Coronary artery calcium (CAC) scoring refines atherosclerotic cardiovascular disease (ASCVD) risk but is not frequently reported on routine non‑gated chest CT (NCCT), whose use expanded in the COVID‑19 era. We sought to develop and validate a workflow-ready deep learning model for fully automated, protocol-agnostic CAC quantification. In this retrospective study, a deep learning (DL) model was trained and validated using 2132 chest CT scans (routine, CT-CAC, and CT-COVID) from patients without established atherosclerotic cardiovascular disease (ASCVD) collected (2013-2023) at a single university hospital. The index test was a DL-based CAC segmentation model; the reference standard was manual annotation by experienced observers. Agreement was evaluated using intra-class correlation coefficients (ICC) for Agatston scores and Cohen's kappa for CAC risk categories. Sensitivity, specificity, positive, and negative predictive values, and F1 scores were calculated to measure diagnostic performance. The DL model demonstrated high reliability for Agatston scores (ICC=0.987) and strong agreement in CAC categories (Cohen's κ=0.86-0.95). Diagnostic performance for CAC >100 (F1=0.956) and CAC >300 (F1=0.967) was very high. External validation in the Mashhad COVID Study showed good agreement (κ=0.8). In the SBU COVID study, the F1 score for detecting moderate-to-severe CAC was 0.928. The proposed DL model delivers accurate, workflow‑ready CAC quantification across routine, dedicated, and pandemic‑era chest CT scans, supporting opportunistic, cost‑effective cardiovascular risk stratification in contemporary clinical practice.

Pulmonary hypertension (PH) is a progressive disorder characterized by elevated pulmonary artery pressure and increased pulmonary vascular resistance, ultimately leading to right heart failure. Early detection is critical for improving patient outcomes. The diagnosis of PH primarily relies on right heart catheterization, but its invasive nature significantly limits its clinical use. Echocardiography, as the most common noninvasive screening and diagnostic tool for PH, provides valuable patient information. This study aims to identify key PH predictors from echocardiographic parameters, laboratory tests, and demographic data using machine learning, ultimately constructing a predictive model to support early noninvasive diagnosis of PH. This study compiled comprehensive datasets comprising echocardiography measurements, clinical laboratory data, and fundamental demographic information from patients with PH and matched controls. The final analytical cohort consisted of 895 participants with 85 evaluated variables. Recursive feature elimination was used to select the most relevant echocardiographic variables, which were subsequently integrated into a composite ultrasound index using machine learning techniques, XGBoost (Extreme Gradient Boosting). LASSO (least absolute shrinkage and selection operator) regression was applied to select the potential predictive variable from laboratory tests. Then, the ultrasound index variables and selected laboratory tests were combined to construct a logistic regression model for the predictive diagnosis of PH. The model's performance was rigorously evaluated using receiver operating characteristic curves, calibration plots, and decision curve analysis to ensure its clinical relevance and accuracy. Both internal and external validation were used to assess the performance of the constructed model. A total of 16 echocardiographic parameters (right atrium diameter, pulmonary artery diameter, left atrium diameter, tricuspid valve reflux degree, right ventricular diameter, E/E' [ratio of mitral valve early diastolic inflow velocity (E) to mitral annulus early diastolic velocity (E')], interventricular septal thickness, left ventricular diameter, ascending aortic diameter, left ventricular ejection fraction, left ventricular outflow tract velocity, mitral valve reflux degree, pulmonary valve outflow velocity, mitral valve inflow velocity, aortic valve reflux degree, and left ventricular posterior wall thickness) combined with 2 laboratory biomarkers (prothrombin time activity and cystatin C) were identified as optimal predictors, forming a high-performance PH prediction model. The diagnostic model demonstrated high predictive accuracy, with an area under the receiver operating characteristic curve of 0.997 in the internal validation and 0.974 in the external validation. Both calibration plots and decision curve analysis validated the model's predictive accuracy and clinical applicability, with optimal performance observed at higher risk stratification cutoffs. This model enhances early PH diagnosis through a noninvasive approach and demonstrates strong predictive accuracy. It facilitates early intervention and personalized treatment, with potential applications in broader cardiovascular disease management.

