IntroductionCardiac amyloidosis (CA) is an underdiagnosed infiltrative cardiomyopathy associated with poor outcomes if not detected early. Artificial intelligence (AI) has emerged as a promising adjunct to conventional diagnostics, leveraging imaging and non-imaging data to improve recognition of CA. However, evidence on the comparative diagnostic performance of AI across modalities remains fragmented. This meta-analysis aimed to synthesize and quantify the diagnostic performance of AI models in CA across multiple modalities. MethodsA systematic literature search was conducted in PubMed, Embase, Web of Science, and Cochrane Library from inception to August 2025. Only published observational studies applying AI to the diagnosis of CA were included. Data were extracted on patient demographics, AI algorithms, modalities, and diagnostic performance metrics. Risk of bias was assessed using QUADAS-2, and certainty of evidence was graded using GRADE. Random-effects meta-analysis (REML) was performed to pool accuracy, precision, recall, F1-score, and area under the curve (AUC). ResultsFrom 115 screened studies, 25 observational studies met the inclusion criteria, encompassing a total of 589,877 patients with a male predominance (372,458 males, 63.2%; 221,818 females, 36.6%). A wide range of AI algorithms were applied, most notably convolutional neural networks (CNNs), which accounted for 526,879 patients, followed by 3D-ResNet architectures (56,872 patients), hybrid segmentation-classification networks (3,747), and smaller studies employing random forests (636), Res-CRNN (89), and traditional machine learning approaches (769). Data modalities included ECG (341,989 patients), echocardiography (>70,000 patients across multiple cohorts), scintigraphy ([~]24,000 patients), cardiac MRI ([~]900 patients), CT (299 patients), and blood tests (261 patients). Pooled diagnostic performance across all modalities demonstrated an overall accuracy of 84.0% (95% CI: 74.6-93.5), precision of 85.8% (95% CI: 79.6-92.0), recall (sensitivity) of 89.6% (95% CI: 85.7-93.4), and an F1-score of 87.2% (95% CI: 81.8-92.6). Area under the curve (AUC) analysis revealed modality-specific variation, with scintigraphy achieving the highest pooled AUC (99.7%), followed by MRI (96.8%), echocardiography (94.3%), blood tests (95.0%), CT (98.0%), and ECG (88.5%). Subgroup analysis confirmed significant differences between modalities (p < 0.001), with MRI and scintigraphy showing consistent high performance and low-to-moderate heterogeneity, while echocardiography displayed moderate accuracy but marked variability, and ECG demonstrated the lowest and most heterogeneous results. ConclusionAI demonstrates strong potential for improving CA diagnosis, with MRI and scintigraphy providing the most reliable performance, echocardiography offering an accessible but heterogeneous option, and ECG models remaining least consistent. While promising, future prospective multicenter studies are needed to validate AI models, improve subtype discrimination, and optimize multimodal integration for real-world clinical use.

Stereotactic Arrhythmia Radioablation (STAR) is a promising treatment for refractory ventricular tachycardia. However, its precision may be hampered by cardiac and respiratory motions. Multiple techniques exist to mitigate the effects of these displacements. The purpose of this work was, based on cardiac and respiratory dynamic CT scans, to generate a patient-specific dynamic model of the structures of interest, that enables simulation of treatments for evaluation of motion management methods. Deep learning-based segmentation was used to extract the geometry of the cardiac structures, whose deformations and displacements were assessed using deformable and rigid image registrations. The combination of the model with dose maps enabled to evaluate the dose locally accumulated during the treatment. The reproducibility of each step was evaluated considering expert references, and treatment simulations were evaluated using data of a physical phantom. The exploitation of the model was illustrated on the data of nine patients, demonstrating that the impact of cardiorespiratory dynamics is potentially important and highly patient-specific, and allowing for future evaluations of motion management methods.

