The right atrium (RA) is critical for cardiac hemodynamics but is often overlooked in clinical diagnostics. This study presents a benchmark framework for RA cavity segmentation from late gadolinium-enhanced magnetic resonance imaging (LGE-MRIs), leveraging a two-stage strategy and a novel 3D deep learning network, RASnet. The architecture addresses challenges in class imbalance and anatomical variability by incorporating multi-path input, multi-scale feature fusion modules, Vision Transformers, context interaction mechanisms, and deep supervision. Evaluated on datasets comprising 354 LGE-MRIs, RASnet achieves SOTA performance with a Dice score of 92.19% on a primary dataset and demonstrates robust generalizability on an independent dataset. The proposed framework establishes a benchmark for RA cavity segmentation, enabling accurate and efficient analysis for cardiac imaging applications. Open-source code (https://github.com/zjinw/RAS) and data (https://zenodo.org/records/15524472) are provided to facilitate further research and clinical adoption.

Accurate analysis of cardiac motion is crucial for evaluating cardiac function. While dynamic cardiac magnetic resonance imaging (CMR) can capture detailed tissue motion throughout the cardiac cycle, the fine-grained 4D cardiac motion tracking remains challenging due to the homogeneous nature of myocardial tissue and the lack of distinctive features. Existing approaches can be broadly categorized into image based and representation-based, each with its limitations. Image-based methods, including both raditional and deep learning-based registration approaches, either struggle with topological consistency or rely heavily on extensive training data. Representation-based methods, while promising, often suffer from loss of image-level details. To address these limitations, we propose Dynamic 3D Gaussian Representation (Dyna3DGR), a novel framework that combines explicit 3D Gaussian representation with implicit neural motion field modeling. Our method simultaneously optimizes cardiac structure and motion in a self-supervised manner, eliminating the need for extensive training data or point-to-point correspondences. Through differentiable volumetric rendering, Dyna3DGR efficiently bridges continuous motion representation with image-space alignment while preserving both topological and temporal consistency. Comprehensive evaluations on the ACDC dataset demonstrate that our approach surpasses state-of-the-art deep learning-based diffeomorphic registration methods in tracking accuracy. The code will be available in https://github.com/windrise/Dyna3DGR.

When preoperative planning for surgeries is conducted on the basis of medical images, artificial intelligence methods can support medical doctors during assessment. In this work, we consider medical guidelines for preoperative planning of the transcatheter aortic valve replacement (TAVR) and identify tasks, that may be supported via semantic segmentation models by making relevant anatomical structures measurable in computed tomography scans. We first derive fine-grained TAVR-relevant pseudo-labels from coarse-grained anatomical information, in order to train segmentation models and quantify how well they are able to find these structures in the scans. Furthermore, we propose an adaptation to the loss function in training these segmentation models and through this achieve a +1.27% Dice increase in performance. Our fine-grained TAVR-relevant pseudo-labels and the computed tomography scans we build upon are available at https://doi.org/10.5281/zenodo.16274176.

Cardiac ultrasound exams provide real-time data to guide clinical decisions but require highly trained sonographers. Artificial intelligence (AI) that uses deep learning algorithms to guide novices in the acquisition of diagnostic echocardiographic studies may broaden access and improve care. The objective of this trial was to evaluate whether nurses without previous ultrasound experience (novices) could obtain diagnostic-quality acquisitions of 10 echocardiographic views using AI-based software. This noninferiority study was prospective, international, nonrandomized, and conducted at 2 medical centers, in the United States and France, from November 2023 to August 2024. Two limited cardiac exams were performed on adult patients scheduled for a clinically indicated echocardiogram; one was conducted by a novice using AI guidance and one by an expert (experienced sonographer or cardiologist) without it. Primary endpoints were evaluated by 5 experienced cardiologists to assess whether the novice exam was of sufficient quality to visually analyze the left ventricular size and function, the right ventricle size, and the presence of nontrivial pericardial effusion. Secondary endpoints included 8 additional cardiac parameters. A total of 240 patients (mean age 62.6 years; 117 women (48.8%); mean body mass index 26.6 kg/m²) completed the study. One hundred percent of the exams performed by novices with the studied software were of sufficient quality to assess the primary endpoints. Cardiac parameters assessed in exams conducted by novices and experts were strongly correlated. AI-based software provides a safe means for novices to perform diagnostic-quality cardiac ultrasounds after a short training period.

