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Luani, Blerim; Braun-Dullaeus, Rüdiger C.
doi: 10.1007/s10554-024-03275-9pmid: 39527380
Navigation of electrophysiology (EP) catheters using intracardiac echocardiography (ICE) is an emerging technique to avoid fluoroscopy and simplify EP procedures. It enables zero-fluoroscopy catheter ablation of most common arrhythmias such as atrial fibrillation, atrioventricular-nodal-reentry-tachycardia, or cavotricuspid isthmus-dependent atrial flutter. In this practical guide, we share our experience and illustrate the principles as well as common manoeuvres for endovascular and intracardiac EP catheter navigation relying solely on ICE visualisation. We also review the available data and highlight the topics which require further investigation in this field.
Daniel, Emmanuel; El-Nayir, Mohammed; Ezeani, Chukwunonso; Nwaezeapu, Karldon; Ogedegbe, Oboseh John; Khan, Misha
doi: 10.1007/s10554-024-03277-7pmid: 39585526
Infective endocarditis (IE) is a severe cardiac condition associated with substantial morbidity and mortality. Traditionally, the modified Duke’s criteria have been used to establish the diagnosis of IE, which includes using transthoracic and transesophageal echocardiography. While echocardiography performs well in diagnosing native valve endocarditis, its diagnostic accuracy decreases in patients with prosthetic valves or implanted cardiac devices such as pacemakers and defibrillators. Given these limitations and advancements in cardiac imaging, including multimodal computed tomography, magnetic resonance imaging, and positron emission tomography, there has been growing interest in the utility of these techniques for diagnosing prosthetic valve endocarditis (PVE) and Cardiovascular implantable electronic device infection (CIEDI). Although numerous studies have investigated the value of these imaging modalities, their findings have been inconsistent. This article aims to reevaluate the role of advanced imaging in diagnosing PVE and CIEDI and its impact on managing prosthetic valves and device-related infective endocarditis. Methods A comprehensive literature search was conducted in PubMed, Cochrane library, Google Scholar, Embase, and other relevant databases. Key terms such as ‘infective endocarditis,’ ‘multimodal imaging,’ ‘prosthetic valve endocarditis,’ ‘18F-FDG PET,’ ‘cardiac MRI,’ and ‘cardiac CT’ were used to identify studies that investigated the role of these imaging modalities in diagnosing PVE and CIEDI. Publications with full text including randomized controlled trials, retrospective studies, case reports, case series, reviews of literature, and society guidelines were included.
Kagawa, Yoshihiko; Takafuji, Masafumi; Fujita, Satoshi; Kokawa, Takanori; Fukuma, Tomoyuki; Ishida, Masaki; Fujii, Eitaro; Okamoto, Ryuji; Kitagawa, Kakuya; Sakuma, Hajime; Dohi, Kaoru
doi:
Li, Dengao; Xing, Wen; Zhao, Jumin; Shi, Changcheng; Wang, Fei
doi: 10.1007/s10554-025-03322-zpmid: 39786626
Amid an aging global population, heart failure has become a leading cause of hospitalization among older people. Its high prevalence and mortality rates underscore the importance of accurate mortality prediction for swift disease progression assessment and better patient outcomes. The evolution of artificial intelligence (AI) presents new avenues for predicting heart failure mortality. Yet current research has predominantly leveraged structured data and unstructured clinical notes from electronic health records (EHR), underutilizing the prognostic value of chest X-rays (CXRs). This study aims to harness deep learning methodologies to explore the feasibility of enhancing the precision of predicting in-hospital all-cause mortality in heart failure patients using CXRs data. We propose a novel multimodal deep learning network based on the spatially and temporally decoupled Transformer (MN-STDT) for in-hospital mortality prediction in heart failure by integrating longitudinal CXRs and structured EHR data. The MN-STDT captures spatial and temporal information from CXRs through a Hybrid Spatial Encoder and a Distance-Aware Temporal Encoder, ultimately fusing features from both modalities for predictive modeling. Initial pre-training of the spatial encoder was conducted on CheXpert, followed by full model training and evaluation on the MIMIC-IV and MIMIC-CXR datasets for mortality prediction tasks. In a comprehensive view, the MN-STDT demonstrated the best performance, with an AUC-ROC of 0.8620, surpassing all baseline models. Comparative analysis revealed that the AUC-ROC of the multimodal model (0.8620) was significantly higher than that of models using only structured data (0.8166) or chest X-ray data alone (0.7479). This study demonstrates the value of CXRs in the prognosis of heart failure, showing that the combination of longitudinal CXRs with structured EHR data can significantly improve the accuracy of mortality prediction in heart failure. Feature importance analysis based on SHAP provides interpretable decision support, paving the way for potential clinical applications.