Image registration is a fundamental task in medical image analysis. Deformations are often closely related to the morphological characteristics of tissues, making accurate feature extraction crucial. Recent weakly supervised methods improve registration by incorporating anatomical priors such as segmentation masks or landmarks, either as inputs or in the loss function. However, such weak labels are often not readily available, limiting their practical use. Motivated by the strong representation learning ability of visual foundation models, this paper introduces SAMIR, an efficient medical image registration framework that utilizes the Segment Anything Model (SAM) to enhance feature extraction. SAM is pretrained on large-scale natural image datasets and can learn robust, general-purpose visual representations. Rather than using raw input images, we design a task-specific adaptation pipeline using SAM's image encoder to extract structure-aware feature embeddings, enabling more accurate modeling of anatomical consistency and deformation patterns. We further design a lightweight 3D head to refine features within the embedding space, adapting to local deformations in medical images. Additionally, we introduce a Hierarchical Feature Consistency Loss to guide coarse-to-fine feature matching and improve anatomical alignment. Extensive experiments demonstrate that SAMIR significantly outperforms state-of-the-art methods on benchmark datasets for both intra-subject cardiac image registration and inter-subject abdomen CT image registration, achieving performance improvements of 2.68% on ACDC and 6.44% on the abdomen dataset. The source code will be publicly available on GitHub following the acceptance of this paper.

The adoption of left ventricular global longitudinal strain (LVGLS) is still restricted by variability among various vendors and observers, despite advancements from tissue Doppler to speckle tracking imaging, machine learning, and, more recently, convolutional neural network (CNN)-based segmentation strain analysis. While CNNs have enabled fully automated strain measurement, they are inherently constrained by restricted receptive fields and a lack of temporal consistency. Transformer-based networks have emerged as a powerful alternative in medical imaging, offering enhanced global attention. Among these, the Video Swin Transformer (V-SwinT) architecture, with its 3D-shifted windows and locality inductive bias, is particularly well suited for ultrasound imaging, providing temporal consistency while optimizing computational efficiency. In this study, we propose the DTHR-SegStrain model based on a V-SwinT backbone. This model incorporates contour regression and utilizes an FCN-style multiscale feature fusion. As a result, it can generate accurate and temporally consistent left ventricle (LV) contours, allowing for direct calculation of myocardial strain without the need for conversion from segmentation to contours or any additional postprocessing. Compared to EchoNet-dynamic and Unity-GLS, DTHR-SegStrain showed greater efficiency, reliability, and validity in LVGLS measurements. Furthermore, the hybridization experiments assessed the interaction between segmentation models and strain algorithms, reinforcing that consistent segmentation contours over time can simplify strain calculations and decrease measurement variability. These findings emphasize the potential of V-SwinT-based frameworks to enhance the standardization and clinical applicability of LVGLS assessments.

Cardiovascular diseases (CVDs) continue to be the topmost cause of the worldwide morbidity and mortality. Risk factors such as diabetes, hypertension, obesity and smoking are significantly worsening the situation. The COVID-19 pandemic has powerfully highlighted the undeniable connection between viral infections and cardiovascular health. Current literature highlights that SARS-CoV-2 contributes to myocardial injury, endothelial dysfunction, thrombosis, and systemic inflammation, increasing the severity of CVD outcomes. Long COVID has also been associated with persistent cardiovascular complications, including myocarditis, arrhythmias, thromboembolic events, and accelerated atherosclerosis. Addressing these challenges requires continued research and public health strategies to mitigate long-term risks. Artificial intelligence (AI) is changing cardiovascular medicine and community health through progressive machine learning (ML) and deep learning (DL) applications. AI enhances risk prediction, facilitates biomarker discovery, and improves imaging techniques such as echocardiography, CT, and MRI for detecting coronary artery disease and myocardial injury on time. Remote monitoring and wearable devices powered by AI enable real-time cardiovascular assessment and personalized treatment. In public health, AI optimizes disease surveillance, epidemiological modeling, and healthcare resource allocation. AI-driven clinical decision support systems improve diagnostic accuracy and health equity by enabling targeted interventions. The integration of AI into cardiovascular medicine and public health offers data-driven, efficient, and patient-centered solutions to mitigate post-COVID cardiovascular complications.