To assess whether few-shot prompting improves the performance of 2 popular large language models (LLMs) (ChatGPT o1 and DeepSeek-R1) in assigning Coronary Artery Disease Reporting and Data System (CAD-RADS™ 2.0) categories. A detailed few-shot prompt based on CAD-RADS™ 2.0 framework was developed using 20 reports from the MIMIC-IV database. Subsequently, 100 modified reports from the same database were categorized using zero-shot and few-shot prompts through the models' user interface. Model accuracy was evaluated by comparing assignments to a reference radiologist's classifications, including stenosis categories and modifiers. To assess reproducibility, 50 reports were reclassified using the same few-shot prompt. McNemar tests and Cohen kappa were used for statistical analysis. Using zero-shot prompting, accuracy was low for both models (ChatGPT: 14%, DeepSeek: 8%), with correct assignments occurring almost exclusively in CAD-RADS 0 cases. Hallucinations occurred frequently (ChatGPT: 19%, DeepSeek: 54%). Few-shot prompting significantly improved accuracy to 98% for ChatGPT and 93% for DeepSeek (both P<0.001) and eliminated hallucinations. Kappa values for agreement between model-generated and radiologist-assigned classifications were 0.979 (0.950, 1.000) (P<0.001) for ChatGPT and 0.916 (0.859, 0.973) (P<0.001) for DeepSeek, indicating almost perfect agreement for both models without a significant difference between the models (P=0.180). Reproducibility analysis yielded kappa values of 0.957 (0.900, 1.000) (P<0.001) for ChatGPT and 0.873 [0.779, 0.967] (P<0.001) for DeepSeek, indicating almost perfect and strong agreement between repeated assignments, respectively, with no significant difference between the models (P=0.125). Few-shot prompting substantially enhances LLMs' accuracy in assigning CAD-RADS™ 2.0 categories, suggesting potential for clinical application and facilitating system adoption.

Cardiovascular diseases (CVDs) remain the leading cause of mortality globally, and echocardiography is critical for diagnosis of both common and congenital cardiac conditions. However, echocardiographic data for certain pathologies are scarce, hindering the development of robust automated diagnosis models. In this work, we propose Echo-Path, a novel generative framework to produce echocardiogram videos conditioned on specific cardiac pathologies. Echo-Path can synthesize realistic ultrasound video sequences that exhibit targeted abnormalities, focusing here on atrial septal defect (ASD) and pulmonary arterial hypertension (PAH). Our approach introduces a pathology-conditioning mechanism into a state-of-the-art echo video generator, allowing the model to learn and control disease-specific structural and motion patterns in the heart. Quantitative evaluation demonstrates that the synthetic videos achieve low distribution distances, indicating high visual fidelity. Clinically, the generated echoes exhibit plausible pathology markers. Furthermore, classifiers trained on our synthetic data generalize well to real data and, when used to augment real training sets, it improves downstream diagnosis of ASD and PAH by 7\% and 8\% respectively. Code, weights and dataset are available here https://github.com/Marshall-mk/EchoPathv1

Recent genome-wide association studies (GWAS) have effectively linked genetic variants to quantitative traits derived from time-series cardiac magnetic resonance imaging, revealing insights into cardiac morphology and function. Deep learning approach generally requires extensive supervised training on manually annotated data. In this study, we developed a novel framework using a 3D U-architecture autoencoder (cineMAE) to learn deep image phenotypes from cardiac magnetic resonance (CMR) imaging for genetic discovery, focusing on long-axis two-chamber and four-chamber views. We trained a masked autoencoder to develop <b>U</b> nsupervised <b>D</b> erived <b>I</b> mage <b>P</b> henotypes for heart (Heart-UDIPs). These representations were found to be informative to indicate various heart-specific phenotypes (e.g., left ventricular hypertrophy) and diseases (e.g., hypertrophic cardiomyopathy). GWAS on Heart UDIP identified 323 lead SNP and 628 SNP-prioritized genes, which exceeded previous methods. The genes identified by method described herein, exhibited significant associations with cardiac function and showed substantial enrichment in pathways related to cardiac disorders. These results underscore the utility of our Heart-UDIP approach in enhancing the discovery potential for genetic associations, without the need for clinically defined phenotypes or manual annotations.

