This study aimed to develop and validate a deep learning (DL) model to detect obstructive coronary artery disease (CAD, ≥ 50% stenosis) in coronary CT angiography (CCTA) among patients presenting to the emergency department (ED) with acute chest pain. The training dataset included 378 patients with acute chest pain who underwent CCTA (10,060 curved multiplanar reconstruction [MPR] images) from a single-center ED between January 2015 and December 2022. The external validation dataset included 298 patients from 3 ED centers between January 2021 and December 2022. A DL model based on You Only Look Once v4, requires manual preprocessing for curved MPR extraction and was developed using 15 manually preprocessed MPR images per major coronary artery. Model performance was evaluated per artery and per patient. The training dataset included 378 patients (mean age 61.3 ± 12.2 years, 58.2% men); the external dataset included 298 patients (mean age 58.3 ± 13.8 years, 54.6% men). Obstructive CAD prevalence in the external dataset was 27.5% (82/298). The DL model achieved per-artery sensitivity, specificity, positive predictive value, negative predictive value (NPV), and area under the curve (AUC) of 92.7%, 89.9%, 62.6%, 98.5%, and 0.919, respectively; and per-patient values of 93.3%, 80.7%, 67.7%, 96.6%, and 0.871, respectively. The DL model demonstrated high sensitivity and NPV for identifying obstructive CAD in patients with acute chest pain undergoing CCTA, indicating its potential utility in aiding ED physicians in CAD detection.

Fractional flow reserve (FFR) - a physiological indicator of coronary stenosis significance - has now become a widely used parameter also in the guidance of percutaneous coronary intervention (PCI). Several studies have shown the superiority of FFR compared to visual assessment, contributing to the reduction in clinical endpoints. However, the current approach to FFR assessment requires coronary instrumentation with a dedicated pressure wire and thus increasing invasiveness, cost, and duration of the procedure. Alternative, noninvasive methods of FFR assessment based on computational fluid dynamics are being widely tested; these approaches are generally not fully automated and may sometimes require substantial computational power. Nowadays, one of the most rapidly expanding fields in medicine is the use of artificial intelligence (AI) in therapy optimization, diagnosis, treatment, and risk stratification. AI usage contributes to the development of more sophisticated methods of imaging analysis and allows for the derivation of clinically important parameters in a faster and more accurate way. Over the recent years, AI utility in deriving FFR in a noninvasive manner has been increasingly reported. In this review, we critically summarize current knowledge in the field of AI-derived FFR based on data from computed tomography angiography, invasive angiography, optical coherence tomography, and intravascular ultrasound. Available solutions, possible future directions in optimizing cathlab performance, including the use of mixed reality, as well as current limitations standing behind the wide adoption of these techniques, are overviewed.

Magnetic susceptibility differences at the heart-lung interface introduce B₀-field inhomogeneities that challenge cardiac MRI at high field strengths (≥ 3 T). Although hardware-based shimming has advanced, conventional approaches often neglect dynamic variations in thoracic anatomy caused by cardiac and respiratory motion, leading to residual off-resonance artifacts. This study aims to characterize motion-induced B₀-field fluctuations in the heart and evaluate a deep learning-enabled motion-adaptive B₀ shimming pipeline to mitigate them. A motion-resolved B₀ mapping sequence was implemented at 3 T to quantify cardiac and respiratory-induced B₀ variations. A motion-adaptive shimming framework was then developed and validated through numerical simulations and human imaging studies. B₀-field homogeneity and T₂* mapping accuracy were assessed in multiple breath-hold positions using standard and motion-adaptive shimming. Respiratory motion significantly altered myocardial B₀ fields (p < 0.01), whereas cardiac motion had minimal impact (p = 0.49). Compared with conventional scanner shimming, motion-adaptive B₀ shimming yielded significantly improved field uniformity across both inspiratory (post-shim SD_ratio: 0.68 ± 0.10 vs. 0.89 ± 0.11; p < 0.05) and expiratory (0.65 ± 0.16 vs. 0.84 ± 0.20; p < 0.05) breath-hold states. Corresponding improvements in myocardial T₂* map homogeneity were observed, with reduced coefficient of variation (0.44 ± 0.19 vs. 0.39 ± 0.22; 0.59 ± 0.30 vs. 0.46 ± 0.21; both p < 0.01). The proposed motion-adaptive B₀ shimming approach effectively compensates for respiration-induced B₀ fluctuations, enhancing field homogeneity and reducing off-resonance artifacts. This strategy improves the robustness and reproducibility of T₂* mapping, enabling more reliable high-field cardiac MRI.

