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  • Journal article
    Zhou T, Li M, Ruan S, Luo T, Jiang B, Zhu J, Ma P, Yang D, Yang Get al., 2026,

    A reliable framework for brain tumor segmentation via multi-modal fusion and uncertainty modeling

    , Information Fusion, Vol: 129, ISSN: 1566-2535

    Accurate 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.
  • Journal article
    Cheng CW, Huang J, Zhang Y, Yang G, Schönlieb CB, Aviles-Rivero AIet al., 2026,

    Mamba neural operator: Who wins? transformers vs. state-space models for PDEs

    , Journal of Computational Physics, Vol: 548, ISSN: 0021-9991

    Partial 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.
  • Journal article
    Ma X, Tao Y, Zhang Z, Zhang Y, Wang X, Zhang S, Ji Z, Zhang Y, Chen Q, Yang Get al., 2026,

    Test-time generative augmentation for medical image segmentation

    , MEDICAL IMAGE ANALYSIS, Vol: 109, ISSN: 1361-8415
  • Journal article
    Jing P, Lee K, Zhang Z, Zhou H, Yuan Z, Gao Z, Zhu L, Papanastasiou G, Fang Y, Yang Get al., 2026,

    Reason like a radiologist: Chain-of-thought and reinforcement learning for verifiable report

    , MEDICAL IMAGE ANALYSIS, Vol: 109, ISSN: 1361-8415
  • Journal article
    Ma J, Jiang M, Fang X, Chen J, Wang Y, Yang Get al., 2026,

    Hybrid aggregation strategy with double inverted residual blocks for lightweight salient object detection

    , NEURAL NETWORKS, Vol: 194, ISSN: 0893-6080
  • Journal article
    Zhang S, Nan Y, Fang Y, Wang S, Liu Y, Papanastasiou G, Gao Z, Li S, Walsh S, Yang Get 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
  • Conference paper
    Hasan MK, Yang G, Yap CH, 2026,

    Motion-Enhanced Cardiac Anatomy Segmentation via an Insertable Temporal Attention Module

    , Pages: 143-153, ISSN: 0302-9743

    Cardiac 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.
  • Journal article
    Rjoob K, McGurk K, Zheng S, Curran L, Ibrahim M, Zeng L, Kim V, Tahasildar S, Kalaie S, Senevirathne S, Gifani P, Zheng J, Bai W, de Marvao A, Ware J, Bender C, O'Regan Det al., 2025,

    A multi-modal vision knowledge graph of cardiovascular disease

    , Nature Cardiovascular Research, ISSN: 2731-0590

    Understanding 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.
  • Journal article
    Lakdawala NK, Hershberger RE, Garcia-Pavia P, Elliott PM, Ginns J, Meder B, Solomon S, Cunningham JW, Gimeno JR, Barriales-Villa R, Adler E, Gerull B, Pereira NL, Halliday BP, Li W, Jarugula P, Maruyama S, Mohran SE, Papadaki M, Anto AR, Anderson RL, Rodriguez HM, Del Rio CL, Edelberg JM, Kurio G, Maya J, Januzzi JLet 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: 2598-2612

    BACKGROUND: 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%
  • Journal article
    Johnson R, Fletcher RA, Peters S, Ohanian M, Soka M, Smolnikov A, Abihider KE, Ackerman MJ, Ader F, Akhtar MM, Amin AS, Ashley EA, Atherton JJ, Austin R, Baas AF, Bagnall RD, Ross SB, Blouin J-L, Brown EE, Bundgaard H, Cannie D, Chmielewski P, Correnti G, Crespo-Leiro MG, Dal Ferro M, Dellefave-Castillo LM, Dominguez F, Dooijes D, Dybro AM, Ed Demri Y, El Hachmi M, Escobar-Lopez L, Foye SJ, Franaszczyk M, Gigli M, Lopez EG, Goudal A, Graw S, Guipponi M, Haan E, Haas J, Hammersley DJ, Hansen FG, Hayward CS, Hey TM, Heymans S, Ho CY, Houweling AC, Ingles J, Ingrey A, Jabbour A, James PA, Jansweijer JA, Jongbloed JDH, Keogh AM, Larrañaga-Moreira JM, Lekanne Deprez RH, Macciocca I, Macdonald PS, Mansencal N, Mansour J, Martinez-Veira C, McDonough B, McGaughran J, Medo K, Merlo M, Michalak E, Monserrat L, Mountain H, Muller SA, Murphy AM, Murray B, Oates EC, Ormondroyd E, Pachter N, Paldino A, Palmyre A, Pereira NL, Picard KC, Poplawski N, Prasad S, Proukhnitzky J, Pruny J-F, Reant P, Richard P, Ronan A, Sedaghat-Hamedani F, Semsarian C, Storm G, Stroeks S, Syrris P, Taylor MRG, Thomson K, Thompson T, van Tintelen JP, Vissing CR, Waddell-Smith KE, Wallis M, Zentner D, Australian Genomics Cardiac Flagship, Arnott C, Marian AJ, Oh J, Fokstuen S, James CA, Barriales-Villa R, Meder B, Wahbi K, Giudicessi JR, Parikh VN, Ware JS, Piriou N, Rooryck C, Lakdawala NK, Mestroni L, Sinagra G, Elliott PM, Watkins H, McNally EM, Charron P, van Spaendonck-Zwarts KY, Garcia-Pavia P, Peña-Peña ML, Mogensen J, Christensen AH, Bilińska ZT, Rasmussen TB, Seidman JG, Seidman CE, Te Riele ASJM, Verdonschot JAJ, Pinto YM, Christiaans I, Fatkin Det al., 2025,

    Titin-related familial dilated cardiomyopathy: factors associated with disease onset.

