Search Results (3,648)

In recent years, physiological signal-based biometrics has gained increasing attention due to its resistance to spoofing attacks, suitability for continuous authentication, and compatibility with wearable devices. This study investigates a multimodal biometric framework based on three physiological signals: galvanic skin response (GSR), electrocardiogram (ECG), and photoplethysmography (PPG). Although GSR has demonstrated promising performance in emotion recognition and animal recognition studies, it remains relatively underexplored in biometric human recognition applications compared with ECG and PPG. As a non-invasive signal that can be easily acquired through simple skin contact, GSR offers several advantages, including low-cost sensing, ease of integration into wearable devices, user convenience, and suitability for continuous monitoring. To address this gap, the proposed framework combines features extracted from ECG, PPG, and GSR signals and employs machine learning algorithms for human recognition. The approach was evaluated using two publicly available datasets, CLAS and MAUS. Experimental results demonstrate that multimodal fusion significantly enhances recognition performance, achieving accuracies of 97% and 99% on the CLAS and MAUS datasets, respectively, with the K-Nearest Neighbors (KNN) classifier. These findings highlight the potential of integrating GSR with ECG and PPG signals to develop biometric systems for wearable and continuous authentication applications. Full article

Objective. This study performed a comparative analysis of autonomic responses during voluntary breath-holds at inspiration and expiration by assessing low-frequency (LF) oscillations in heart rate (HR) and photoplethysmogram (PPG) signals. Methods. Eleven healthy volunteers underwent a modified head-up tilt test with breath-holds at inspiration and expiration in supine and standing positions. Electrocardiogram, finger PPG, and respiratory signals were recorded simultaneously. LF oscillation (0.04–0.15 Hz) amplitudes were assessed during spontaneous breathing and breath-hold phases. Relative changes in LF amplitudes (ΔLFA) were calculated for each signal, and the ΔLFA ratio (ΔLFA_PPG/ΔLFA_HR) was derived. Results. Cardiac and vascular signals showed divergent responses in LF oscillation amplitude during breath-holds. While PPG signals demonstrated a significant increase during expiration-holds (ΔLFA_PPG = +71.56%, p = 0.003), HR signals showed a non-significant overall decrease (ΔLFA_HR = −25.28%, p = 0.481). In this exploratory study (n = 11), comparative analysis showed that the vascular response (ΔLFA_PPG) was significantly greater than the cardiac response (ΔLFA_HR) during expiration-holds (p = 0.005) and across all stages (p = 0.012). However, expiration holds were systematically shorter than inspiration holds by approximately 24 s (median 32.1 s vs. 56.3 s). Because breath-hold duration was self-determined and not standardized, the observed differences between inspiration and expiration conditions may reflect either respiratory phase, cumulative apnea duration, or their interaction (our design cannot disentangle these effects). The ΔLFA ratio showed a negative median value overall (−0.25), indicating a complex and often inverse relationship between vascular and cardiac responses. However, due to the small sample size (n = 11), these results are strictly hypothesis-generating and cannot be generalized beyond the studied cohort. The study was powered only to detect large effect sizes (Cohen’s d > 1.2), and the wide bootstrap confidence intervals indicate substantial estimation uncertainty. Independent replication in larger, more diverse populations is essential before any clinical or physiological generalization can be made. Conclusions. This study documents opposing directional changes in cardiac and peripheral vascular LF oscillations during shorter expiration and longer inspiration breath-hold. Because respiratory phase and apnea duration are confounded in our design, we cannot determine whether these differential responses are phase-dependent, duration-dependent, or driven by both factors. The findings highlight the necessity of multi-signal analysis incorporating both ECG and PPG for a more comprehensive autonomic assessment of local and systemic autonomic influences and underscore that future studies must employ standardized breath-hold durations across both respiratory phases to isolate the specific contribution of respiratory phase. The ΔLFA ratio is explored here as a descriptive metric of the direction and magnitude of cardiac-vascular response differences, but its mathematical stability and physiological interpretation remain limited and require validation with direct sympathetic nerve recordings. Full article

