient-weighted Class Activation Mapping analysis was conducted to visualize and scrutinize the essential regions crucial for accurate predictions. Statistical analysis indicates that our proposed architecture outperforms pre-trained models and existing mango leaf disease detection methods. This diagnostic approach can be a rapid disease detection tool for imaging specialists utilizing leaf images. The robustness and efficiency of the presented work in handling complex and noisy image data make it a promising tool for automated agricultural disease diagnosis systems, facilitating timely and precise interventions in mango orchards.