Myocardial strain plays a crucial role in diagnosing heart failure and myocardial infarction. Its computation relies on assessing heart muscle motion throughout the cardiac cycle. This assessment can be performed by following key points on each frame of a cine Magnetic Resonance Imaging (MRI) sequence. The use of segmentation labels yields more accurate motion estimation near heart muscle boundaries. However, since few frames in a cardiac sequence usually have segmentation labels, most methods either rely on annotated pairs of frames/volumes, greatly reducing available data, or use all frames of the cardiac cycle without segmentation supervision. Moreover, these techniques rarely utilize more than two phases during training. In this work, a new semi-supervised motion estimation algorithm using all frames of the cardiac sequence is presented. The distance map generated from the end-diastolic segmentation label is used to weight loss functions. The method is tested on an in-house dataset containing 271 patients. Several deep learning image registration and tracking algorithms were retrained on our dataset and compared to our approach. The proposed approach achieves an average End Point Error (EPE) of 1.02mm, against 1.19mm for RAFT (Recurrent All-Pairs Field Transforms). Using the end-diastolic distance map further improves this metric to 0.95mm compared to 0.91 for the fully supervised version. Correlations in systolic peak were 0.83 and 0.90 for the left ventricular global radial and circumferential strain respectively, and 0.91 for the right ventricular circumferential strain.

Cardiovascular illnesses continue to be a predominant cause of mortality globally, underscoring the necessity for prompt and precise diagnosis to mitigate consequences and healthcare expenditures. This work presents a complete hybrid methodology that integrates machine learning techniques with medical image analysis to improve the identification of cardiovascular diseases. This research integrates many imaging modalities such as echocardiography, cardiac MRI, and chest radiographs with patient health records, enhancing diagnosis accuracy beyond standard techniques that depend exclusively on numerical clinical data. During the preprocessing phase, essential visual elements are collected from medical pictures utilizing image processing methods and convolutional neural networks (CNNs). These are subsequently integrated with clinical characteristics and input into various machine learning classifiers, including Support Vector Machines (SVM), Random Forest (RF), XGBoost, and Deep Neural Networks (DNNs), to differentiate between healthy persons and patients with cardiovascular illnesses. The proposed method attained a remarkable diagnostic accuracy of up to 96%, exceeding models reliant exclusively on clinical data. This study highlights the capability of integrating artificial intelligence with medical imaging to create a highly accurate and non-invasive diagnostic instrument for cardiovascular disease.

This study aimed to identify patient characteristics linked to mistaken treatments and major adverse cardiovascular events (MACE) in percutaneous coronary intervention (PCI) for coronary artery disease (CAD) using deep learning-based fractional flow reserve (DEEPVESSEL-FFR, DVFFR). A retrospective cohort of 3,840 PCI patients was analyzed using latent class analysis (LCA) based on eight factors. Mistaken treatment was defined as negative DVFFR patients undergoing revascularization or positive DVFFR patients not receiving it. MACE included all-cause mortality, rehospitalization for unstable angina, and non-fatal myocardial infarction. Patients were classified into comorbidities (Class 1), smoking-drinking (Class 2), and relatively healthy (Class 3) groups. Mistaken treatment was highest in Class 2 (15.4% vs. 6.7%, <i>P</i> < .001), while MACE was highest in Class 1 (7.0% vs. 4.8%, <i>P</i> < .001). Adjusted analyses showed increased mistaken treatment risk in Class 1 (OR 1.96; 95% CI 1.49-2.57) and Class 2 (OR 1.69; 95% CI 1.28-2.25) compared with Class 3. Class 1 also had higher MACE risk (HR 1.53; 95% CI 1.10-2.12). In conclusion, comorbidities and smoking-drinking classes had higher mistaken treatment and MACE risks compared with the relatively healthy class.