Seasonal variation in heart rate variability (HRV) reflects multiple interacting determinants rather than a single underlying determinant. In this study, we aimed to examine subgroup-level seasonal HRV variation in relation to physical activity (PA) using large-scale real-world data. We analyzed 133,747 24-h ECG recordings with tri-axial accelerometry from the ALLSTAR database across eight regions in Japan (after excluding regions with insufficient sample sizes) collected between 2015 and 2021. Seasonal variation (Δ) was defined as the difference between the maximum and minimum seasonal mean values. Weighted least squares models (WLS) were applied to examine associations between ΔPA and multiple HRV indices, including interaction terms for sex and region, while regional differences in residual variability were assessed using Levene’s test. During the normal period, significant associations between ΔPA and ΔHRV were observed for specific indices (ΔULF, ΔVLF, ΔHF, and ΔLF/HF), whereas other indices were not significant. During the Coronavirus Disease 2019 (COVID-19) period, significant associations were observed for ΔRRI, ΔSDRR, and ΔLF/HF, indicating that the association between PA and seasonal HRV variation was index-specific. Sex interactions were not statistically significant after FDR (False Discovery Rate) correction in either period, suggesting a limited role of sex in the PA–HRV relationship at the population level. Regional differences in HRV sensitivity to PA were statistically significant but heterogeneous across regions. In contrast, residual variability exhibited significant regional differences across multiple HRV indices in both periods. These patterns were not fully explained by sample size and showed stable regional heterogeneity. These findings suggest that subgroup-level regional heterogeneity in seasonal HRV variation is primarily reflected in the unexplained component rather than in the direct PA–HRV relationship, indicating the presence of region-specific variability in the unexplained component beyond behavioral influences. Full article

Background/Objectives: Coronary dominance is a key anatomical variant in the interpretation of coronary artery disease and its correlation with electrocardiographic (ECG) findings. While right dominance is the most common pattern, left dominance may influence clinical and ECG presentations. This study aimed to determine the prevalence of coronary dominance using coronary CT angiography (CCTA) and evaluate its association with disease severity, gender, and ECG findings. Methods: A retrospective observational analytical study was conducted including 677 patients who underwent CCTA. Coronary dominance was classified as right, left, or codominant. Disease severity was assessed using CAD-RADS. Demographic, clinical, anatomical, and ECG variables were analyzed. Statistical analysis included descriptive statistics, chi-square tests, and multivariable logistic regression analysis. Results: Right coronary dominance was predominant (89.7%), followed by left dominance (8%) and codominance (2.3%). No significant association was found between coronary dominance and disease severity or gender (p = 0.32). Male gender was significantly associated with severe disease (CAD-RADS 4–5) (p < 0.005). The left anterior descending (LAD) artery was the most frequently affected vessel. In the left dominance subgroup, 72.2% of patients had a normal ECG, with a low prevalence of ischemic findings (3%). Cases of left dominance with severe disease and confirmed acute coronary syndrome were identified. Conclusions: Coronary dominance is not associated with disease severity or gender; however, left dominance represents a clinically relevant finding due to its association with atypical or normal ECG presentations. Its systematic inclusion in CCTA reports is recommended to improve clinical correlation and decision-making. Full article

Spiking Neural P systems provide a rule-based model of distributed computation inspired by membrane computing, while kernel P systems use guarded transformations and structured control of rule applicability. This paper introduces Convolutive Kernel-Guarded Spiking Neural P systems ($\mathrm{CK}-\mathrm{SN}\mathrm{P}$ systems), a formal and trainable framework in which spike-rule applicability may depend on local kernel responses computed over ordered neighborhoods of spike multiplicities. The proposed model provides a general mechanism for local feature computation, combining explicit operational semantics with kernel-based predicates that can be fixed, selected, or embedded in trainable realizations. We define the syntax and transition semantics of the model, relate the construction to delay-free extended Spiking Neural P systems and kernel P systems under stated assumptions, and present a reproducible instantiation for electrocardiographic beat classification under a patient-independent protocol. The empirical study illustrates how CK–SN P local responses can be combined with RR, Gaussian, and Fourier descriptors and evaluated with classical and neural classifiers. Overall, the study clarifies both the formal role of guarded local computation and its practical use as an interpretable feature-generation mechanism. Full article

