Wenjia Bai

Cardiac imaging enables quantitative assessment of cardiac structure and function but remains constrained by cost, infrastructure and specialist expertise. In contrast, electrocardiogram (ECG) is widely accessible yet underexploited, despite encoding latent information about cardiac physiology. Here we introduce visionECG , a conditional flow matching framework that learns a probabilistic mapping between two biological distributions—the space of cardiac electrical signals and the space of cardiac geometries. Using 71,132 paired ECG and cardiac mesh sequence datasets from the UK Biobank, with external assessment in 5,000 patients with ECG-echocardiogram pairs, the model reconstructs quantitatively accurate spatiotemporal representations of the left ventricle using ECG inputs and basic demographic information alone. These reconstructions enable discrimination of structural abnormalities and disease labels, provide visualisations of functional abnormalities, and support flexible quantification of both global and regional parameters. By reframing the ECG as a generative source of patient-specific left ventricular geometry and motion, this work establishes a scalable framework for translating low-dimensional signals into high-dimensional, physiologically grounded structured representations.

Right ventricular (RV) trabeculation may reflect adaptation to loading conditions and imprinting of early developmental processes, yet its genetic determinants and clinical relevance in adults remain poorly understood. In contrast to the left ventricle, the RV has distinct developmental origins, geometry and loading conditions, suggesting chamber-specific mechanisms of trabecular remodelling. Using deep learning-based image segmentation and fractal dimension analysis, we quantified RV trabecular complexity in diastole and systole in 48,118 UK Biobank participants with genetic data. RV trabecular morphology was associated with systolic function, as well as cardiometabolic and respiratory conditions. Genetic analyses across the allele frequency spectrum identified 52 common loci and 45 genes with a burden of protein-altering variants, implicating sarcomeric function, cytoskeletal organisation, and early cardiac patterning. Although many loci showed shared effects across both ventricles, we also identified RV-specific genetic associations linked to congenital heart disease and respiratory phenotypes. Notably, associations at CFTR suggest a connection between airway-associated mucus regulation and RV remodelling, whereas MYH6 implicates sarcomeric and developmental mechanisms in adult RV patterning. Together, these findings establish RV trabeculation as a trait that captures both shared and chamber-specific mechanisms relevant to cardiovascular health and disease.

Optimal sleep has a vital role in promoting healthy ageing and enhancing longevity. Here we propose Sleep Chart to assess the relationship between self-reported sleep duration and 23 biological ageing clocks derived from in vivo imaging¹, plasma proteomics² and metabolomics³. First, a systemic, U-shaped pattern emerges between sleep duration and biological age gaps across nine brain and body systems and three omics technologies. The sample-specific lowest biological age gaps are achieved between 6.4 and 7.8 h of sleep duration, varying by organ and sex in the UK Biobank (aged 37–84 years). Furthermore, short (8 h) sleep duration, compared with a normal sleep duration (6–8 h), are associated with increased risk of systemic diseases beyond the brain and all-cause mortality, with evidence from genetic correlations and time-to-incident survival predictions, such as depression and diabetes. Finally, the pathways by which long and short sleep duration are associated with late-life depression differ: ageing clocks may partially mediate the pathway for long sleep duration, while short sleep duration shows a more direct link. Although Mendelian randomization does not provide strong evidence that disease causally affects sleep, it cannot completely exclude such reverse causality. Our findings suggest a cross-organ, multi-omics U-shaped relationship between sleep duration and biological ageing clocks, highlighting the potential of sleep optimization to promote healthy ageing, lower disease risk and extend longevity.

Background: Cardiac magnetic resonance imaging is central to cardiovascular diagnosis and management, yet extracting key clinical measurements remains time-consuming, subjective, and of limited reproducibility. Current deep learning methods often require a separate model trained from scratch for each task, and generating sufficient labelled training data demands substantial clinical expertise. Methods: We developed CineMA, a multi-view conv-transformer masked autoencoder foundation model, pre-trained on 15 million cine cardiac magnetic resonance images from 74,916 studies. The model was fine-tuned and evaluated on eight independent datasets for segmentation, landmark localisation, disease diagnosis, and prognostication, representing the largest such benchmark to date. Performance was compared against convolutional neural network baselines, including nnUNet. Results: Here we show, without dataset-specific hyperparameter tuning, CineMA approaches nnUNet performance in ventricle segmentation and ejection fraction estimation while achieving higher consistency across repeated scans. CineMA surpasses convolutional baselines in cardiovascular disease detection with notably improved specificity, and matches their performance in long-axis function measurement. Beyond cardiac diseases, CineMA shows potential for predicting systemic conditions and survival outcomes, with comparable performance across demographic subgroups. Conclusions: CineMA demonstrates accuracy, learning efficiency, adaptability, and fairness across diverse cardiac image analysis tasks, offering a strong alternative to task-specific model training for automated cardiac image analysis.

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.

The management of pulmonary arterial hypertension (PAH), like so many diseases, currently relies on episodic data from clinic visits, which offers limited insight into a patient’s disease trajectory and dynamic clinical decisions. Patient digital twins present a technological solution that combines hospital and community data into a single dynamic and predictive virtual representation of the patient. Digital twins operationalize real-world observational inference at the individual level, functioning as a complementary decision-support tool alongside trial evidence. This review introduces the concept of a patient digital twin and provides a roadmap for the development, evaluation, and potential implementation of a patient digital twin for PAH. The resulting twin has the potential to change PAH care pathways, shifting PAH care from reactive to proactive management.