Atrial fibrillation, a common cardiac arrhythmia with rapid and irregular atrial electrical activity, requires accurate left atrial segmentation for effective treatment planning. Recently, deep learning methods have gained encouraging success in left atrial segmentation. However, current methodologies critically depend on the assumption of consistently complete centered left atrium as input, which neglects the structural incompleteness and boundary discontinuities arising from random-crop operations during inference. In this paper, we propose BSA-Net, which exploits an adaptive adjustment strategy in both feature position and loss optimization to establish long-range feature relationships and strengthen robust intermediate feature representations in boundary regions. Specifically, we propose a Spatial-adaptive Convolution (SConv) that employs a shuffle operation combined with lightweight convolution to directly establish cross-positional relationships within regions of potential relevance. Moreover, we develop the dual Boundary Prioritized loss, which enhances boundary precision by differentially weighting foreground and background boundaries, thus optimizing complex boundary regions. With the above technologies, the proposed method enjoys a better speed-accuracy trade-off compared to current methods. BSA-Net attains Dice scores of 92.55%, 91.42%, and 84.67% on the LA, Utah, and Waikato datasets, respectively, with a mere 2.16 M parameters-approximately 80% fewer than other contemporary state-of-the-art models. Extensive experimental results on three benchmark datasets have demonstrated that BSA-Net, consistently and significantly outperforms existing state-of-the-art methods.

It is unclear whether coronary artery stenosis, plaque burden, and composition differ between major epicardial arteries supplying ischemic myocardial territories. We studied 837 symptomatic patients undergoing coronary computed tomography angiography (CTA) and ¹⁵O-water PET myocardial perfusion imaging for suspected obstructive coronary artery disease. Coronary CTA was analyzed using Artificial Intelligence-Guided Quantitative Computed Tomography (AI-QCT) to assess stenosis and atherosclerotic plaque characteristics. Myocardial ischemia was defined by regional PET perfusion in the left anterior descending (LAD), left circumflex (LCX), and right coronary artery (RCA) territories. Among arteries supplying ischemic territories, the LAD exhibited significantly higher stenosis and both absolute and normalized plaque volumes compared to LCX and RCA (p<0.001 for all). Multivariable logistic regression showed diameter stenosis (p=0.001-0.015), percent atheroma volume (PAV; p<0.001), and percent non-calcified plaque volume (p=0.001-0.017) were associated with ischemia across all three arteries. Percent calcified plaque volume was associated with ischemia only in the RCA (p=0.001). The degree of stenosis and atherosclerotic burden are significantly higher in LAD as compared to LCX and RCA, both in epicardial coronary arteries supplying non-ischemic or ischemic myocardial territories. In all the three main coronary arteries both luminal narrowing and plaque burden are independent predictors of ischemia, where the plaque burden is mainly driven by non-calcified plaque. However, many vessels supplying ischemic territories have relatively low stenosis degree and plaque burden, especially in the LCx and RCA, limiting the ability of diameter stenosis and PAV to predict myocardial ischemia.