    , Eur Heart J, Vol: 46, Pages: 5240-5257

    BACKGROUND 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.
  • Journal article
    Ardissino M, Nicolaides K, Conti-Ramsden F, Mack J, Syngelaki A, Escobar MME, Wright A, Wright D, Ware J, Butterworth A, Williamson C, Honigberg M, Sovio U, Smith GCS, Hysi P, de Marvao Aet 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 article
    Danylenko O, Lwin MT, Kasouridis I, Masding A, Von Klemperer K, Macrae R, Monteiro RP, Senior R, Li Wet 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 article
    Khalique Z, Scott AD, Ferreira PF, Molto M, Nielles-Vallespin S, Pennell DJet 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 Imaging

    BACKGROUND: 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±
  • Journal article
    Wang F, Wang Z, Li Y, Lyu J, Qin C, Wang S, Guo K, Sun M, Huang M, Zhang H, Tanzer M, Li Q, Chen X, Huang J, Wu Y, Zhang H, Hamedani KA, Lyu Y, Sun L, Li Q, He T, Lan L, Yao Q, Xu Z, Xin B, Metaxas DN, Razizadeh N, Nabavi S, Yiasemis G, Teuwen J, Zhang Z, Wang S, Zhang C, Ennis DB, Xue Z, Hu C, Xu R, Oksuz I, Lyu D, Huang Y, Guo X, Hao R, Patel JH, Cai G, Chen B, Zhang Y, Hua S, Chen Z, Dou Q, Zhuang X, Tao Q, Bai W, Qin J, Wang H, Prieto C, Markl M, Young A, Li H, Hu X, Wu L, Qu X, Yang G, Wang Cet al., 2025,

    Towards Modality- and Sampling-Universal Learning Strategies for Accelerating Cardiovascular Imaging: Summary of the CMRxRecon2024 Challenge.

    , IEEE Trans Med Imaging, Vol: PP

    Cardiovascular 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.
  • Journal article
    Patlatzoglou K, Pastika L, Barker J, Sieliwonczyk E, Khattak GR, Zeidaabadi B, Ribeiro AH, Ware JS, Peters NS, Ribeiro ALP, Kramer DB, Waks JW, Sau A, Ng FSet al., 2025,

    The cost of explainability in artificial intelligence-enhanced electrocardiogram models

    , npj Digital Medicine, ISSN: 2398-6352

    Artificial 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.
  • Journal article
    Akbari T, Hammersley DJ, May CY-Y, Halliday BP, Prasad SKet al., 2025,

    The impact of cardio-renal-metabolic profile in dilated cardiomyopathy

    , Current Cardiology Reports, Vol: 27, ISSN: 1534-3170

    Purpose 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.
  • Journal article
    Le TT, Lim S, Yang C, Bryant JA, Han Y, Phua SK, Aw TC, Cook SA, Chin CWLet al., 2025,

    Circulating Interleukin-6 Predicts Adverse Outcomes in Asians with Hypertrophic Cardiomyopathy

    , Medcomm, Vol: 6
  • Journal article
    Wang Z, Xiao M, Zhou Y, Wang C, Wu N, Li Y, Gong Y, Chang S, Chen Y, Zhu L, Zhou J, Cai C, Wang H, Jiang X, Guo D, Yang G, Qu Xet al., 2025,

    Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI

    , IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, Vol: 72, Pages: 3642-3654, ISSN: 0018-9294
  • Journal article
    Hao P, Wang H, Yang G, Zhu Let al., 2025,

    Enhancing Visual Reasoning With LLM-Powered Knowledge Graphs for Visual Question Localized-Answering in Robotic Surgery

    , IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 29, Pages: 9027-9040, ISSN: 2168-2194
  • Journal article
    Wang H, Chen Y, Chen W, Xu H, Zhao H, Sheng B, Fu H, Yang G, Zhu Let al., 2025,

    Serp-Mamba: Advancing High-Resolution Retinal Vessel Segmentation With Selective State-Space Model.