Accurate R-peak detection in electrocardiogram (ECG) signals is fundamental for cardiovascular analysis. However, most existing methods address differences in sampling frequency (f_s) through signal resampling or transfer learning, which may alter the temporal definition of annotated events. In this study, we propose a f_s consistent framework for ECG R-peak detection that avoids both resampling and retraining. The proposed method is based on low-sampling morphological learning combined with physiological temporal constraints (PTC). A lightweight classifier based on Extreme Gradient Boosting (XGB) was trained on 128-Hz ECG data from the MIT-BIH Normal Sinus Rhythm Database to learn local morphological structures, and feature extraction is defined in milliseconds with time-normalized derivatives to ensure consistency across f_s. The trained model is directly applied to higher-f_s datasets (360 Hz, 500 Hz, and 1000 Hz) without modification. Final peak locations are determined through deterministic processing, including PTC and local snap processing. Experimental results demonstrated that the proposed method achieved stable detection performance across multiple sampling frequencies. When evaluated in a sample-wise manner, the proposed method achieved mean F1-scores of 0.885 on MIT-BIH Arrhythmia Database (360 Hz), 0.848 on Lobachevsky University Electrocardiography Database (LUDB, 500 Hz, sinus rhythm), 0.837 on LUDB (500 Hz, arrhythmia), and 0.953 on PTB Diagnostic ECG Database (1000 Hz), without any resampling or retraining. The integration of probabilistic candidate detection and deterministic temporal alignment enables consistent peak localization under cross-frequency conditions. These findings demonstrate that augmenting machine learning with deterministic decision mechanisms provides a principled framework for f_s-consistent ECG peak detection. Full article

Background: Small animal models are essential for atrial fibrillation (AF) research. Researchers in AF use an electrocardiogram (ECG), echocardiography and invasive electrophysiology study (EPS) to assess atrial structural and electrical remodeling. In relatively smaller cardiac structures and rapid heart rates, the examination can be challenging without special tools designed for animal study. Moreover, conventional invasive EPSs often cause significant trauma, alter autonomic tone, and limit longitudinal evaluations. This study aimed to evaluate the feasibility of repurposing hospital-grade medical devices for the non-invasive, multi-modality assessment of atrial myopathy in a rat model of metabolic syndrome (MetS). Methods: A total of 12 male Sprague-Dawley rats underwent the multi-modality assessment. Structural remodeling was evaluated using hospital-grade echocardiography (8–12 MHz) to measure left atrial (LA) dimensions and volume. Surface ECG was used to determine P-wave duration. Electrical remodeling and AF inducibility were assessed using transesophageal pacing (TEP)-based EPS, evaluating the atrial effective refractory period (AERP), sinus node recovery time (SNRT), and response to rapid atrial burst pacing. Results: The protocols showed high procedural safety (survival rate 91.67%) and successfully characterized atrial myopathy. Surface ECG showed marked intra-atrial conduction delay with prolonged P-wave duration in the MetS group (30.17 ± 4.62 vs. 22.33 ± 1.86 ms, p < 0.05). Echocardiography revealed signs of structural remodeling in the MetS group, evidenced by marked prolonged Isovolumic Relaxation Time (IVRT: 35.602 ± 3.043 vs. 19.187 ± 3.631 ms; p < 0.001) and increased Left Atrial Area (0.223 ± 0.0556 vs. 0.134 ± 0.033; p = 0.007). Furthermore, TEP-based EPS quantified electrical remodeling. The MetS group had shorter AERP (73.33 ± 10.33 ms vs. 120.00 ± 34.06 ms; p = 0.010) and Corrected SNRT (100.67 ± 53.98 ms) versus controls (208.33 ± 76.97 ms; p = 0.018). The MetS group exhibited a higher absolute AF inducibility rate (50%, three out of six rats) compared to the SH group (33.3%, two out of six rats). Conclusions: The integration of surface ECG, echocardiography, and TEP-based EPS provides a safe, highly reproducible, and comprehensive method for evaluating both structural and electrical components of atrial myopathy in small animal models, allowing for robust longitudinal studies. Full article

Existing seizure detection methods cannot fully exploit the spatiotemporal features of multimodal signals. They also fail to capture deep associations among cross-modal features. This limits their ability to learn unified representations of spatiotemporal dependencies. This work proposes CMEpiNet (Complex-valued Multimodal Epilepsy detection Network model) to address this issue. CMEpiNet first uses complex-valued convolutions for feature extraction. It explicitly models phase synchronization, phase shifts, and cross-frequency coupling. Thus, EEG, ECG, and EMG features are represented in the complex-valued domain. During feature fusion, CMEpiNet uses a two-level semantic alignment-based fusion method. It applies cross-modal consistency constraints in a shared alignment space. It also performs distribution-level alignment in an epilepsy-related semantic latent space. These operations ensure the consistency of multimodal features in the global semantic structure. Finally, CMEpiNet uses a spatial attention-guided 3D convolutional classifier. The classifier jointly models the temporal, feature, and modality dimensions. Experimental results on the SeizeIT2 dataset show that CMEpiNet improves seizure detection sensitivity, reduces the false alarm rate, and maintains stable performance under perturbations. Full article