Eschen, Christian Kim; Banasik, Karina; Dahl, Anders Bjorholm; Chmura, Piotr Jaroslaw; Bruun-Rasmussen, Peter; Pedersen, Frants; Køber, Lars; Engstrøm, Thomas; Bøttcher, Morten; Winther, Simon; Christensen, Alex Hørby; Bundgaard, Henning; Brunak, Søren
doi: 10.1007/s10554-025-03324-xpmid: 39789341
Wu, Kuo-Chen; Hsieh, Te-Chun; Hsu, Zong-Kai; Chang, Chao-Jen; Yeh, Yi-Chun; Lu, Long-Sheng; Chang, Yuan‑Yen; Kao, Chia-Hung
doi: 10.1007/s10554-025-03327-8pmid: 39804436
Coronary artery calcification (CAC) is a key marker of coronary artery disease (CAD) but is often underreported in cancer patients undergoing non-gated CT or PET/CT scans. Traditional CAC assessment requires gated CT scans, leading to increased radiation exposure and the need for specialized personnel. This study aims to develop an artificial intelligence (AI) method to automatically detect CAC from non-gated, freely-breathing, low-dose CT images obtained from positron emission tomography/computed tomography scans. A retrospective analysis of 677 PET/CT scans from a medical center was conducted. The dataset was divided into training (88%) and testing (12%) sets. The DLA-3D model was employed for high-resolution representation learning of cardiac CT images. Data preprocessing techniques were applied to normalize and augment the images. Performance was assessed using the area under the curve (AUC), accuracy, sensitivity, specificity and p-values. The AI model achieved an average AUC of 0.85 on the training set and 0.80 on the testing set. The model demonstrated expert-level performance with a specificity of 0.79, a sensitivity of 0.67, and an overall accuracy of 0.73 for the test group. In real-world scenarios, the model yielded a specificity of 0.8, sensitivity of 0.6, and an accuracy of 0.76. Comparison with human experts showed comparable performance. This study developed an AI method utilizing DLA-3D for automated CAC detection in non-gated PET/CT images. Findings indicate reliable CAC detection in routine PET/CT scans, potentially enhancing both cancer diagnosis and cardiovascular risk assessment. The DLA-3D model shows promise in aiding non-specialist physicians and may contribute to improved cardiovascular risk assessment in oncological imaging, encouraging additional CAC interpretation.
