Atrial fibrillation (AF) in heart failure with reduced ejection fraction (HFrEF) has prognostic implications. Using a machine learning algorithm (FIND-AF), we aimed to explore clinical events and the cardiac magnetic resonance (CMR) characteristics of the pre-AF phenotype in HFrEF. A cohort of individuals aged ≥18 years with HFrEF without AF from the MATCH 1 and MATCH 2 studies (2018-2024) stratified by FIND-AF score. All received cardiac magnetic resonance using Cvi42 software for volumetric and T1/T2. The primary outcome was time to a composite of MACE inclusive of heart failure hospitalisation, myocardial infarction, stroke and all-cause mortality. Secondary outcomes included the association between CMR findings and FIND-AF score. Of 385 patients [mean age 61.7 (12.6) years, 39.0% women] with a median 2.5 years follow-up, the primary outcome occurred in 58 (30.2%) patients in the high FIND-AF risk group and 23 (11.9%) in the low FIND-AF risk group (hazard ratio 3.25, 95% CI 2.00-5.28, P < 0.001). Higher FIND-AF score was associated with higher indexed left ventricular mass (β = 4.7, 95% CI 0.5-8.9), indexed left atrial volume (β = 5.9, 95% CI 2.2-9.6), higher indexed left ventricular end-diastolic volume (β = 9.55, 95% CI 1.37-17.74, P = 0.022), native T1 signal (β = 18.0, 95% CI 7.0-29.1) and extracellular volume (β = 1.6, 95% CI 0.6-2.5). A pre-AF HFrEF subgroup with distinct CMR characteristics and poor prognosis may be identified, potentially guiding interventions to reduce clinical events.

Echocardiography is the first-line noninvasive cardiac imaging modality, providing rich spatio-temporal information on cardiac anatomy and physiology. Recently, foundation model trained on extensive and diverse datasets has shown strong performance in various downstream tasks. However, translating foundation models into the medical imaging domain remains challenging due to domain differences between medical and natural images, the lack of diverse patient and disease datasets. In this paper, we introduce EchoFM, a general-purpose vision foundation model for echocardiography trained on a large-scale dataset of over 20 million echocardiographic images from 6,500 patients. To enable effective learning of rich spatio-temporal representations from periodic videos, we propose a novel self-supervised learning framework based on a masked autoencoder with a spatio-temporal consistent masking strategy and periodic-driven contrastive learning. The learned cardiac representations can be readily adapted and fine-tuned for a wide range of downstream tasks, serving as a strong and flexible backbone model. We validate EchoFM through experiments across key downstream tasks in the clinical echocardiography workflow, leveraging public and multi-center internal datasets. EchoFM consistently outperforms SOTA methods, demonstrating superior generalization capabilities and flexibility. The code and checkpoints are available at: https://github.com/SekeunKim/EchoFM.git.

Deep learning-based myocardial scar segmentation from late gadolinium enhancement (LGE) cardiac MRI has shown great potential for accurate and timely diagnosis and treatment planning for structural cardiac diseases. However, the limited availability and variability of LGE images with high-quality scar labels restrict the development of robust segmentation models. To address this, we introduce CLAIM: \textbf{C}linically-Guided \textbf{L}GE \textbf{A}ugmentation for Real\textbf{i}stic and Diverse \textbf{M}yocardial Scar Synthesis and Segmentation framework, a framework for anatomically grounded scar generation and segmentation. At its core is the SMILE module (Scar Mask generation guided by cLinical knowledgE), which conditions a diffusion-based generator on the clinically adopted AHA 17-segment model to synthesize images with anatomically consistent and spatially diverse scar patterns. In addition, CLAIM employs a joint training strategy in which the scar segmentation network is optimized alongside the generator, aiming to enhance both the realism of synthesized scars and the accuracy of the scar segmentation performance. Experimental results show that CLAIM produces anatomically coherent scar patterns and achieves higher Dice similarity with real scar distributions compared to baseline models. Our approach enables controllable and realistic myocardial scar synthesis and has demonstrated utility for downstream medical imaging task.

Recent advancements in diffusion probabilistic models (DPMs) have revolutionized image processing, demonstrating significant potential in medical applications. Accurate segmentation of the left ventricle (LV) in echocardiograms is crucial for diagnostic procedures and necessary treatments. However, ultrasound images are notoriously noisy with low contrast and ambiguous LV boundaries, thereby complicating the segmentation process. To address these challenges, this paper introduces Echo-DND, a novel dual-noise diffusion model specifically designed for this task. Echo-DND leverages a unique combination of Gaussian and Bernoulli noises. It also incorporates a multi-scale fusion conditioning module to improve segmentation precision. Furthermore, it utilizes spatial coherence calibration to maintain spatial integrity in segmentation masks. The model's performance was rigorously validated on the CAMUS and EchoNet-Dynamic datasets. Extensive evaluations demonstrate that the proposed framework outperforms existing SOTA models. It achieves high Dice scores of 0.962 and 0.939 on these datasets, respectively. The proposed Echo-DND model establishes a new standard in echocardiogram segmentation, and its architecture holds promise for broader applicability in other medical imaging tasks, potentially improving diagnostic accuracy across various medical domains. Project page: https://abdur75648.github.io/Echo-DND