Polysomnography (PSG) is the gold standard for diagnosing sleep disorders, but its complexity and cost limit widespread use. Heart rate variability (HRV) is traditionally assessed from electrocardiography (ECG), while photoplethysmography (PPG), widely available in wearable devices, offers a more accessible alternative. However, its reliability over full-night recordings remains underexplored. This study analyzes data from 50 subjects across five groups (healthy controls, rapid eye movement sleep behavior disorder, obstructive sleep apnea, periodic limb movements, and mixed comorbidities) to assess agreement between ECG-derived HRV and PPG-derived pulse rate variability (PRV), considering time-, frequency-, and nonlinear-domain features. Correlation and equivalence analyses were performed, with and without removal of artifactual segments. Correlation coefficients exceeded 0.6 for most features and improved to above 0.7 after artifact removal. Consistent improvements were observed across all subject groups. Equivalence testing further identified a subset of features showing high agreement and low bias. The results indicate that, with appropriate pre-processing, PPG can approximate ECG-derived variability in full-night sleep recordings. The identification of robust features for screening purposes supports the use of PRV for wearable-based screening and monitoring in heterogeneous sleep disorder populations. Full article

Background/Objectives: Electrocardiogram (ECG)-based myocardial infarction (MI) classifiers may achieve high internal validation performance but show reduced performance when applied to data from another source. The task is a controlled binary healthy-versus-MI benchmark and is not intended to represent real-world chest-pain triage or autonomous clinical deployment. This study evaluated the source-only transportability of engineered 12-lead ECG feature families for binary healthy-versus-MI classification across a cardiologist-annotated hospital dataset and PTB-XL. Methods: The hospital dataset contained 1749 usable recordings from 1434 patients after excluding 206 broken-data records, with 1550 Healthy and 199 MI recordings. The matched PTB-XL binary subset contained 14,982 recordings from 13,436 patients, with 9513 Healthy and 5469 MI recordings. Eleven engineered feature families and five classifier families were compared under preprocessing, patient-aware splitting, source-validation hyperparameter and threshold selection, and bootstrap uncertainty estimation. The reported leading rows are the highest observed configurations in a prespecified benchmark grid, not locked clinical models. Results: Internal performance was higher than strict source-only transfer performance. In the hospital dataset, fiducial interval descriptors with Extra Trees reached balanced accuracy 0.775 and receiver operating characteristic area under the curve (ROC-AUC) 0.855. In PTB-XL, a broad hybrid feature bank with ST-segment information and XGBoost reached a balanced accuracy of 0.898 and ROC-AUC of 0.965. Strict source-only transfer was weaker and asymmetric: the highest observed balanced accuracy was 0.580 for hospital-to-PTB-XL transfer and 0.632 for PTB-XL-to-hospital transfer. Ranking transportability and operating-threshold transportability diverged, most notably for hospital-to-PTB-XL transfer, where ROC-AUC was 0.774 but sensitivity at the source-selected threshold was only 0.164. A secondary target-threshold analysis improved balanced accuracy to 0.682 and 0.640, respectively, but this used target labels only to re-select the operating threshold and was not a strict source-only result. Conclusions: The findings indicate a transportability gap: PTB-XL-to-hospital transfer was more balanced than hospital-to-PTB-XL transfer, but neither direction achieved performance comparable to internal validation. The source-only operating-point results are not acceptable for clinical MI screening or decision support without additional calibration, target-setting validation, and prospective assessment. Full article