Intravascular ultrasound (IVUS)-based tissue characterization has been used to detect vulnerable plaque or lipid-rich plaque (LRP). Recently, advancements in artificial intelligence (AI) technology have enabled automated coronary arterial plaque segmentation and tissue characterization. The purpose of this study was to evaluate the feasibility and diagnostic accuracy of a deep learning model for plaque segmentation, tissue characterization and identification of LRP. A total of 1,098 IVUS images from 67 patients who underwent IVUS-guided percutaneous coronary intervention were selected for the training group, while 1,100 IVUS images from 100 vessels (88 patients) were used for the validation group. A 7-layer U-Net ++ was applied for automated coronary artery segmentation and tissue characterization. Segmentation and quantification of the external elastic membrane (EEM), lumen and guidewire artifact were performed and compared with manual measurements. Plaque tissue characterization was conducted using integrated backscatter (IB)-IVUS as the gold standard. LRP was defined as %lipid area of ≥65 %. The deep learning model accurately segmented EEM and lumen. AI-predicted %lipid area (R = 0.90, P < 0.001), % fibrosis area (R = 0.89, P < 0.001), %dense fibrosis area (R = 0.81, P < 0.001) and % calcification area (R = 0.89, P < 0.001), showed strong correlation with IB-IVUS measurements. The model predicted LRP with a sensitivity of 62 %, specificity of 94 %, positive predictive value of 69 %, negative predictive value of 92 % and an area under the receiver operating characteristic curve of 0.919 (95 % CI:0.902-0.934), respectively. The deep-learning model demonstrated accurate automatic segmentation and tissue characterization of human coronary arteries, showing promise for identifying LRP.

To evaluate the performance of an artificial intelligence (AI)-based software to identify second-trimester fetal ultrasound examinations suspicious for congenital heart defects. The software analyzes all grayscale two-dimensional ultrasound cine clips of an examination to evaluate eight morphologic findings associated with severe congenital heart defects. A data set of 877 examinations was retrospectively collected from 11 centers. The presence of suspicious findings was determined by a panel of expert pediatric cardiologists, who determined that 311 examinations had at least one of the eight suspicious findings. The AI software processed each examination, labeling each finding as present, absent, or inconclusive. Of the 280 examinations with known severe congenital heart defects, 278 (sensitivity 0.993, 95% CI, 0.974-0.998) had at least one of the eight suspicious findings present as determined by the fetal cardiologists, highlighting the relevance of these eight findings. We then evaluated the performance of the AI software, which identified at least one finding as present in 271 examinations, that all eight findings were absent in five examinations, and was inconclusive in four of the 280 examinations with severe congenital heart defects, yielding a sensitivity of 0.968 (95% CI, 0.940-0.983) for severe congenital heart defects. When comparing the AI to the determination of findings by fetal cardiologists, the detection of any finding by the AI had a sensitivity of 0.987 (95% CI, 0.967-0.995) and a specificity of 0.977 (95% CI, 0.961-0.986) after exclusion of inconclusive examinations. The AI rendered a decision for any finding (either present or absent) in 98.7% of examinations. The AI-based software demonstrated high accuracy in identification of suspicious findings associated with severe congenital heart defects, yielding a high sensitivity for detecting severe congenital heart defects. These results show that AI has potential to improve antenatal congenital heart defect detection.

<b><i>Objective:</i></b> Cardiovascular risk stratification based on traditional risk factors lacks precision at the individual level. While coronary artery calcium (CAC) scoring enhances risk prediction by detecting calcified atherosclerotic plaques, it may underestimate risk in individuals with noncalcified plaques-a pattern common in younger type 1 diabetes (T1D) patients. Understanding the prevalence of noncalcified atherosclerosis in T1D is crucial for developing more effective screening strategies. Therefore, this study aimed to assess the burden of clinically significant atherosclerosis in T1D patients with CAC <100 using artificial intelligence (AI)-guided quantitative coronary computed tomographic angiography (AI-QCT). <b><i>Methods:</i></b> This study enrolled T1D patients aged ≥30 years with disease duration ≥10 years and no manifest or symptomatic atherosclerotic cardiovascular disease (ASCVD). CAC and carotid ultrasound were assessed in all participants. AI-QCT was performed in patients with CAC 0 and at least one plaque in the carotid arteries or those with CAC 1-99. <b><i>Results:</i></b> Among the 167 participants (mean age 52 ± 10 years; 44% women; T1D duration 29 ± 11 years), 93 (56%) had CAC = 0, 46 (28%) had CAC 1-99, 8 (5%) had CAC 100-299, and 20 (12%) had CAC ≥300. AI-QCT was performed in a subset of 52 patients. Only 11 (21%) had no evidence of coronary artery disease. Significant coronary stenosis was identified in 17% of patients, and 30 (73%) presented with at least one high-risk plaque. Compared with CAC-based risk categories, AI-QCT reclassified 58% of patients, and 21% compared with the STENO1 risk categories. There was only fair agreement between AI-QCT and CAC (κ = 0.25), and a slight agreement between AI-QCT and STENO1 risk categories (κ = 0.02). <b><i>Conclusion:</i></b> AI-QCT may reveal subclinical atherosclerotic burden and high-risk features that remain undetected by traditional risk models or CAC. These findings challenge the assumption that a low CAC score equates to a low cardiovascular risk in T1D.

