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Journal articleZhou T, Li M, Ruan S, et al., 2026,
A reliable framework for brain tumor segmentation via multi-modal fusion and uncertainty modeling
, Information Fusion, Vol: 129, ISSN: 1566-2535Accurate brain tumor segmentation from MRI scans is critical for effective diagnosis and treatment planning. Recent advances in deep learning have significantly improved brain tumor segmentation performance. However, these models still face challenges in clinical adoption due to their inherent uncertainties and potential for errors. In this paper, we propose a novel MR brain tumor segmentation approach that integrates multi-modal data fusion and uncertainty quantification to improve the accuracy and reliability of brain tumor segmentation. Recognizing that each MR modality contributes unique insights into the tumor’s characteristics, we propose a novel modality-aware guidance by explicitly categorizing the modalities into ”teacher” (FLAIR and T1c) and ”student” (T2 and T1) groups. Since the teacher modalities are the most informative modalities for identifying brain tumors, we propose a multi-modal teacher-student fusion strategy. This strategy leverages the teacher modalities to guide the student modalities in both spatial and channel feature representation aspects. To address prediction reliability, we employ Monte Carlo dropout during training to generate multiple uncertainty estimates. Additionally, we develop a novel uncertainty-aware loss function that optimizes segmentation accuracy while quantifying the uncertainty in predictions. Experimental results conducted on three BraTS datasets demonstrate the effectiveness of the proposed components and the superior performance compared to the state-of-the-art methods, highlighting their potential for clinical application.
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Journal articleCheng CW, Huang J, Zhang Y, et al., 2026,
Mamba neural operator: Who wins? transformers vs. state-space models for PDEs
, Journal of Computational Physics, Vol: 548, ISSN: 0021-9991Partial differential equations (PDEs) are widely used to model complex physical systems, but solving them efficiently remains a significant challenge. Recently, Transformers have emerged as the preferred architecture for PDEs due to their ability to capture intricate dependencies. However, they struggle with representing continuous dynamics and long-range interactions. To overcome these limitations, we introduce the Mamba Neural Operator (MNO), a novel framework that enhances neural operator-based techniques for solving PDEs. MNO establishes a formal theoretical connection between structured state-space models (SSMs) and neural operators, offering a unified structure that can adapt to diverse architectures, including Transformer-based models. By leveraging the structured design of SSMs, MNO captures long-range dependencies and continuous dynamics more effectively than traditional Transformers. Through extensive analysis, we show that MNO significantly boosts the expressive power and accuracy of neural operators, making it not just a complement but a superior framework for PDE-related tasks, bridging the gap between efficient representation and accurate solution approximation.
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Journal articleMa X, Tao Y, Zhang Z, et al., 2026,
Test-time generative augmentation for medical image segmentation
, MEDICAL IMAGE ANALYSIS, Vol: 109, ISSN: 1361-8415 -
Journal articleJing P, Lee K, Zhang Z, et al., 2026,
Reason like a radiologist: Chain-of-thought and reinforcement learning for verifiable report
, MEDICAL IMAGE ANALYSIS, Vol: 109, ISSN: 1361-8415 -
Journal articleMa J, Jiang M, Fang X, et al., 2026,
Hybrid aggregation strategy with double inverted residual blocks for lightweight salient object detection
, NEURAL NETWORKS, Vol: 194, ISSN: 0893-6080 -
Journal articleZhang S, Nan Y, Fang Y, et al., 2026,
Dynamical multi-order responses and global semantic-infused adversarial learning: A robust airway segmentation method
, MEDICAL IMAGE ANALYSIS, Vol: 108, ISSN: 1361-8415 -
Journal articleArdissino M, Morley AP, Truong B, et al., 2026,
Genetic Association of Circulating Proteins and Gene Transcripts With Spontaneous Coronary Artery Dissection.