Sano, Arata; Sugimoto, Takeshi; Iwasaki, Tomoya; Miki, Tomonori; Takai, Shigeki; Wakana, Noriyuki; Zen, Kan; Yamada, Hiroyuki; Matoba, Satoaki
doi: 10.1007/s10554-025-03328-7pmid: 39779617
Endovascular treatment (EVT) for patients with lower extremity artery disease is widely used as a less invasive alternative to surgical bypass. Recently, transradial artery intervention has gained popularity owing to its minimally invasive nature. The distance from the radial artery to the target vessel is critical for success; however, effective pre-assessment methods have not yet been established. This study aimed to evaluate the usefulness of predistance measurements from the left radial artery using simple computed tomography (CT) images. In this study, distance measurements were performed from the left radial artery to the left and right iliac artery bifurcations and from the left radial artery to the common femoral artery at the upper femoral border. These distances, measured using CT images before and after the lower-extremity contrast study, were compared with the distances identified during the lower-extremity contrast study. Distances measured using simple CT images showed a high correlation with the distances identified during the lower-extremity contrast examination (r = 0.9317, p < 0.0001; from the left radial artery to the left and right iliac artery bifurcation; r = 0.9402, p < 0.0001; and from the left radial artery to the right common femoral artery at the upper femoral border). Our results suggest that pre-distance measurement using simple CT images can be a useful tool for EVT using the left radial artery approach. Although future large-scale studies are required, this technique merits consideration owing to its widespread adoption in clinical practice.
Abbassi, Manel; Besbes, Bouthaina; Elkadri, Noomene; Hachicha, Salmen; Boudiche, Selim; Daly, Foued; Ben Halima, Manel; Jebberi, Zeynab; Ouali, Sana; Mghaieth, Fathia
doi: 10.1007/s10554-025-03329-6pmid:
Beneki, Eirini; Dimitriadis, Kyriakos; Theofilis, Panagiotis; Pyrpyris, Nikolaos; Iliakis, Panayiotis; Kalompatsou, Argyro; Kostakis, Panagiotis; Koukos, Markos; Soulaidopoulos, Stergios; Tzimas, Georgios; Tsioufis, Konstantinos; Lancellotti, Patrizio; Aggeli, Constantina
doi: 10.1007/s10554-025-03330-zpmid:
Showing 1 to 10 of 24 Articles
Myocardial extracellular volume fraction (ECV) measured via MRI serves as a quantitative indicator of myocardial fibrosis. However, accurate measurement of ECV using MRI in the presence of AF is challenging. Meanwhile, CT could be a promising alternative tool for measuring ECV regardless of sinus rhythm or AF. The purpose of this study was to assess the reliability of estimating ECV using CT in patients with AF by comparing it with MRI-derived ECV. Forty-two patients (n = 42) with AF underwent cardiac CT a median of 12 days before catheter ablation, and cardiac MRI a median of 1 day after catheter ablation. Myocardial ECV measured by CT and MRI was compared. Pre-ablation CT scan was performed in the presence of AF in 25 patients, with the remaining 17 in sinus rhythm (SR). All patients were in SR at the time of MRI post ablation. The average of CT-derived ECVs was 0.277 ± 0.022 and that of MRI-derived ECVs was 0.282 ± 0.019 in patients with AF. The average of CT-derived ECVs was 0.268 ± 0.025 and that of MRI-derived ECVs was 0.278 ± 0.025 in patients with SR at the time of the CT scan. CT and MRI were in good agreement with mean differences of -0.0048 ± 0.027 in AF and − 0.0095 ± 0.0354 in SR. CT-derived ECV in the presence of AF measured before ablation showed good agreement with ECV by MRI in SR after ablation. CT-ECV estimations are reliable and feasible in patients with AF.Graphical AbstractECV by CT in AF shows good agreement with ECV by MRI after catheter ablation in SR. ECV estimation by CT is feasible in patients with AF. AF: atrial fibrillation, ECV: extracellular volume fraction, SD: standard deviation, SR: sinus rhythm[graphic not available: see fulltext]
The initial evaluation of stenosis during coronary angiography is typically performed by visual assessment. Visual assessment has limited accuracy compared to fractional flow reserve and quantitative coronary angiography, which are more time-consuming and costly. Applying deep learning might yield a faster and more accurate stenosis assessment. We developed a deep learning model to classify cine loops into left or right coronary artery (LCA/RCA) or “other”. Data were obtained by manual annotation. Using these classifications, cine loops before revascularization were identified and curated automatically. Separate deep learning models for LCA and RCA were developed to estimate stenosis using these identified cine loops. From a cohort of 19,414 patients and 332,582 cine loops, we identified cine loops for 13,480 patients for model development and 5056 for internal testing. External testing was conducted using automated identified cine loops from 608 patients. For identification of significant stenosis (visual assessment of diameter stenosis > 70%), our model obtained a receiver operator characteristic (ROC) area under the curve (ROC-AUC) of 0.903 (95% CI: 0.900–0.906) on the internal test. The performance was evaluated on the external test set against visual assessment, 3D quantitative coronary angiography, and fractional flow reserve (≤ 0.80), obtaining ROC AUC values of 0.833 (95% CI: 0.814–0.852), 0.798 (95% CI: 0.741–0.842), and 0.780 (95% CI: 0.743–0.817), respectively. The deep-learning-based stenosis estimation models showed promising results for predicting stenosis. Compared to previous work, our approach demonstrates performance increase, includes all 16 segments, does not exclude revascularized patients, is externally tested, and is simpler using fewer steps.