Background: There are certain disadvantages to using CAD/CAM technologies. Marginal and internal accuracy of fit is valued as one of the most important criteria for the clinical quality and success of all-ceramic crowns. The assessment of the marginal fit of lithium disilicate and zirconia CAD/CAM crowns before and after ceramic layering is crucial. Methods: One ideally prepared model tooth was duplicated into 64 plaster models. A standardized wax pattern for a monolithic crown and a coping were produced and used to mill 16 lithium disilicate monolithic crowns and 16 cores using a soft milling process. 16 zirconia crowns and 16 cores were also fabricated. A factorial design with (material) (lithium disilicate [E] or zirconia [Z]); (design) (monolithic, [M] or (core) [C]); and (finish) (as-produced [P] or veneered/glazed [G]) was used to create the following groups: ZMP, ZMG, ZCP, ZCG, EMP, EMG, ECP, and ECG (n = 8). The milled restorations were treated accordingly using ZirLiner, IPS e.max Ceram, and IPS e.max Glaze. The restorations were cemented to their dies, embedded in epoxy resin, and sectioned into two planes with a diamond saw. Vertical and horizontal marginal fit at the finishing line was measured in a standardized way at four locations (mesial, distal, facial, and lingual). Results: There were no differences, p > 0.05, between all Z groups; however, they had significantly wider horizontal gaps, p < 0.05, (116 ± 5 µm) than E groups (64 ± 13 µm). Among lithium disilicate groups, the glazed monolithic (EMG) and veneered/glazed coping (ECG) subgroups showed significantly smaller horizontal gaps (approximately 50 ± 6 µm). Statistical analysis was performed using two-way ANOVA with a significance level set at α = 0.05. Conclusions: Veneering techniques did not affect zirconia. Lithium disilicate had a better marginal fit than zirconia, but this was influenced by veneering techniques. Lithium disilicate veneering and/or glazing significantly improved the marginal fit. Full article

Background: Understanding how humans perceive and represent themselves is central to cognitive neuroscience. Although self-face processing is well characterized, how distinct dimensions of the self, such as facial and creator identities, are neurally integrated remains largely unknown. Heartbeat-related self-expression and its underlying neural interactions remain unexplored. Methods: We simultaneously recorded EEG and ECG signals from 32 art students while they viewed self- and other-created portraits (of self and other). Results: The results revealed distinct processing pathways for facial identity and creator identity, while both self-related stimuli elicited special neural responses. Furthermore, their interaction emerged in early visual evoked potentials (188–344 ms) and late heartbeat-evoked potentials (344–596 ms). Conclusions: These findings indicate that the self is modulated by visual and interoceptive signals, offering new insights into the neural mechanisms of selfhood in artistic practice. Full article

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) has been linked to atrial fibrillation (AF); however, its relationship with specific AF subtypes remains unclear. This prospective, single-center, observational case–control study investigated whether MASLD is independently associated with AF presence and its subtypes. Materials: A total of 327 participants were analyzed, including 119 controls and 208 patients with AF. Comprehensive clinical history, anthropometric measures, laboratory testing, 24 h Holter ECG, and echocardiography were performed. Clinically inferred MASLD was defined according to the current EASL–EASD–EASO guidelines using clinical and non-invasive indices (Hepatic Steatosis Index, Fatty Liver Index, Fibrosis-4 Index). No liver biopsy or imaging confirmation of steatosis or fibrosis was performed, and therefore, the diagnosis represents a clinically inferred (“probable”) MASLD. To minimize systematic bias and improve baseline comparability between groups, propensity score matching and complementary regression analyses were applied. Results: Overall probable MASLD prevalence did not differ between AF and controls (42% vs. 44%, p = 0.742). A clear phenotypic gradient emerged across subtypes: lowest in permanent AF (PermAF, 27.1%) versus paroxysmal (47.1%) and persistent AF (51.4%) (p = 0.021). PermAF exhibited the most advanced comorbidity—highest CHF (78.6%), CKD (71.4%), HFpEF (48.6%), FIB-4 (median 2.67), the lowest TG/HDL–cholesterol ratio (1.93 vs. 3.32; p < 0.001), and progressive renal impairment. Statin therapy reached 80% in clinically inferred MASLD-positive PermAF. The elevated FIB-4 observed in PermAF must be interpreted with explicit caution: this group was substantially older (median 79.5 years) and carried the highest burden of chronic heart failure and chronic kidney disease; therefore, in this subgroup, FIB-4 most plausibly reflects age and cardio-renal comorbidity rather than histologically confirmed hepatic fibrosis. After matching, MASLD was not an independent predictor of AF presence (OR = 0.96; 95% CI: 0.59–1.46) or its clinical severity. Conclusions: Probable MASLD, defined by clinical and non-invasive indices, was not independently associated with AF in this cohort, but AF subtypes exhibited a clear phenotypic gradient—from a metabolically driven profile in early AF to a cardio-renal and fibrotic pattern in advanced, elderly AF. Elevated FIB-4 values in PermAF most plausibly reflect age and cardio-renal comorbidity rather than true histologically confirmed hepatic fibrosis. These findings support a phenotype- and population-dependent MASLD–AF relationship and underscore the need for imaging- and histology-verified longitudinal studies. Full article

