Fine-Grained ECG-Text Contrastive Learning via Waveform Understanding Enhancement

Electrocardiograms (ECGs) are essential for diagnosing cardiovascular diseases. While previous ECG-text contrastive learning methods have shown promising results, they often overlook the incompleteness of the reports. Given an ECG, the report is generated by first identifying key waveform features and then inferring the final diagnosis through these features. Despite their importance, these waveform features are often not recorded in the report as intermediate results. Aligning ECGs with such incomplete reports impedes the model's ability to capture the ECG's waveform features and limits its understanding of diagnostic reasoning based on those features. To address this, we propose FG-CLEP (Fine-Grained Contrastive Language ECG Pre-training), which aims to recover these waveform features from incomplete reports with the help of large language models (LLMs), under the challenges of hallucinations and the non-bijective relationship between waveform features and diagnoses. Additionally, considering the frequent false negatives due to the prevalence of common diagnoses in ECGs, we introduce a semantic similarity matrix to guide contrastive learning. Furthermore, we adopt a sigmoid-based loss function to accommodate the multi-label nature of ECG-related tasks. Experiments on six datasets demonstrate that FG-CLEP outperforms state-of-the-art methods in both zero-shot prediction and linear probing across these datasets.

DC-Seg: Disentangled Contrastive Learning for Brain Tumor Segmentation with Missing Modalities

Accurate segmentation of brain images typically requires the integration of complementary information from multiple image modalities. However, clinical data for all modalities may not be available for every patient, creating a significant challenge. To address this, previous studies encode multiple modalities into a shared latent space. While somewhat effective, it remains suboptimal, as each modality contains distinct and valuable information. In this study, we propose DC-Seg (Disentangled Contrastive Learning for Segmentation), a new method that explicitly disentangles images into modality-invariant anatomical representation and modality-specific representation, by using anatomical contrastive learning and modality contrastive learning respectively. This solution improves the separation of anatomical and modality-specific features by considering the modality gaps, leading to more robust representations. Furthermore, we introduce a segmentation-based regularizer that enhances the model's robustness to missing modalities. Extensive experiments on the BraTS 2020 and a private white matter hyperintensity(WMH) segmentation dataset demonstrate that DC-Seg outperforms state-of-the-art methods in handling incomplete multimodal brain tumor segmentation tasks with varying missing modalities, while also demonstrate strong generalizability in WMH segmentation. The code is available at https://github.com/CuCl-2/DC-Seg.

Phenotype-Guided Generative Model for High-Fidelity Cardiac MRI Synthesis: Advancing Pretraining and Clinical Applications

Cardiac Magnetic Resonance (CMR) imaging is a vital non-invasive tool for diagnosing heart diseases and evaluating cardiac health. However, the limited availability of large-scale, high-quality CMR datasets poses a major challenge to the effective application of artificial intelligence (AI) in this domain. Even the amount of unlabeled data and the health status it covers are difficult to meet the needs of model pretraining, which hinders the performance of AI models on downstream tasks. In this study, we present Cardiac Phenotype-Guided CMR Generation (CPGG), a novel approach for generating diverse CMR data that covers a wide spectrum of cardiac health status. The CPGG framework consists of two stages: in the first stage, a generative model is trained using cardiac phenotypes derived from CMR data; in the second stage, a masked autoregressive diffusion model, conditioned on these phenotypes, generates high-fidelity CMR cine sequences that capture both structural and functional features of the heart in a fine-grained manner. We synthesized a massive amount of CMR to expand the pretraining data. Experimental results show that CPGG generates high-quality synthetic CMR data, significantly improving performance on various downstream tasks, including diagnosis and cardiac phenotypes prediction. These gains are demonstrated across both public and private datasets, highlighting the effectiveness of our approach. Code is availabel at https://anonymous.4open.science/r/CPGG.

Large-scale cross-modality pretrained model enhances cardiovascular state estimation and cardiomyopathy detection from electrocardiograms: An AI system development and multi-center validation study

Cardiovascular diseases (CVDs) present significant challenges for early and accurate diagnosis. While cardiac magnetic resonance imaging (CMR) is the gold standard for assessing cardiac function and diagnosing CVDs, its high cost and technical complexity limit accessibility. In contrast, electrocardiography (ECG) offers promise for large-scale early screening. This study introduces CardiacNets, an innovative model that enhances ECG analysis by leveraging the diagnostic strengths of CMR through cross-modal contrastive learning and generative pretraining. CardiacNets serves two primary functions: (1) it evaluates detailed cardiac function indicators and screens for potential CVDs, including coronary artery disease, cardiomyopathy, pericarditis, heart failure and pulmonary hypertension, using ECG input; and (2) it enhances interpretability by generating high-quality CMR images from ECG data. We train and validate the proposed CardiacNets on two large-scale public datasets (the UK Biobank with 41,519 individuals and the MIMIC-IV-ECG comprising 501,172 samples) as well as three private datasets (FAHZU with 410 individuals, SAHZU with 464 individuals, and QPH with 338 individuals), and the findings demonstrate that CardiacNets consistently outperforms traditional ECG-only models, substantially improving screening accuracy. Furthermore, the generated CMR images provide valuable diagnostic support for physicians of all experience levels. This proof-of-concept study highlights how ECG can facilitate cross-modal insights into cardiac function assessment, paving the way for enhanced CVD screening and diagnosis at a population level.

Rapid and Accurate Diagnosis of Acute Aortic Syndrome using Non-contrast CT: A Large-scale, Retrospective, Multi-center and AI-based Study

Chest pain symptoms are highly prevalent in emergency departments (EDs), where acute aortic syndrome (AAS) is a catastrophic cardiovascular emergency with a high fatality rate, especially when timely and accurate treatment is not administered. However, current triage practices in the ED can cause up to approximately half of patients with AAS to have an initially missed diagnosis or be misdiagnosed as having other acute chest pain conditions. Subsequently, these AAS patients will undergo clinically inaccurate or suboptimal differential diagnosis. Fortunately, even under these suboptimal protocols, nearly all these patients underwent non-contrast CT covering the aorta anatomy at the early stage of differential diagnosis. In this study, we developed an artificial intelligence model (DeepAAS) using non-contrast CT, which is highly accurate for identifying AAS and provides interpretable results to assist in clinical decision-making. Performance was assessed in two major phases: a multi-center retrospective study (n = 20,750) and an exploration in real-world emergency scenarios (n = 137,525). In the multi-center cohort, DeepAAS achieved a mean area under the receiver operating characteristic curve of 0.958 (95% CI 0.950-0.967). In the real-world cohort, DeepAAS detected 109 AAS patients with misguided initial suspicion, achieving 92.6% (95% CI 76.2%-97.5%) in mean sensitivity and 99.2% (95% CI 99.1%-99.3%) in mean specificity. Our AI model performed well on non-contrast CT at all applicable early stages of differential diagnosis workflows, effectively reduced the overall missed diagnosis and misdiagnosis rate from 48.8% to 4.8% and shortened the diagnosis time for patients with misguided initial suspicion from an average of 681.8 (74-11,820) mins to 68.5 (23-195) mins. DeepAAS could effectively fill the gap in the current clinical workflow without requiring additional tests.