Coronary CT Angiography (CCTA) is essential for assessing atherosclerosis and coronary artery disease, aiding in early detection, risk prediction, and clinical assessment. However, traditional CCTA interpretation is limited by observer variability, time inefficiency, and inconsistent plaque characterization. AI has emerged as a transformative tool, enhancing diagnostic accuracy, workflow efficiency, and risk prediction for major adverse cardiovascular events (MACE). Studies show that AI improves stenosis detection by 27%, inter-reader agreement by 30%, and reduces reporting times by 40%, thereby addressing key limitations of manual interpretation. Integrating AI with multimodal imaging (e.g., FFR-CT, PET-CT) further enhances ischemia detection by 28% and lesion classification by 35%, providing a more comprehensive cardiovascular evaluation. This review synthesizes recent advancements in CCTA-AI automation, risk stratification, and precision diagnostics while critically analyzing data quality, generalizability, ethics, and regulation challenges. Future directions, including real-time AI-assisted triage, cloud-based diagnostics, and AI-driven personalized medicine, are explored for their potential to revolutionize clinical workflows and optimize patient outcomes.

Automatic detection and classification of Cardiovascular disease (CVD) from Computed Tomography (CT) images play an important part in facilitating better-informed clinical decisions. However, most of the recent deep learning based methods either directly work on raw CT data or utilize it in pair with anatomical cardiac structure segmentation by training an end-to-end classifier. As such, these approaches become much more difficult to interpret from a clinical perspective. To address this challenge, in this work, we break down the CVD classification pipeline into three components: (i) image segmentation, (ii) image registration, and (iii) downstream CVD classification. Specifically, we utilize the Atlas-ISTN framework and recent segmentation foundational models to generate anatomical structure segmentation and a normative healthy atlas. These are further utilized to extract clinically interpretable radiomic features as well as deformation field based geometric features (through atlas registration) for CVD classification. Our experiments on the publicly available ASOCA dataset show that utilizing these features leads to better CVD classification accuracy (87.50\%) when compared against classification model trained directly on raw CT images (67.50\%). Our code is publicly available: https://github.com/biomedia-mira/grc-net

Automated detection and segmentation of surgical devices, such as catheters or wires, in X-ray fluoroscopic images have the potential to enhance image guidance in minimally invasive heart surgeries. In this paper, we present a convolutional neural network model that integrates a resnet architecture with multiple prediction heads to achieve real-time, accurate localization of electrodes on catheters and catheter segmentation in an end-to-end deep learning framework. We also propose a multi-task learning strategy in which our model is trained to perform both accurate electrode detection and catheter segmentation simultaneously. A key challenge with this approach is achieving optimal performance for both tasks. To address this, we introduce a novel multi-level dynamic resource prioritization method. This method dynamically adjusts sample and task weights during training to effectively prioritize more challenging tasks, where task difficulty is inversely proportional to performance and evolves throughout the training process. The proposed method has been validated on both public and private datasets for single-task catheter segmentation and multi-task catheter segmentation and detection. The performance of our method is also compared with existing state-of-the-art methods, demonstrating significant improvements, with a mean <math xmlns="http://www.w3.org/1998/Math/MathML"><mi>J</mi></math> of 64.37/63.97 and with average precision over all IoU thresholds of 84.15/83.13, respectively, for detection and segmentation multi-task on the validation and test sets of the catheter detection and segmentation dataset. Our approach achieves a good balance between accuracy and efficiency, making it well-suited for real-time surgical guidance applications.