We hypothesize that epicardial adipose tissue (EAT) structure differs between patients with coronary disease and healthy individuals and that EAT may undergo changes during an acute coronary syndrome (ACS). This study aimed to investigate EAT thickness (EATt) and structure using ultrasound radiomics in patients with ACS, patients with chronic coronary syndrome (CCS), and controls and compare the findings between the three groups. This prospective monocentric comparative cohort study included three patient groups: ACS, CCS, and asymptomatic controls. EATt was assessed using transthoracic echocardiography. Geometrical features (as mean gray value and raw integrated density) and texture features (as angular second moment, contrast and correlation) were computed from grayscale Tagged Image File Format biplane images using ImageJ software. EATt did not significantly differ between the ACS group (8.14 ± 3.17 mm) and the control group (6.92 ± 2.50 mm), whereas CCS patients (9.96 ± 3.19 mm) had significantly thicker EAT compared to both the ACS group (p = 0.025) and the control group (p < 0.001). Radiomics analysis revealed differences in geometrical parameters with discriminatory capabilities between both ACS group and controls and CCS group and controls. A multivariate analysis comparing ACS and CCS patients revealed that differences in EAT characteristics were significant only in patients with a body mass index below 26.25 kg/m². In this subgroup, patients older than 68 exhibited a higher modal gray value (p = 0.016), whereas those younger than 68 had a lower minimum gray value (p = 0.05). Radiomic analysis highlights its potential in developing imaging biomarkers for early diagnosis and coronary artery disease progression monitoring.Graphical Abstract[graphic not available: see fulltext]
BackgroundIntracardiac echocardiography (ICE) appears to be a potential alternative for percutaneous left atrial appendage occlusion (LAAO) to transesophageal echocardiography (TEE). Thus, a meta-analysis was performed comparing ICE vs. TEE for LAAO guidance.MethodsA comprehensive literature search was performed using MEDLINE, Scopus and Web of Science electronic databases from their inception to November 2023.Results18 studies (124,230 patients) were included. Technical success was higher in ICE- compared to TEE-guidance (OR: 1.36, 95% CI 1.14 to 1.63, p = 0.006) and fewer devices employed (SMD: -0.22, 95% CI -0.43 to -0.01, p = 0.04, I2 = 62%). ICE guidance related with more pericardial effusion/tamponade and iatrogenic residual shunts (logRR: 0.62, 95% CI 0.36 to 0.89, p < 0.001 and RR: 1.53, 95% CI 1.12 to 2.09, p = 0.02, I2 = 1%, respectively). More vascular complications were noted in ICE group (logRR: 0.45, 95% CI 0.11 to 0.78, p = 0.009).ConclusionICE-guided imaging is an effective alternative to TEE in LAAO, as it shows better efficacy than TEE, considering technical success. However, the higher rates of adverse events should be carefully considered.