The aorta is the body's largest arterial vessel, serving as the primary pathway for oxygenated blood within the systemic circulation. Aortic aneurysms consistently rank among the top twenty causes of mortality in the United States. Thoracic aortic aneurysm (TAA) arises from abnormal dilation of the thoracic aorta and remains a clinically significant disease, ranking as one of the leading causes of death in adults. A thoracic aortic aneurysm ruptures when the integrity of all aortic wall layers is compromised due to elevated blood pressure. Currently, three-dimensional computed tomography (3D CT) is considered the gold standard for diagnosing TAA. The geometric characteristics of the aorta, which can be quantified from medical imaging, and stresses on the aortic wall, which can be obtained by finite element analysis (FEA), are critical in evaluating the risk of rupture and dissection. Deep learning based image segmentation has emerged as a reliable method for extracting anatomical regions of interest from medical images. Voxel based segmentation masks of anatomical structures are typically converted into structured mesh representation to enable accurate simulation. Hexahedral meshes are commonly used in finite element simulations of the aorta due to their computational efficiency and superior simulation accuracy. Due to anatomical variability, patient specific modeling enables detailed assessment of individual anatomical and biomechanics behaviors, supporting precise simulations, accurate diagnoses, and personalized treatment strategies. Finite element (FE) simulations provide valuable insights into the biomechanical behaviors of tissues and organs in clinical studies. Developing accurate FE models represents a crucial initial step in establishing a patient-specific, biomechanically based framework for predicting the risk of TAA.

Introduction Heart failure (HF) is a life-threatening syndrome with significant morbidity and mortality. While evidence-based drug treatments have effectively reduced morbidity and mortality in HF with reduced ejection fraction (HFrEF), few therapies have been demonstrated to improve outcomes in HF with preserved ejection fraction (HFpEF). The multifaceted clinical presentation is one of the main reasons why the current understanding of HFpEF remains limited. This may be caused by the existence of several HFpEF disease subtypes that each need different treatments. There is therefore an unmet need for a holistic approach that combines comprehensive imaging with metabolomic, transcriptomic and genomic mapping to subtype HFpEF patients. This protocol details the approach employed in the HeartMagic study to address this gap in understanding. Methods This prospective multi-center observational cohort study will include 500 consecutive patients with actual or recent hospitalization for treatment of HFpEF at two Swiss university hospitals, along with 50 age-matched HFrEF patients and 50 age-matched healthy controls. Diagnosis of heart failure is based on clinical signs and symptoms and subgrouping HF patients is based on the left-ventricular ejection fraction. In addition to routine clinical workup, participants undergo genomic, transcriptomic, and metabolomic analyses, while the anatomy, composition, and function of the heart are quantified by comprehensive echocardiography and magnetic resonance imaging (MRI). Quantitative MRI is also applied to characterize the kidney. The primary outcome is a composite of one-year cardiovascular mortality or rehospitalization. Machine learning (ML) based multi-modal clustering will be employed to identify distinct HFpEF subtypes in the holistic data. The clinical importance of these subtypes shall be evaluated based on their association with the primary outcome. Statistical analysis will include group comparisons across modalities, survival analysis for the primary outcome, and integrative multi-modal clustering combining clinical, imaging, ECG, genomic, transcriptomic, and metabolomic data to identify and validate HFpEF subtypes. Discussion The integration of comprehensive MRI with extensive genomic and metabolomic profiling in this study will result in an unprecedented panoramic view of HFpEF and should enable us to distinguish functional subgroups of HFpEF patients. This approach has the potential to provide unprecedented insights on HFpEF disease and should provide a basis for personalized therapies. Beyond this, identifying HFpEF subtypes with specific molecular and structural characteristics could lead to new targeted pharmacological interventions, with the potential to improve patient outcomes.