, Circ Genom Precis MedBACKGROUND: Spontaneous coronary artery dissection (SCAD) is an uncommon cause of myocardial infarction that disproportionately affects women, particularly during pregnancy and the peripartum period. Limited understanding of its underlying pathophysiology hinders the development of effective preventive and therapeutic strategies. METHODS: This study investigated associations between genetically predicted circulating proteins and tissue-specific RNA levels with genetically predicted SCAD risk using Mendelian randomization and Bayesian colocalization. Genetic scores for >1500 circulating proteins were derived from the UK Biobank (n=34 557) and deCODE (n=35 559). Scores for 13 848 gene transcripts in arterial and fibroblast tissues were generated from Genotype-Tissue Expression data. Associations between these scores and SCAD were assessed in a genome-wide association study meta-analysis of 1917 individuals with SCAD and 9292 controls. Findings were validated in vitro using mass spectrometry-based proteomic analysis of extracellular vesicles from 50 patients with SCAD and 50 healthy controls. RESULTS: Genetic associations of 4 circulating proteins with SCAD (AFAP1 [actin filament-associated protein 1], ECM1 [extracellular matrix protein 1], SPON1 [spondin 1], and STAT6 [signal transducer and activator of transcription 6]) were identified. Two were supported by gene expression data (AFAP1 and ECM1), and one by tissue-specific Bayesian colocalization analyses (ECM1). Protein interaction mapping identified potential shared pathways through the JAK-STAT signaling pathway and inflammatory regulation. Mass spectrometry-based proteomic analysis demonstrated that ECM1 was significantly upregulated in SCAD cases versus controls. CONCLUSIONS: Integrative analysis of proteomic, transcriptomic, and experimental data revealed 4 circulating proteins genetically associated with SCAD risk, with ECM1 emerging as a key protein with a likely causal role in SCAD pa
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Journal articleMohal JS, Whinnett ZI, Mohiddin SA, et al., 2026,
Electromechanically Optimized Right Ventricular Pacing for Obstructive Hypertrophic Cardiomyopathy: The EMORI-HCM Trial.
, J Am Coll Cardiol, Vol: 87, Pages: 124-139BACKGROUND: Many patients with symptomatic obstructive hypertrophic cardiomyopathy (oHCM) have devices capable of right ventricular pacing (RVP). Although pacing can reduce left ventricular outflow tract gradient (LVOTg), it can also reduce cardiac output, so its net effect is variable. OBJECTIVES: We tested whether electromechanical optimization of the programmed atrio-ventricular delay (AVD) allows RVP to achieve a net benefit on symptoms. METHODS: EMORI-HCM (Electromechanically Optimized Right Ventricular Pacing in Obstructive Hypertrophic Cardiomyopathy) is a multicenter, blinded, randomized, crossover trial of AVD-optimized RVP in patients with symptomatic oHCM with resting or provoked gradient of at least 30 mm Hg. Patients with existing dual-chamber devices were randomized to either 3 months of continuous AVD-optimized RVP (intervention) followed by 3 months of backup-only RVP (control), or vice versa. AVD was optimized using a high-precision multiple-alternation protocol assessing acute change in beat-by-beat blood pressure while varying AVD. The primary outcome was symptoms measured by the Kansas City Cardiomyopathy Questionnaire Clinical Summary Score. Secondary outcomes include patient-reported daily symptom data collected using a dedicated smartphone application (ORBITA-app), dichotomous patient preference, EQ-5D, exercise capacity, and LVOTg. Patients were blinded to treatment allocation. Symptom assessments were self-administered. Outcome measures were recorded at baseline, crossover, and completion. Analysis was by Bayesian ordinal mixed modeling. RESULTS: Between October 2021 and October 2024, 117 screened patients met the inclusion criteria, of whom 60 were randomized. AVD-optimized RVP improved Kansas City Cardiomyopathy Questionnaire Clinical Summary Score (+4.5; 95% credible interval [CrI]: 1.3-8.1; probability of benefit [Prbenefit] = 0.997) and daily symptom scores (OR: 1.29; 95% CrI: 0.98-1.68; Prbenefit: 0.969) compared with backup-only pacin
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Conference paperZhang H, Huang J, Wu Y, et al., 2026,
Lightweight Hypercomplex MRI Reconstruction: A Generalized Kronecker-Parameterized Approach
, Pages: 95-105, ISSN: 0302-9743Magnetic Resonance Imaging (MRI) is crucial for clinical diagnostics but is hindered by prolonged scan times. Current deep learning models enhance MRI reconstruction but are often memory-intensive and unsuitable for resource-limited systems. This paper introduces a lightweight MRI reconstruction model leveraging Kronecker-Parameterized Hypercomplex Neural Networks to achieve high performance with reduced parameters. By integrating Kronecker-based modules, including Kronecker MLP, Kronecker Window Attention, and Kronecker Convolution, the proposed model efficiently extracts spatial features while preserving representational power. We introduce Kronecker U-Net and Kronecker SwinMR, which maintain high reconstruction quality with approximately 50% fewer parameters compared to existing models. Experimental evaluation on the FastMRI dataset demonstrates competitive PSNR, SSIM, and LPIPS metrics, even at high acceleration factors (8× and 16×), with no significant performance drop. Additionally, Kronecker variants exhibit superior generalization and reduced overfitting on limited datasets, facilitating efficient MRI reconstruction on hardware-constrained systems. This approach sets a new benchmark for parameter-efficient medical imaging models. Code is available at:https://github.com/Whethe/HyperKron-MRI-Recon.