Current machine learning-based (ML) models usually attempt to utilize all available patient data to predict patient outcomes while ignoring the associated cost and time for data acquisition. The purpose of this study is to create a multi-stage ML model to predict cardiac resynchronization therapy (CRT) response for heart failure (HF) patients. This model exploits uncertainty quantification to recommend additional collection of single-photon emission computed tomography myocardial perfusion imaging (SPECT MPI) variables if baseline clinical variables and features from electrocardiogram (ECG) are not sufficient. Two hundred eighteen patients who underwent rest-gated SPECT MPI were enrolled in this study. CRT response was defined as an increase in left ventricular ejection fraction (LVEF) > 5% at a 6 ± 1 month follow-up. A multi-stage ML model was created by combining two ensemble models: Ensemble 1 was trained with clinical variables and ECG; Ensemble 2 included Ensemble 1 plus SPECT MPI features. Uncertainty quantification from Ensemble 1 allowed for multi-stage decision-making to determine if the acquisition of SPECT data for a patient is necessary. The performance of the multi-stage model was compared with that of Ensemble models 1 and 2. The response rate for CRT was 55.5% (n = 121) with overall male gender 61.0% (n = 133), an average age of 62.0 ± 11.8, and LVEF of 27.7 ± 11.0. The multi-stage model performed similarly to Ensemble 2 (which utilized the additional SPECT data) with AUC of 0.75 vs. 0.77, accuracy of 0.71 vs. 0.69, sensitivity of 0.70 vs. 0.72, and specificity 0.72 vs. 0.65, respectively. However, the multi-stage model only required SPECT MPI data for 52.7% of the patients across all folds. By using rule-based logic stemming from uncertainty quantification, the multi-stage model was able to reduce the need for additional SPECT MPI data acquisition without significantly sacrificing performance.

Artificial intelligence (AI)-enabled multimodal cardiovascular imaging holds significant promise for improving diagnostic accuracy, enhancing risk stratification, and supporting clinical decision-making. However, its translation into routine practice remains limited by multiple technical, infrastructural, and clinical barriers. This review synthesizes current challenges, including variability in image quality, alignment, and acquisition protocols; scarcity of large, annotated multimodality datasets; interoperability limitations across vendors and institutions; clinical skepticism due to limited prospective validation; and substantial development and implementation costs. Drawing from recent advances, we outline future research priorities to bridge the gap between technical feasibility and clinical utility. Key strategies include developing unified, vendor-agnostic AI models resilient to inter-institutional variability; integrating diverse data types such as genomics, wearable biosensors, and longitudinal clinical records; leveraging reinforcement learning for adaptive decision-support systems; and employing longitudinal imaging fusion for disease tracking and predictive analytics. We emphasize the need for rigorous prospective clinical trials, harmonized imaging standards, and collaborative data-sharing frameworks to ensure robust, equitable, and scalable deployment. Addressing these challenges through coordinated multidisciplinary efforts will be essential to realize the full potential of AI-driven multimodal cardiovascular imaging in advancing precision cardiovascular care.