Epicardial adipose tissue represents a metabolically active visceral fat depot that is in direct contact with the left ventricular myocardium. While it is associated with coronary artery disease, little is known regarding its role in aortic stenosis. We sought to investigate the association of epicardial adipose tissue with aortic stenosis severity and progression, myocardial remodelling and function, and mortality in asymptomatic patients with aortic stenosis. In a post hoc analysis of 124 patients with asymptomatic mild-to-severe aortic stenosis participating in a prospective clinical trial, baseline epicardial adipose tissue was quantified on CT angiography using fully automated deep learning-enabled software. Aortic stenosis disease severity was assessed at baseline and 1 year. The primary endpoint was all-cause mortality. Neither epicardial adipose tissue volume nor attenuation correlated with aortic stenosis severity or subsequent disease progression as assessed by echocardiography or CT (p>0.05 for all). Epicardial adipose tissue volume correlated with plasma cardiac troponin concentration (r=0.23, p=0.009), left ventricular mass (r=0.46, p<0.001), ejection fraction (r=-0.28, p=0.002), global longitudinal strain (r=0.28, p=0.017), and left atrial volume (r=0.39, p<0.001). During the median follow-up of 48 (IQR 26-73) months, a total of 23 (18%) patients died. In multivariable analysis, both epicardial adipose tissue volume (HR 1.82, 95% CI 1.10 to 3.03; p=0.021) and plasma cardiac troponin concentration (HR 1.47, 95% CI 1.13 to 1.90; p=0.004) were associated with all-cause mortality, after adjustment for age, body mass index and left ventricular ejection fraction. Patients with epicardial adipose tissue volume >90 mm³ had 3-4 times higher risk of death (adjusted HR 3.74, 95% CI 1.08 to 12.96; p=0.037). Epicardial adipose tissue volume does not associate with aortic stenosis severity or its progression but does correlate with blood and imaging biomarkers of impaired myocardial health. The latter may explain the association of epicardial adipose tissue volume with an increased risk of all-cause mortality in patients with asymptomatic aortic stenosis. gov (NCT02132026).

Fetal cardiovascular magnetic resonance is an emerging tool for prenatal congenital heart disease assessment, but long acquisition times and fetal movements limit its clinical use. This study evaluates the clinical utility of deep learning super-resolution reconstructions for rapidly acquired, low-resolution fetal cardiovascular magnetic resonance. This prospective study included participants with fetal congenital heart disease undergoing fetal cardiovascular magnetic resonance in the third trimester of pregnancy, with axial cine images acquired at normal resolution and low resolution. Low-resolution cine data was subsequently reconstructed using a deep learning super-resolution framework (cine_DL). Acquisition times, apparent signal-to-noise ratio, contrast-to-noise ratio, and edge rise distance were assessed. Volumetry and functional analysis were performed. Qualitative image scores were rated on a 5-point Likert scale. Cardiovascular structures and pathological findings visible in cine_DL images only were assessed. Statistical analysis included the Student paired <i>t</i> test and the Wilcoxon test. A total of 42 participants were included (median gestational age, 35.9 weeks [interquartile range (IQR), 35.1-36.4]). Cine_DL acquisition was faster than cine images acquired at normal resolution (134±9.6 s versus 252±8.8 s; <i>P</i><0.001). Quantitative image quality metrics and image quality scores for cine_DL were higher or comparable with those of cine images acquired at normal-resolution images (eg, fetal motion, 4.0 [IQR, 4.0-5.0] versus 4.0 [IQR, 3.0-4.0]; <i>P</i><0.001). Nonpatient-related artifacts (eg, backfolding) were more pronounced in Cine_DL compared with cine images acquired at normal-resolution images (4.0 [IQR, 4.0-5.0] versus 5.0 [IQR, 3.0-4.0]; <i>P</i><0.001). Volumetry and functional results were comparable. Cine_DL revealed additional structures in 10 of 42 fetuses (24%) and additional pathologies in 5 of 42 fetuses (12%), including partial anomalous pulmonary venous connection. Deep learning super-resolution reconstructions of low-resolution acquisitions shorten acquisition times and achieve diagnostic quality comparable with standard images, while being less sensitive to fetal bulk movements, leading to additional diagnostic findings. Therefore, deep learning super-resolution may improve the clinical utility of fetal cardiovascular magnetic resonance for accurate prenatal assessment of congenital heart disease.

The accurate segmentation of myocardial scars from cardiac MRI is essential for clinical assessment and treatment planning. In this study, we propose a robust deep-learning pipeline for fully automated myocardial scar detection and segmentation by fine-tuning state-of-the-art models. The method explicitly addresses challenges of label noise from semi-automatic annotations, data heterogeneity, and class imbalance through the use of Kullback-Leibler loss and extensive data augmentation. We evaluate the model's performance on both acute and chronic cases and demonstrate its ability to produce accurate and smooth segmentations despite noisy labels. In particular, our approach outperforms state-of-the-art models like nnU-Net and shows strong generalizability in an out-of-distribution test set, highlighting its robustness across various imaging conditions and clinical tasks. These results establish a reliable foundation for automated myocardial scar quantification and support the broader clinical adoption of deep learning in cardiac imaging.