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Conference paperHasan MK, Yang G, Yap CH, 2026,
Motion-Enhanced Cardiac Anatomy Segmentation via an Insertable Temporal Attention Module
, Pages: 143-153, ISSN: 0302-9743Cardiac anatomy segmentation is useful for clinical assessment of cardiac morphology to inform diagnosis and intervention. Deep learning (DL), especially with motion information, has improved segmentation accuracy. However, existing techniques for motion enhancement are not yet optimal, and they have high computational costs due to increased dimensionality or reduced robustness due to suboptimal approaches that use non-DL motion registration, non-attention models, or single-headed attention. They further have limited adaptability and are inconvenient for incorporation into existing networks where motion awareness is desired. Here, we propose a novel, computationally efficient Temporal Attention Module (TAM) that offers robust motion enhancement, modeled as a small, multi-headed, cross-temporal attention module. TAM’s uniqueness is that it is a lightweight, plug-and-play module that can be inserted into a broad range of segmentation networks (CNN-based, Transformer-based, or hybrid) for motion enhancement without requiring substantial changes in the network’s backbone. This feature enables high adaptability and ease of integration for enhancing both existing and future networks. Extensive experiments on multiple 2D and 3D cardiac ultrasound and MRI datasets confirm that TAM consistently improves segmentation across a range of networks while maintaining computational efficiency and improving on currently reported performance. The evidence demonstrates that it is a robust, generalizable solution for motion-awareness enhancement that is scalable (such as from 2D to 3D). The code is available at https://github.com/kamruleee51/TAM.
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Journal articleRjoob K, McGurk K, Zheng S, et al., 2025,
A multi-modal vision knowledge graph of cardiovascular disease
, Nature Cardiovascular Research, ISSN: 2731-0590Understanding gene-disease associations is important for uncovering pathological mechanisms and identifying potential therapeutic targets. Knowledge graphs can represent and integrate data from multiplebiomedical sources, but lack individual-level information on target organ structure and function. Here wedevelop CardioKG, a knowledge graph that integrates over 200,000 computer vision-derived cardiovascular phenotypes from biomedical images with data extracted from 18 biological databases to model overa million relationships. We used a variational graph auto-encoder to generate node embeddings from theknowledge graph to predict gene-disease associations, assess druggability and identify drug repurposing strategies. The model predicted genetic associations and therapeutic opportunities for leading causesof cardiovascular disease, which were associated with improved survival. Candidate therapies includedmethotrexate for heart failure and gliptins for atrial fibrillation, and the addition of imaging data enhancedpathway discovery. These capabilities support the use of biomedical imaging to enhance graph-structuredmodels for identifying treatable disease mechanisms.
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Journal articlePouliopoulos J, Imran M, Anthony C, et al., 2025,
Cardiovascular magnetic resonance feature tracking for rejection surveillance after cardiac transplantation.