This study aimed to explore whether an artificial intelligence iterative reconstruction (AIIR) algorithm combined with low-dose aortic computed tomography angiography (CTA) demonstrates clinical effectiveness in assessing preoperative access for transcatheter aortic valve implantation (TAVI). A total of 109 patients were prospectively recruited for aortic CTA scans and divided into two groups: group A (n = 51) with standard-dose CT examinations (SDCT) and group B (n = 58) with low-dose CT examinations (LDCT). Group B was further subdivided into groups B1 and B2. Groups A and B2 used the hybrid iterative algorithm (HIR: Karl 3D), whereas Group B1 used the AIIR algorithm. CT attenuation and noise of different vessel segments were measured, and the contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) were calculated. Two radiologists, who were blinded to the study details, rated the subjective image quality on a 5-point scale. The effective radiation doses were also recorded for groups A and B. Group B1 demonstrated the highest CT attenuation, SNR, and CNR and the lowest image noise among the three groups (p < 0.05). The scores of subjective image noise, vessel and non-calcified plaque edge sharpness, and overall image quality in Group B1 were higher than those in groups A and B2 (p < 0.001). Group B2 had the highest artifacts scores compared with groups A and B1 (p < 0.05). The radiation dose in group B was reduced by 50.33% compared with that in group A (p < 0.001). The AIIR algorithm combined with low-dose CTA yielded better diagnostic images before TAVI than the Karl 3D algorithm.

Brugada Syndrome (BrS) is an inherited cardiac ion channelopathy associated with an elevated risk of sudden cardiac death, particularly due to ventricular arrhythmias in structurally normal hearts. Affecting approximately 1 in 2,000 individuals, BrS is most prevalent among middle-aged males of Asian descent. Although diagnosis is based on the presence of a Type 1 electrocardiographic (ECG) pattern, either spontaneous or induced, accurately stratifying risk in asymptomatic and borderline patients remains a major clinical challenge. This review explores current and emerging approaches to BrS risk stratification, focusing on electrocardiographic, electrophysiological, imaging, and computational markers. Non-invasive ECG indicators such as the β-angle, fragmented QRS, S wave in lead I, early repolarisation, aVR sign, and transmural dispersion of repolarisation have demonstrated predictive value for arrhythmic events. Adjunctive tools like signal-averaged ECG, Holter monitoring, and exercise stress testing enhance diagnostic yield by capturing dynamic electrophysiological changes. In parallel, imaging modalities, particularly speckle-tracking echocardiography and cardiac magnetic resonance have revealed subclinical structural abnormalities in the right ventricular outflow tract and atria, challenging the paradigm of BrS as a purely electrical disorder. Invasive electrophysiological studies and substrate mapping have further clarified the anatomical basis of arrhythmogenesis, while risk scoring systems (e.g., Sieira, BRUGADA-RISK, PAT) and machine learning models offer new avenues for personalised risk assessment. Together, these advances underscore the importance of an integrated, multimodal approach to BrS risk stratification. Optimising these strategies is essential to guide implantable cardioverter-defibrillator decisions and improve outcomes in patients vulnerable to life-threatening arrhythmias.