The number of patients referred for and requiring a transthoracic echocardiogram (TTE) has increased over the years resulting in more cardiac sonographers reporting work related musculoskeletal pain. We sought to determine if a scanning protocol that replaced conventional workflows with advanced technologies such as multiplane imaging, artificial intelligence (AI) and automation could be used to optimise conventional workflows and potentially reduce ergonomic risk for cardiac sonographers. The aim was to assess whether this alternate protocol could reduce active scanning time as well as interaction with the ultrasound machine compared to a standard echocardiogram without a reduction in image quality and interpretability. Volunteer participants were recruited for a study that comprised of two TTE's with separate protocols. Both were clinically complete, but Protocol A combined automation, AI assisted acquisition and measurement, simultaneous and multiplane imaging whilst Protocol B reflected a standard scanning protocol without these additional technologies. Keystrokes were significantly reduced with the advanced protocol as compared to the typical protocol (230.9 ± 24.2 vs. 502.8 ± 56.2; difference 271.9 ± 61.3, p < 0.001). Furthermore, there was a reduction in scan time with protocol A compared to protocol B the standard TTE protocol (13.4 ± 2.3 min vs. 18.0 ± 2.6 min; difference 4.6 ± 2.9 min, p < 0.001) as well as a decrease of approximately 27% in the time the sonographers were required to reach beyond a neutral position on the ultrasound console. A TTE protocol that embraces modern technologies such as AI, automation, and multiplane imaging shows potential for a reduction in ultrasound keystrokes and scan time without a reduction in quality and interpretability. This may aid a reduction in ergonomic workload as compared to a standard TTE.

The new artificial intelligence-based software, Roadmap (HeartFlow), may assist in evaluating coronary artery stenosis during cardiac computed tomography (CT) for transcatheter aortic valve replacement (TAVR). Consecutive TAVR candidates who underwent both cardiac CT angiography (CTA) and invasive coronary angiography were enrolled. We evaluated the ability of three methods to predict obstructive coronary artery disease (CAD), defined as ≥50 ​% stenosis on quantitative coronary angiography (QCA), and the need for percutaneous coronary intervention (PCI) within one year: Roadmap, clinician CT specialists with Roadmap, and CT specialists alone. The area under the curve (AUC) for predicting QCA ≥50 ​% stenosis was similar for CT specialists with or without Roadmap (0.93 [0.85-0.97] vs. 0.94 [0.88-0.98], p ​= ​0.82), both significantly higher than Roadmap alone (all p ​< ​0.05). For PCI prediction, no significant differences were found between QCA and CT specialists, with or without Roadmap, while Roadmap's AUC was lower (all p ​< ​0.05). The negative predictive value (NPV) of CT specialists with Roadmap for ≥50 ​% stenosis was 97 ​%, and for PCI prediction, the NPV was comparable to QCA (p ​= ​1.00). In contrast, the positive predictive value (PPV) of Roadmap alone for ≥50 ​% stenosis was 49 ​%, the lowest among all approaches, with a similar trend observed for PCI prediction. While Roadmap alone is insufficient for clinical decision-making due to low PPV, Roadmap may serve as a "second observer", providing a supportive tool for CT specialists by flagging lesions for careful review, thereby enhancing workflow efficiency and maintaining high diagnostic accuracy with excellent NPV.

Electrocardiogram (ECG)-gated cine imaging in breath-hold enables high-quality diagnostics in most patients but can be compromised by arrhythmia and inability to hold breath. Real-time cardiac MRI offers faster and robust exams without these limitations. To achieve sufficient acceleration, advanced reconstruction methods, which transfer data into high-quality images, are required. In this study, undersampled spiral balanced SSFP (bSSFP) real-time data in free-breathing were acquired at 1.5T in 16 healthy volunteers and five arrhythmic patients, with ECG-gated Cartesian cine in breath-hold serving as clinical reference. Image reconstructions were performed using a tailored and specifically trained score-based diffusion model, compared to a variational network and different compressed sensing approaches. The techniques were assessed using an expert reader study, scalar metric calculations, difference images against a segmented reference, and Bland-Altman analysis of cardiac functional parameters. In participants with irregular RR-cycles, spiral real-time acquisitions showed superior image quality compared to the clinical reference. Quantitative and qualitative metrics indicate enhanced image quality of the diffusion model in comparison to the alternative reconstruction methods, although improvements over the variational network were minor. Slightly higher ejection fractions for the real-time diffusion reconstructions were exhibited relative to the clinical references with a bias of 1.1 ± 5.7% for healthy subjects. The proposed real-time technique enables free-breathing acquisitions of spatio-temporal images with high quality, covering the entire heart in less than 1 min. Evaluation of ejection fraction using the ECG-gated reference can be vulnerable to arrhythmia and averaging effects, highlighting the need for real-time approaches. Prolonged inference times and stochastic variability of the diffusion reconstruction represent obstacles to overcome for clinical translation.