, J Cardiovasc Magn Reson, Vol: 28BACKGROUND: Endomyocardial biopsy (EMB) is the standard invasive method for monitoring acute cardiac allograft rejection (ACAR); however, non-invasive alternatives are increasingly proving to be dependable. OBJECTIVES: We aimed to identify and validate dependable cardiovascular magnetic resonance (CMR) strain indices for ACAR detection. METHODS: We analyzed 160 CMR scans, including long- and short-axis cines, as well as T1/T2 maps from 54 transplant recipients. Uniparametric and multiparametric models integrating left ventricular strain metrics and tissue characteristics were developed to classify histological rejection grades (0, 1 R, ≥2 R) and evaluate therapeutic response. RESULTS: Regression analysis using generalized linear mixed-models identified significant differences between rejection groups, with global radial strain (GRS) (z-value = 3.1, p = 0.002) and global circumferential strain (GCS) (z-value = 2.5 p<0.008) outperforming global longitudinal strain (GLS) in discriminating ≥2 R from 1 R rejection. Diagnostic performance for detecting ≥2 R rejection was excellent, particularly for GCS (AUC = 0.852, negative predictive value [NPV] = 98.3%) and GRS (AUC = 0.826, NPV = 95.8% (95.8/100)), with enhanced accuracy in the anterolateral mid-basal segments (AUC>0.886, NPV>97.9%). Strain metrics effectively monitored recovery post-therapy for ≥2 R rejection, showing significant improvements (GRS Δ24.5±7.1%, GCS Δ15.9±4.6%, GLS Δ27.4±11.8%, all p<0.02). Furthermore, as strained-based detection of ≥2 R rejection correlated with increases in edema detected using T1/T2 mapping (all p<0.001), integrating strain with T1/T2 mapping significantly enhanced diagnostic accuracy, with T2+GRS (AUC = 0.931, NPV = 98.2) and T1+T2+GCS (AUC = 0.943, NPV = 97.5) as the most effective models. CONCLUSION: Segmental CMR strain analysis demonstrates excellent diagn
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Journal articleLakdawala NK, Hershberger RE, Garcia-Pavia P, et al., 2025,
Danicamtiv, a Selective Agonist of Cardiac Myosin, for Dilated Cardiomyopathy: A Phase 2 Open-Label Trial.
, J Am Coll Cardiol, Vol: 86, Pages: 2598-2612BACKGROUND: Precision therapies for dilated cardiomyopathy (DCM) are lacking despite diverse manifestations and clinical trajectories based on underlying etiology. DCM-associated pathogenic variants in cardiac beta-myosin heavy chain (MYH7) and titin (TTN) can impair the function/availability of cardiac myosin. Danicamtiv is a novel investigational small molecule that selectively enhances cardiac myosin function. OBJECTIVES: The study sought to translationally evaluate danicamtiv in the setting of myosin dysfunction, from in vitro assessments to a clinical trial exploring its safety and efficacy in DCM due to MYH7 or TTN variants, or other causes (either positive or negative genetic results). METHODS: The danicamtiv effects on DCM-variant cardiac myosin enzyme activity and skinned left ventricular (LV) fiber force generation were assessed in vitro. A phase 2a, baseline-controlled, open-label trial enrolled individuals with DCM into cohorts by variants (MYH7 or TTN) or other causes. In the week of treatment period (TP)1, participants received oral danicamtiv 25 mg twice daily with dose adjustment in TP2 to 10 or 50 mg twice daily. The primary endpoint was safety/tolerability; secondary endpoints included echocardiography-assessed changes in cardiac structure/function. RESULTS: In vitro, danicamtiv increased the activity of wild-type and DCM myosin, increasing force production in cardiac fibers. Forty-one participants were enrolled in the trial (mean age 49.6 ± 13 years; mean baseline LV ejection fraction 33.4% ± 8.0%). Twelve had MYH7 variants, 14 had TTN variants, and 15 had other cause DCM. Treatment-emergent adverse events were reported in 22 (53.7%) of 41 participants (all mild or moderate; 1 discontinuation); an asymptomatic increase in cardiac troponin was detected in 3 participants in the other causes cohort. After TP2, the MYH7 and TTN cohorts showed improvements from baseline in LV ejection fraction (MYH7: 8.8% [95% CI: 5.03%-12.64%]; TTN: 5.9%
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Journal articleJohnson R, Fletcher RA, Peters S, et al., 2025,
Titin-related familial dilated cardiomyopathy: factors associated with disease onset.