Elucidating the biomechanical behavior of the myocardium is crucial for understanding cardiac physiology, but cannot be directly inferred from clinical imaging and typically requires finite element (FE) simulations. However, conventional FE methods are computationally expensive and often fail to reproduce observed cardiac motions. We propose IMC-PINN-FE, a physics-informed neural network (PINN) framework that integrates imaged motion consistency (IMC) with FE modeling for patient-specific left ventricular (LV) biomechanics. Cardiac motion is first estimated from MRI or echocardiography using either a pre-trained attention-based network or an unsupervised cyclic-regularized network, followed by extraction of motion modes. IMC-PINN-FE then rapidly estimates myocardial stiffness and active tension by fitting clinical pressure measurements, accelerating computation from hours to seconds compared to traditional inverse FE. Based on these parameters, it performs FE modeling across the cardiac cycle at 75x speedup. Through motion constraints, it matches imaged displacements more accurately, improving average Dice from 0.849 to 0.927, while preserving realistic pressure-volume behavior. IMC-PINN-FE advances previous PINN-FE models by introducing back-computation of material properties and better motion fidelity. Using motion from a single subject to reconstruct shape modes also avoids the need for large datasets and improves patient specificity. IMC-PINN-FE offers a robust and efficient approach for rapid, personalized, and image-consistent cardiac biomechanical modeling.

Aortic valve calcium scoring is an essential tool for diagnosing, treating, monitoring, and assessing the risk of aortic stenosis. The current gold standard for determining the aortic valve calcium score is computed tomography (CT). However, CT is costly and exposes patients to ionizing radiation, making it less ideal for frequent monitoring. Echocardiography, a safer and more affordable alternative that avoids radiation, is more widely accessible, but its variability between and within experts leads to subjective interpretations. Given these limitations, there is a clear need for an automated, objective method to measure the aortic valve calcium score from echocardiography, which could reduce costs and improve patient safety. In this paper, we first employ the YOLOv5 method to detect the region of interest in the aorta within echocardiography images. Building on this, we propose a novel approach that combines UNet and diffusion model architectures to segment calcified areas within the identified region, forming the foundation for automated aortic valve calcium scoring. This architecture leverages UNet's localization capabilities and the diffusion model's strengths in capturing fine-grained structures, enhancing both segmentation accuracy and consistency. The proposed method achieves 85.08% precision, 80.01% recall, and 71.13% Dice score on a novel dataset comprising 160 echocardiography images from 86 distinct patients. This system enables cardiologists to focus more on critical aspects of diagnosis by providing a faster, more objective, and cost-effective method for aortic valve calcium scoring and eliminating the risk of radiation exposure.

Artificial intelligence, including deep learning models, will play a transformative role in automated medical image analysis for the diagnosis of cardiac disorders and their management. Automated accurate delineation of cardiac images is the first necessary initial step for the quantification and automated diagnosis of cardiac disorders. In this paper, we propose a deep learning based enhanced UNet model, U-R-Veda, which integrates convolution transformations, vision transformer, residual links, channel-attention, and spatial attention, together with edge-detection based skip-connections for an accurate fully-automated semantic segmentation of cardiac magnetic resonance (CMR) images. The model extracts local-features and their interrelationships using a stack of combination convolution blocks, with embedded channel and spatial attention in the convolution block, and vision transformers. Deep embedding of channel and spatial attention in the convolution block identifies important features and their spatial localization. The combined edge information with channel and spatial attention as skip connection reduces information-loss during convolution transformations. The overall model significantly improves the semantic segmentation of CMR images necessary for improved medical image analysis. An algorithm for the dual attention module (channel and spatial attention) has been presented. Performance results show that U-R-Veda achieves an average accuracy of 95.2%, based on DSC metrics. The model outperforms the accuracy attained by other models, based on DSC and HD metrics, especially for the delineation of right-ventricle and left-ventricle-myocardium.