Gramian Angular Field (GAF) encodings transform time series into two-dimensional images suitable for convolutional neural network (CNN) classification. Existing applications typically use either the Gramian Angular Summation Field (GASF) or the Gramian Angular Difference Field (GADF) independently, although these two encodings capture complementary pairwise angular relationships. This paper proposes the Composite Gramian Angular Field (CGAF), a single-image time-series representation obtained by a weighted algebraic combination of the summation and difference GAF components. The weights are optimised using coarse grid search followed by Gaussian-process Bayesian refinement, with all candidate evaluation restricted to training-only inner validation partitions. The selected weights are frozen before held-out test evaluation. CGAF produces a single encoded output image (approximately 0.08 MB, compared with approximately 0.16 MB for retaining separate GASF and GADF images) and encodes at $5.9\pm 0.3$ ms per sample. We evaluate CGAF in three domain-specific settings—EEG cognitive engagement, PTB-DB heartbeat classification, and FordA automotive fault detection—and on a selected subset of 20 datasets from the UCR Time Series Classification Archive. The method is compared with GASF, GADF, recurrence plots, spectrogram-based encodings, and non-image time-series baselines including SVM, ResNet-1D, InceptionTime, and ROCKET. On the evaluated datasets, CGAF consistently improves over the individual GASF and GADF encodings. It achieves macro-F1 $=0.867\pm 0.027$ on the EEG pilot study, heartbeat-segment-level macro-F1 $=0.941\pm 0.018$ on PTB-DB, and test accuracy $=91.2\%$ on FordA. Because patient identifiers are unavailable for PTB-DB, that result does not establish patient-level generalisation. On the selected UCR subset, CGAF outperforms both GASF and GADF on all 20 datasets. It achieves the best overall accuracy among all evaluated methods on 14 of 20 datasets, whereas ROCKET achieves the best overall accuracy on the remaining six datasets. The results suggest that algebraic integration of summation-based and difference-based angular dependencies can improve image-based time-series classification without modifying the CNN backbone or adding gradient-trained parameters. The EEG results should be interpreted as pilot evidence, whereas broader generalisation requires evaluation on the full UCR/UEA archive, additional biomedical cohorts, and further backbone architectures. Full article

Current acute coronary syndrome (ACS) care relies on sequential, single-modality diagnostics, in which the electrocardiogram, the troponin trajectory, and the coronary angiogram are interpreted independently rather than as a joint signal. This narrative review maps rather than pools the evidence. We selectively searched PubMed, EMBASE, Cochrane CENTRAL, and Web of Science (January 2015–February 2026); study selection was performed by a single reviewer, without duplicate screening, a PRISMA flow diagram, or a formal risk-of-bias assessment. The three key findings are as follows: A machine learning-enabled electrocardiogram (ECG) for diagnosing occlusion due to myocardial infarction achieved an AUC of 0.938 (95% CI = 0.924–0.951) on data not seen during training and correctly diagnosed 42% of patients that expert interpreters missed. A machine learning-enabled high-sensitivity troponin interpretation method, CoDE-ACS, reported an AUC of 0.953 and increased the number of patients ruled out at initial evaluation from 27% to 61%. Angiographically derived physiological methods produced conflicting results—quantitative flow ratios reduced major adverse cardiovascular events (MACE) in the FAVOR III China trial (HR 0.65), but in FAVOR III Europe the angiography-derived approach did not prove non-inferior to FFR; if anything, QFR guidance led to more events (6.7% vs. 4.2%, an event rate about 60% higher in the QFR arm; HR 1.63; 95% CI 1.11–2.41). There was no difference between FFR-angio and FFR in the ALL-RISE trial. These are diagnostic-accuracy and prognostic-association findings; no trial has yet shown that AI-guided ACS care reduces death, reinfarction, or ischemia-driven revascularization. Full article