, Eur Heart J, Vol: 46, Pages: 5240-5257BACKGROUND AND AIMS: Truncating variants in the TTN gene (TTNtv) are the most common genetic cause of dilated cardiomyopathy (DCM) but also occur as incidental findings in the general population. This study investigated factors associated with the clinical manifestation of TTNtv. METHODS: An international multicentre retrospective observational study was performed in families with TTNtv-related DCM. Shared frailty models were used to estimate associations of variant characteristics with lifetime risk of DCM, and logistic regression to estimate odds ratios (ORs) for individual-level clinical risk factor profiles (cardiac conditions, cardiovascular comorbidities, lifestyle) and DCM. RESULTS: A total of 3158 subjects in 1043 families with TTNtv-related DCM were studied. TTNtv-positive subjects were 21-fold more likely to develop DCM [OR, 21.21; 95% confidence interval (CI), 14.80-30.39]. Disease onset was earlier in males, but was similar for TTNtv of different types and locations. The presence of clinical risk factors was associated with earlier DCM onset (OR, 3.41; 95% CI, 2.06-5.64), with a prior history of atrial fibrillation having a two-fold increased odds of DCM (OR, 2.05; 95% CI, 1.27-3.32). The prevalence of clinical risk factors increased with age; however, the strength of the DCM association was greatest for young-onset (<30 years) disease (OR, 4.75; 95% CI, 2.35-9.60). Administration of beta-adrenergic receptor or renin-angiotensin system-blocking drugs prior to overt DCM was associated with 87% reduced odds of DCM (OR, .13; 95% CI, .08-.23). CONCLUSIONS: Disease onset in TTNtv-associated familial DCM is dependent on individual patient context and is potentially modifiable by risk factor management and prophylactic therapeutic intervention.
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Journal articleArdissino M, Nicolaides K, Conti-Ramsden F, et al., 2025,
Polygenic Risk Scores for Preeclampsia Prediction Beyond Gold-Standard Clinical Models in Multiethnic Populations
, JOURNAL OF THE AMERICAN HEART ASSOCIATION, Vol: 14 -
Journal articleDanylenko O, Lwin MT, Kasouridis I, et al., 2025,
The Role of Stress Echocardiography in Patients With Anomalous Aortic Origin of Coronary Arteries: Two Tertiary Cardiac Centers' Experience
, ECHOCARDIOGRAPHY-A JOURNAL OF CARDIOVASCULAR ULTRASOUND AND ALLIED TECHNIQUES, Vol: 42, ISSN: 0742-2822 -
Journal articleKhalique Z, Scott AD, Ferreira PF, et al., 2025,
Diffusion Tensor CMR Assessment of the Microstructural Response to Dobutamine Stress in Health and Comparison With Patients With Recovered Dilated Cardiomyopathy.
, Circ Cardiovasc ImagingBACKGROUND: Contractile reserve assessment assesses myocardial performance and prognosis. The microstructural mechanisms that facilitate increased cardiac function have not been described, but can be studied using diffusion tensor cardiovascular magnetic resonance. Resting microstructural contractile function is characterized by reorientation of aggregated cardiomyocytes (sheetlets) from wall-parallel in diastole to a more wall-perpendicular configuration in systole, with the diffusion tensor cardiovascular magnetic resonance parameter E2A defining their orientation, and sheetlet mobility defining the angle through which they rotate. We used diffusion tensor cardiovascular magnetic resonance to identify the microstructural response to dobutamine stress in healthy volunteers and then compared with patients with recovered dilated cardiomyopathy (rDCM). METHODS: In this first-of-its-kind prospective observational study, 20 healthy volunteers and 32 patients with rDCM underwent diffusion tensor cardiovascular magnetic resonance at rest, during dobutamine, and on recovery. RESULTS: In healthy volunteers, both diastolic and systolic E2A increased with dobutamine stress (13±3° to 17±5°; P<0.001 and 59±11° to 65±7°; P=0.002). Sheetlet mobility remained unchanged (45±11° to 49±10°; P=0.19), but biphasic mean E2A increased (36±6° to 41±4°; P<0.001). In rDCM, diastolic E2A at rest was higher than in healthy volunteers (20±8° versus 13±3°, P<0.001), and sheetlet mobility was reduced (34±12° versus 45±11°; P<0.001). During dobutamine stress, rDCM diastolic and systolic E2A increased compared with rest (20±8° to 24±10°; P=0.001 and 54±13° to 63±11°; P=0.005). However, sheetlet mobility in patients with rDCM failed to increase with dobutamine to healthy levels (39±13° versus 49±
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Journal articleTänzer M, Scott AD, Khalique Z, et al., 2025,
Accelerating cDTI with deep learning-based tensor de-noising and breath hold reduction. a step towards improved efficiency and clinical feasibility
, Journal of Cardiovascular Magnetic Resonance, Vol: 27, ISSN: 1097-6647BackgroundCardiac Diffusion Tensor Imaging (cDTI) non-invasively provides unique insights into cardiac microstructure. Current protocols require multiple breath-hold repetitions to achieve adequate signal-to-noise ratio, resulting in lengthy scan times. The aim of this study was to develop a cDTI de-noising method that would enable the reduction of repetitions while preserving image quality.MethodsWe present a novel de-noising framework for cDTI acceleration centred on three fundamental advances: (1) a paradigm shift from image-based to tensor-space de-noising that better preserves structural information, (2) an ensemble of Vision Transformer-based models specifically optimised for tensor processing through adversarial training, and (3) a sophisticated data augmentation strategy that maximises training data utilisation through dynamic repetition selection.ResultsOur approach reduces scan times by a factor of up to 4 while achieving a 20% reduction in cDTI maps errors over existing de-noising methods (Table 1) and preserving anatomical features such as infarct characterisation and transmural cardiomyocyte orientation patterns. Crucially, our proposed method succeeds in clinical cases where other algorithms previously failed.ConclusionsThis demonstrates substantial improvements in cDTI acquisition efficiency, achieving up to 4-fold scan time reduction (3-5 breath-holds) while maintaining diagnostic accuracy across diverse cardiac pathologies.