As cardiovascular disease (CVD) is the leading cause of noncancer mortality in colorectal or gastric cancer patients, it is essential to identify patients at increased CVD risk. Coronary artery calcium (CAC) is an established predictor of atherosclerotic CVD; however, its application is limited in this population. This study evaluates the association between automated CAC scoring using chest computed tomography and atherosclerotic CVD risk in colorectal or gastric cancer patients. A retrospective cohort study was conducted using electronic health records linked to claims data of colorectal or gastric cancer patients who underwent non-ECG-gated chest computed tomography at 2 tertiary hospitals in South Korea between 2011 and 2019. CAC was automatically quantified using deep learning software and used to classify patients into 4 groups (CAC=0, 0<CAC≤100, 100<CAC≤400, CAC>400). The primary outcome was major adverse cardiovascular events (myocardial infarction, stroke, or cardiovascular mortality), and assessed using the multivariable Fine and Gray subdistribution hazard model. A meta-analysis was performed to calculate pooled subdistribution hazard ratios. A total of 3153 patients were included in this study (36.5% female; 36.3% CAC=0; 38.1% 0<CAC≤100; 14.1% 100<CAC≤400; 11.5% CAC>400). The mean follow-up period was 4.1 years. The incidence rate of MACE was 5.28, 8.03, 9.99, and 29.14 per 1000 person-years in CAC=0, 0<CAC≤100, 100<CAC≤400, and CAC>400. Compared with CAC=0, the risk of MACE was not significantly different in patients with 0<CAC≤100 (subdistribution hazard ratio, 1.43 [95% CI, 0.41-5.01]), and 100<CAC≤400 (subdistribution hazard ratio, 0.99 [95% CI, 0.48-2.04]). Patients with CAC>400 had 2.33 (95% CI, 1.24-4.39) times higher risk of MACE compared with those with CAC=0. CAC>400 was associated with an increased risk of MACE compared with CAC=0 among colorectal or gastric cancer patients. CAC quantified on routine chest computed tomography scans provides prognostic information for atherosclerotic CVD risk in this population.

Accurate segmentation of vascular structures in coronary angiography remains a core challenge in medical image analysis due to the complexity of elongated, thin, and low-contrast vessels. Classical convolutional neural networks (CNNs) often fail to preserve topological continuity, while recent Vision Transformer (ViT)-based models, although strong in global context modeling, lack precise boundary awareness. In this work, we introduce BAVT, a Boundary-Aware Vision Transformer, a ViT-based architecture enhanced with an edge-aware loss that explicitly guides the segmentation toward fine-grained vascular boundaries. Unlike hybrid transformer-CNN models, BAVT retains a minimal, scalable structure that is fully compatible with large-scale vision foundation model (VFM) pretraining. We validate our approach on the DCA-1 coronary angiography dataset, where BAVT achieves superior performance across medical image segmentation metrics outperforming both CNN and hybrid baselines. These results demonstrate the effectiveness of combining plain ViT encoders with boundary-aware supervision for clinical-grade vascular segmentation.

Pulmonary arterial hypertension (PAH) significantly affects the pulmonary vasculature, requiring accurate estimation of mean pulmonary arterial pressure (mPAP) and pulmonary vascular resistance index (PVRi). Although cardiac catheterization is the gold standard for these measurements, it poses risks, especially in children. This pilot study explored how machine learning (ML) can predict pulmonary hemodynamics from non-invasive cardiac magnetic resonance (CMR) cine images in pediatric PAH patients. A retrospective analysis of 40 CMR studies from children with PAH using a four-fold stratified group cross-validation was conducted. The endpoints were severity profiles of mPAP and PVRi, categorised as 'low', 'high', and 'extreme'. Deep learning (DL) and traditional ML models were optimized through hyperparameter tuning. Receiver operating characteristic curves and area under the curve (AUC) were used as the primary evaluation metrics. DL models utilizing CMR cine imaging showed the best potential for predicting mPAP and PVRi severity profiles on test folds (AUC_mPAP=0.82 and AUC_PVRi=0.73). True positive rates (TPR) for predicting low, high, and extreme mPAP were 5/10, 11/16, and 11/14, respectively. TPR for predicting low, high, and extreme PVRi were 5/13, 14/15, and 7/12, respectively. Optimal DL models only used spatial patterns from consecutive CMR cine frames to maximize prediction performance. This exploratory pilot study demonstrates the potential of DL leveraging CMR imaging for non-invasive prediction of mPAP and PVRi in pediatric PAH. While preliminary, these findings may lay the groundwork for future advancements in CMR imaging in pediatric PAH, offering a pathway to safer disease monitoring and reduced reliance on invasive cardiac catheterization.