Fontan palliation for univentricular congenital heart disease progresses to hemodynamic failure with complex flow patterns poorly characterized by conventional 2D imaging. Current assessment relies on fluoroscopic angiography, providing limited 3D geometric information essential for computational fluid dynamics (CFD) analysis and surgical planning. A multi-step AI pipeline was developed utilizing Google's Gemini 2.5 Flash (2.5B parameters) for systematic, iterative processing of fluoroscopic angiograms through transformer-based neural architecture. The pipeline encompasses medical image preprocessing, vascular segmentation, contrast enhancement, artifact removal, and virtual hemodynamic flow visualization within 2D projections. Final views were processed through Tencent's Hunyuan3D-2mini (384M parameters) for stereolithography file generation. The pipeline successfully generated geometrically optimized 2D projections from single-view angiograms after 16 processing steps using a custom web interface. Initial iterations contained hallucinated vascular features requiring iterative refinement to achieve anatomically faithful representations. Final projections demonstrated accurate preservation of complex Fontan geometry with enhanced contrast suitable for 3D conversion. AI-generated virtual flow visualization identified stagnation zones in central connections and flow patterns in branch arteries. Complete processing required under 15 minutes with second-level API response times. This approach demonstrates clinical feasibility of generating CFD-suitable geometries from routine angiographic data, enabling 3D generation and rapid virtual flow visualization for cursory insights prior to full CFD simulation. While requiring refinement cycles for accuracy, this establishes foundation for democratizing advanced geometric and hemodynamic analysis using readily available imaging data.

Congenital heart diseases (CHD) are a significant cause of neonatal mortality and morbidity. Detecting these abnormalities during pregnancy increases survival rates, enhances prognosis, and improves pregnancy management and quality of life for the affected families. Foetal echocardiography can be considered an accurate method for detecting CHDs. However, the detection of CHDs can be limited by factors such as the sonographer's skill, expertise and patient specific variables. Using artificial intelligence (AI) has the potential to address these challenges, increasing antenatal CHD detection during prenatal care. A scoping review was conducted using Google Scholar, PubMed, and ScienceDirect databases, employing keywords, Boolean operators, and inclusion and exclusion criteria to identify peer-reviewed studies. Thematic mapping and synthesis of the found literature were conducted to review key concepts, research methods and findings. A total of n = 233 articles were identified, after exclusion criteria, the focus was narrowed to n = 7 that met the inclusion criteria. Themes in the literature identified the potential of AI to assist clinicians and trainees, alongside emerging new ethical limitations in ultrasound imaging. AI-based tools in ultrasound imaging offer great potential in assisting sonographers and doctors with decision-making in CHD diagnosis. However, due to the paucity of data and small sample sizes, further research and technological advancements are needed to improve reliability and integrate AI into routine clinical practice. This scoping review identified the reported accuracy and limitations of AI-based tools within foetal cardiac ultrasound imaging. AI has the potential to aid in reducing missed diagnoses, enhance training, and improve pregnancy management. There is a need to understand and address the ethical and legal considerations involved with this new paradigm in imaging.

Stress perfusion cardiovascular magnetic resonance (CMR) is widely used to detect myocardial ischemia, mostly through visual assessment. Recent studies suggest that strain imaging at rest and during stress can also help in prognostic stratification. However, the additional prognostic value of combining both rest and stress strain imaging has not been fully established. This study examined the incremental benefit of combining these strain measures with traditional risk prognosticators and CMR findings to predict major adverse clinical events (MACE) in a cohort of consecutive patients referred for stress CMR. This retrospective, single-center observational study included all consecutive patients with known or suspected coronary artery disease referred for stress CMR between 2016 and 2018. Fully automated machine learning was used to obtain global longitudinal strain at rest (rest-GLS) and global circumferential strain at stress (stress-GCS). The primary outcome was MACE, including cardiovascular death or hospitalization for heart failure. Cox models were used to assess the incremental prognostic value of combining these strain features with traditional prognosticators. Of 2778 patients (age 65±12 years, 68% male), 96% had feasible, fully automated rest-GLS and stress-GCS measurements. After a median follow-up of 5.2 (4.8-5.5) years, 316 (11.1%) patients experienced MACE. After adjustment for traditional prognosticators, both rest-GLS (hazard ratio, 1.09 [95% CI, 1.05-1.13]; <i>P</i><0.001) and stress-GCS (hazard ratio, 1.08 [95% CI, 1.03-1.12]; <i>P</i><0.001) were independently associated with MACE. The best cutoffs for MACE prediction were >-10% for rest-GLS and stress-GCS, with a C-index improvement of 0.02, continuous net reclassification improvement of 15.6%, and integrative discrimination index of 2.2% (all <i>P</i><0.001). The combination of rest-GLS and stress-GCS, with a cutoff of >-10% provided an incremental prognostic value over and above traditional prognosticators, including CMR parameters, for predicting MACE in patients undergoing stress CMR.