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Journal articleWang F, Wang Z, Li Y, et al., 2025,
Towards Modality- and Sampling-Universal Learning Strategies for Accelerating Cardiovascular Imaging: Summary of the CMRxRecon2024 Challenge.
, IEEE Trans Med Imaging, Vol: PPCardiovascular health is vital to human well-being, and cardiac magnetic resonance (CMR) imaging is considered the clinical reference standard for diagnosing cardiovascular disease. However, its adoption is hindered by long scan times, complex contrasts, and inconsistent quality. While deep learning methods perform well on specific CMR imaging sequences, they often fail to generalize across modalities and sampling schemes. The lack of benchmarks for high-quality, fast CMR image reconstruction further limits technology comparison and adoption. The CMRxRecon2024 challenge, attracting over 200 teams from 18 countries, addressed these issues with two tasks: generalization to unseen modalities and robustness to diverse undersampling patterns. We introduced the largest public multi-modality CMR raw dataset, an open benchmarking platform, and shared code. Analysis of the best-performing solutions revealed that prompt-based adaptation and enhanced physics-driven consistency enabled strong cross-scenario performance. These findings establish principles for generalizable reconstruction models and advance clinically translatable AI in cardiovascular imaging.
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Journal articlePatlatzoglou K, Pastika L, Barker J, et al., 2025,
The cost of explainability in artificial intelligence-enhanced electrocardiogram models
, npj Digital Medicine, ISSN: 2398-6352Artificial intelligence-enhanced electrocardiogram (AI-ECG) models have shown outstanding performance in diagnostic and prognostic tasks, yet their black-box nature hampers clinical adoption. Meanwhile, a growing demand for explainable AI in medicine underscores the need for transparent, trust-worthy decision-making. Moving beyond post-hoc explainability techniques that have shown unreliable results, we focus on explicit representation learning using variational autoencoders (VAE), to capture inherently interpretable ECG features. While VAEs have demonstrated potential for ECG interpretability, the presumed performance-explainability trade-off remains underexplored, with many studies relying on complex, non-linear methods that obscure the morphological information of the features. In this work, we present a novel framework (VAE-SCAN) to model bi-directional, interpretable associations between ECG features and clinical factors. We also investigate how different representations affect ECG decoding performance across models with varying levels of explainability. Our findings demonstrate the cost introduced by intrinsic ECG interpretability, based on which we discuss potential implications and directions.