Advancements in cardiac computed tomography angiography (CCTA) have enabled the extraction of physiological data from an anatomy-based imaging modality. This review outlines the key methodologies for deriving fractional flow reserve (FFR) from CCTA, with a focus on two primary methods: 1) computational fluid dynamics-based FFR (CT-FFR) and 2) plaque-derived ischemia assessment using artificial intelligence and quantitative plaque metrics. These techniques have expanded the role of CCTA beyond anatomical assessment, allowing for concurrent evaluation of coronary physiology without the need for invasive testing. This review provides an overview of the principles, workflows, and limitations of each technique and aims to inform on the current state and future direction of non-invasive coronary physiology assessment.

Epicardial adipose tissue (EAT) is associated with cardiometabolic risk in type 2 diabetes (T2D), but its spatial distribution and structural alterations remain understudied. We aim to develop a shape-aware, AI-based method for automated segmentation and morphogeometric analysis of EAT in T2D. A total of 90 participants (45 with T2D and 45 age-, sex-matched controls) underwent cardiac 3D Dixon MRI, enrolled between 2014 and 2018 as part of the sub-study of the Swedish SCAPIS cohort. We developed EAT-Seg, a multi-modal deep learning model incorporating signed distance maps (SDMs) for shape-aware segmentation. Segmentation performance was evaluated using the Dice similarity coefficient (DSC), the 95% Hausdorff distance (HD95), and the average symmetric surface distance (ASSD). Statistical shape analysis combined with partial least squares discriminant analysis (PLS-DA) was applied to point cloud representations of EAT to capture latent spatial variations between groups. Morphogeometric features, including volume, 3D local thickness map, elongation and fragmentation index, were extracted and correlated with PLS-DA latent variables using Pearson correlation. Features with high-correlation were identified as key differentiators and evaluated using a Random Forest classifier. EAT-Seg achieved a DSC of 0.881, a HD95 of 3.213 mm, and an ASSD of 0.602 mm. Statistical shape analysis revealed spatial distribution differences in EAT between T2D and control groups. Morphogeometric feature analysis identified volume and thickness gradient-related features as key discriminators (r > 0.8, P < 0.05). Random Forest classification achieved an AUC of 0.703. This AI-based framework enables accurate segmentation for structurally complex EAT and reveals key morphogeometric differences associated with T2D, supporting its potential as a biomarker for cardiometabolic risk assessment.

Previous studies have confirmed that fully automated three-dimensional echocardiography (3DE) right ventricular (RV) quantification software can accurately assess adult RV function. However, data on its accuracy in children are scarce. This study aimed to test the accuracy of the software in children using cardiac magnetic resonance (MR) as the gold standard. This study prospectively enrolled 82 children who underwent both echocardiography and cardiac MR within 24 h. The RV end-diastolic volume (EDV), end-systolic volume (ESV), and ejection fraction (EF) were obtained using the novel 3DE-RV quantification software and compared with cardiac MR values across different groups. The novel 3DE-RV quantification software was feasible in all 82 children (100%). Fully automated analysis was achieved in 35% patients with an analysis time of 8 ± 2 s and 100% reproducibility. Manual editing was necessary in the remaining 65% patients. The 3DE-derived RV volumes and EF correlated well with cardiac MR measurements (RVEDV, r=0.93; RVESV, r=0.90; RVEF, r=0.82; all P <0.001). Although the automated approach slightly underestimated RV volumes and overestimated RVEF compared with cardiac MR in the entire cohort, the bias was smaller in children with RVEF ≥ 45%, normal RV size, and good 3DE image quality. Fully automated 3DE-RV quantification software provided accurate and completely reproducible results in 35% children without any adjustment. The RV volumes and EF measured using the automated 3DE method correlated well with those from cardiac MR, especially in children with RVEF ≥ 45%, normal RV size, and good 3DE image quality. Therefore, the novel automated 3DE method may achieve rapid and accurate assessment of RV function in children with normal heart anatomy.