    , IEEE Trans Med Imaging, Vol: 44, Pages: 4811-4825

    Ultra-Wide-Field Scanning Laser Ophthalmoscopy (UWF-SLO) images capture high-resolution views of the retina with typically spanning 200 degrees. Accurate segmentation of vessels in UWF-SLO images is essential for detecting and diagnosing fundus disease. Recent studies highlight that Mamba's selective State Space Model (SSM) excels in modeling long-range dependencies with linear computational complexity, making it highly suitable for preserving the continuity of elongated vessel structures, especially for high-resolution UWF images. Inspired by this, we propose the Serpentine Mamba (Serp-Mamba) network to address this challenging task. Specifically, we recognize the intricate, varied, and delicate nature of the tubular structure of vessels. Furthermore, the high-resolution of UWF-SLO images exacerbates the imbalance between the vessel and background categories. Based on the above observations, we first devise a Serpentine Interwoven Adaptive (SIA) scan mechanism, which scans UWF-SLO images along curved vessel structures in a snake-like crawling manner. This approach, consistent with vascular texture transformations, ensures the effective and continuous capture of curved vascular structure features. Second, we propose an Ambiguity-Driven Dual Recalibration (ADDR) module to address the category imbalance problem intensified by high-resolution images. Our ADDR module delineates pixels by two learnable thresholds and refines ambiguous pixels through a dual-driven strategy, thereby accurately distinguishing vessels and background regions. Experiment results on three datasets demonstrate the superior performance of our Serp-Mamba on high-resolution vessel segmentation. We also conduct a series of ablation studies to verify the impact of our designs. Our code will be released upon publication (https://github.com/whq-xxh/Serp-Mamba).
  • Journal article
    Jin W, Tian X, Wang N, Wu B, Shi B, Zhao B, Yang Get al., 2025,

    Representation-driven sampling and adaptive policy resetting for improving multi-Agent reinforcement learning

    , NEURAL NETWORKS, Vol: 192, ISSN: 0893-6080
  • Journal article
    Kacar P, Prokselj K, Ghonim S, Semple T, V Babu-Narayan S, Nashat H, Wort SJ, Gatzoulis MA, Brida Met al., 2025,

    Advances in the imaging of pulmonary hypertension

    , INTERNATIONAL JOURNAL OF CARDIOLOGY CONGENITAL HEART DISEASE, Vol: 22, ISSN: 2666-6685
  • Journal article
    Stroeks SLVM, Oko-Osi S, Arasu A, Hirst JE, Tayal UPet al., 2025,

    Sex differences in dilated cardiomyopathy: evidence gaps and future directions

    , JACC, ISSN: 0735-1097

    Dilated cardiomyopathy (DCM), which affects 1 in 250 people, is a leading global cause of heart failure and the most common indication for heart transplantation. Evidence suggests that DCM is more prevalent in men, but whether this reflects biological differences or underdiagnosis in women remains uncertain. This review explores the impact of sex on DCM, examining differences in epidemiology, etiology, clinical presentation, treatment response, and outcomes. Women often present with less severe cardiac phenotypes, including lower levels of fibrosis and better left ventricular function, yet the long-term prognosis of DCM in women is less clear. Through a systematic review and meta-analysis, we found that male DCM patients with variants in PLN, DSP, and LMNA had higher arrhythmic event rates compared with TTNtv and BAG3 carriers. In female patients with DCM, those with RBM20, DSP, and PLN variants faced the highest arrhythmic risk, and TTNtv carriers the lowest. PLN and LMNA variants had the highest heart failure risk in both sexes, whereas BAG3, RBM20, and TTN variants had lower heart failure rates in female compared with male carriers. These findings highlight the influence of sex and genotype on clinical outcomes. Current risk-stratification tools, such as those used for implantable cardioverter-defibrillators, may undertreat women owing to reliance on sex-neutral thresholds. We highlight the role of genetic, environmental, and reproductive factors in shaping these disparities, including the influence of pregnancy, pregnancy complications, and menopause. This review identifies key gaps in knowledge and calls for expanded representation of women in DCM studies and the development of sex-specific risk models. Addressing these gaps is essential to improving outcomes and advancing equitable personalized care for all DCM patients.
  • Journal article
    Hatipoglu S, Voges I, Pushparajah K, Pomiato E, Mohiaddin R, Izgi C, Pennell DJ, Krupickova S, Di Salvo Get al., 2025,

    Stress imaging in paediatric and congenital heart disease patients.

    , Eur Heart J Cardiovasc Imaging

    Stress imaging in paediatric cardiology and congenital heart disease patients has an increasing role for functional assessment. Indications include coronary artery anomalies and disease in association with anomalous aortic origin of coronary arteries, Kawasaki disease or surgical manipulation of the coronary ostia, as well as assessment of elevated filling pressures, dynamic left ventricular outflow obstruction or significance of valvular heart disease. This review provides practical guidance focused on commonly used stress echocardiography and stress cardiovascular magnetic resonance in context of their clinical indications for this age group.
  • Journal article
    Halliday BP, Owen R, Ragavan A, Smith KL, Statton B, Berry A, Kasiakogias A, Tsoumani Z, Shanmuganathan M, Dungu JN, De Marvao A, Tayal U, Ware JS, O'regan DP, Pennell DJ, Cleland JGF, Prasad SK, Gregson J, Murphy MP, Rider OJ, Valkovic Let al., 2025,

    A double-blind, randomized placebo-controlled trial examining the effect of MitoQ on myocardial energetics in patients with dilated cardiomyopathy

    , EUROPEAN HEART JOURNAL-CARDIOVASCULAR IMAGING, ISSN: 2047-2404

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