Obesity is a global public health priority and a major risk factor for cardiovascular disease (CVD). Emerging evidence indicates variation in pathologic consequences of obesity deposition across different body compartments. Biological heart age may be estimated from imaging measures of cardiac structure and function and captures risk beyond traditional measures. Using cardiac and abdominal magnetic resonance imaging (MRI) from 34,496 UK Biobank participants and linked health record data, we investigated how compartment-specific obesity phenotypes relate to cardiac ageing and incident CVD risk. Biological heart age was estimated using machine learning from 56 cardiac MRI phenotypes. K-means clustering of abdominal visceral (VAT), abdominal subcutaneous (ASAT), and pericardial (PAT) adiposity identified a high-risk cluster (characterised by greater adiposity across all three depots) associated with accelerated cardiac ageing - and a lower-risk cluster linked to decelerated ageing. These clusters provided more precise stratification of cardiovascular ageing trajectories than established body mass index categories. Mediation analysis showed that VAT and PAT explained 13.7% and 11.9% of obesity-associated CVD risk, respectively, whereas ASAT contributed minimally, with effects more pronounced in males. Thus, cardiovascular risk appears to be driven primarily by visceral and pericardial rather than subcutaneous fat. Our findings reveal a distinct risk profile of compartment-specific fat distributions and show the importance of pericardial and visceral fat as drivers of greater cardiovascular ageing. Advanced image-defined adiposity profiling may enhance CVD risk prediction beyond anthropometric measures and enhance mechanistic understanding.

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.

Coronary artery disease (CAD) remains a leading cause of morbidity and mortality worldwide, with incidence rates continuing to rise. Automated coronary artery medical image segmentation can ultimately improve CAD management by enabling more advanced and efficient diagnostic assessments. Deep learning-based segmentation methods have shown significant promise and offered higher accuracy while reducing reliance on manual inputs. However, achieving consistent performance across diverse datasets remains a persistent challenge due to substantial variability in imaging protocols, equipment and patient-specific factors, such as signal intensities, anatomical differences and disease severity. This study investigates the influence of image quality and resolution, governed by vessel size and common disease characteristics that introduce artefacts, such as calcification, on coronary artery segmentation accuracy in computed tomography coronary angiography (CTCA). Two datasets were utilised for model training and validation, including the publicly available ASOCA dataset (40 cases) and a GeoCAD dataset (70 cases) with more cases of coronary disease. Coronary artery segmentations were generated using three deep learning frameworks/architectures: default U-Net, Swin-UNETR, and EfficientNet-LinkNet. The impact of various factors on model generalisation was evaluated, focusing on imaging characteristics (contrast-to-noise ratio, artery contrast enhancement, and edge sharpness) and the extent of calcification at both the coronary tree and individual vessel branch levels. The calcification ranges considered were 0 (no calcification), 1-99 (low), 100-399 (moderate), and > 400 (high). The findings demonstrated that image features, including artery contrast enhancement (r = 0.408, p < 0.001) and edge sharpness (r = 0.239, p = 0.046), were significantly correlated with improved segmentation performance in test cases. Regardless of severity, calcification had a negative impact on segmentation accuracy, with low calcification affecting the segmentation most poorly (p < 0.05). This may be because smaller calcified lesions produce less distinct contrast against the bright lumen, making it harder for the model to accurately identify and segment these lesions. Additionally, in males, a larger diameter of the first obtuse marginal branch (OM1) (p = 0.036) was associated with improved segmentation performance for OM1. Similarly, in females, larger diameters of left main (LM) coronary artery (p = 0.008) and right coronary artery (RCA) (p < 0.001) were associated with better segmentation performance for LM and RCA, respectively. These findings emphasise the importance of accounting for imaging characteristics and anatomical variability when developing generalisable deep learning models for coronary artery segmentation. Unlike previous studies, which broadly acknowledge the role of image quality in segmentation, our work quantitatively demonstrates the extent to which contrast enhancement, edge sharpness, calcification and vessel diameter impact segmentation performance, offering a data-driven foundation for model adaptation strategies. Potential improvements include optimising pre-segmentation imaging (e.g. ensuring adequate edge sharpness in low-contrast regions) and developing algorithms to address vessel-specific challenges, such as improving segmentation of low-level calcifications and accurately identifying LM, RCA and OM1 of smaller diameters.