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Journal articleAkbari T, Mach L, Hammersley DJ, et al., 2025,
Visually assessed ischaemia on cardiac magnetic resonance, but not quantitative perfusion metrics, predicts symptomatic improvement in coronary artery bypass
, Journal of Cardiovascular Magnetic Resonance, Vol: 27, ISSN: 1097-6647BackgroundSerial perfusion cardiovascular magnetic resonance (CMR) in symptomatic patients undergoing coronary artery bypass grafting (CABG) may provide mechanistic insight into dynamic abnormalities of the myocardium.ObjectivesTo assess how changes in cardiac reperfusion and remodelling associate with symptom improvement in patients undergoing CABGMethodsPatients awaiting elective CABG completed serial quality of life questionnaires and detailed CMR at baseline and at 6-12 months post CABG as per protocol. Automated fully quantitative stress and rest myocardial blood flow was calculated, alongside assessment of the visual ischaemic burden. Findings were correlated with changes in symptomatology.ResultsOf 40 patients who underwent serial evaluation with CMR (mean age 62.1±9.3, median LVEF 68% [IQR: 62-73%]), there was improvement in the median visual ischaemic burden (42% [IQR: 27-51] vs 18% [IQR: 11-21], P<0.001), mean global stress myocardial blood flow (1.34±0.5 ml/min/g vs 1.59±0.5 ml/min/g, P=0.002) and median global myocardial perfusion reserve (1.85±0.6 vs 2.4±0.9, P<0.001) following CABG. Greater improvement in the SAQ-7 summary score was associated with a greater decrease in the visual ischaemic burden following CABG (ρ=-0.38, P=0.02). Quantitative MBF metrics did not associate with baseline or change in SAQ-7 summary score.ConclusionSerial perfusion CMR identifies dynamic changes in markers of myocardial perfusion in patients following CABG. Greater reduction of visually assessed ischaemia associated with improvement in SAQ-7 score. Quantitative perfusion indices were not associated with symptom improvement in this study. The results also suggest residual inducible ischaemia post CABG requiring further studies to elucidate its clinical relevance.
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Journal articleTeh I, Moulin K, Ferreira PF, et al., 2025,
Multi-center investigation of cardiac diffusion tensor imaging in healthy volunteers by the Society of Cardiovascular Magnetic Resonance Cardiac Diffusion Special Interest Group NETwork (SIGNET)
, Journal of Cardiovascular Magnetic Resonance, Vol: 27, ISSN: 1097-6647BackgroundCardiac diffusion tensor imaging (cDTI) is an emerging technique for microstructural characterization of the heart and has shown clinical potential in a range of cardiomyopathies. However, there is substantial variation reported for in vivo cDTI results across the literature, and sensitivity of cDTI to differences in imaging sites, scanners, acquisition protocols, and post-processing methods remains incompletely understood.MethodsSIGNET is a prospective multi-center, observational study in traveling and non-traveling healthy volunteers. The study was initiated by the executive board of the Society of Cardiovascular Magnetic Resonance (SCMR) Cardiac Diffusion Special Interest Group (SIG) as a follow-up to a previous multi-center study on phantom validation of cardiac DTI and a recently published SCMR consensus statement on cardiac diffusion MRI. The study has been developed by the Project Management Committee in consultation with the SCMR cardiac diffusion SIG, which includes international experts in cardiac diffusion MRI. To date, more than 20 international institutions have engaged with the study, including sites that are new to cardiac DTI, making this the largest collaborative effort in the field.DiscussionSIGNET will provide important information about the key sources of variation in cardiac DTI. This will help rationalize strategies for addressing and minimizing such variation. Harmonization of protocols in this and future studies will underpin efforts to translate cardiac DTI for clinical application.
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Journal articleAkbari T, Hammersley DJ, May CY-Y, et al., 2025,
The impact of cardio-renal-metabolic profile in dilated cardiomyopathy
, Current Cardiology Reports, Vol: 27, ISSN: 1534-3170Purpose of ReviewDilated cardiomyopathy is an important contributor to heart failure burden worldwide. With an aging population and rising multimorbidity, in this review, we describe the prevalence of metabolic syndrome and renal failure in patients with dilated cardiomyopathy and focus on common underlying mechanisms, evaluate outcomes in these patients and highlight newer therapeutic strategies.Recent FindingsA significant proportion of patients with dilated cardiomyopathy has concomitant metabolic syndrome and renal disease. This combination of multimorbidity portends worse prognosis and often presents unique challenges in treatment given the complex interplay and shared pathophysiological pathways.SummaryOptimization of the cardio-renal-metabolic profile should be a key consideration in the management of patients with dilated cardiomyopathy. Therapeutic strategies targeting common pathophysiological pathways are needed in order to improve overall outcomes.
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Journal articleLe TT, Lim S, Yang C, et al., 2025,
Circulating Interleukin-6 Predicts Adverse Outcomes in Asians with Hypertrophic Cardiomyopathy
, Medcomm, Vol: 6 -
Journal articleJin W, Tian X, Wang N, et al., 2025,
Representation-driven sampling and adaptive policy resetting for improving multi-Agent reinforcement learning
, NEURAL NETWORKS, Vol: 192, ISSN: 0893-6080
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