Accurate computer-aided interpretation of fetal heart rate (FHR) recordings depends on detecting the baseline and transient accelerations (Acc) and decelerations (Dec) that deviate from it. Most deep learning models treat FHR as a simple 1-D time sequence, overlooking the spectral separation between the low-frequency baseline and high-frequency Acc/Dec patterns. Neglecting this clinically important time-frequency structure can result in missed detections of Acc and Dec events and increased susceptibility to noise. To overcome these limitations, we present WaveFHR-VNet-a U-Net-style, multi-scale, multi-basis wavelet-voting network that analyzes FHR signals in the joint time-frequency domain. WaveFHR-VNet embeds a discrete wavelet transform (DWT) in every encoder block. Each DWT splits the features into approximation (low-pass) coefficients, which preserve the low-frequency baseline trends, and detail (high-pass) coefficients, which preserve the high-frequency Acc/Dec edges. Cascading these decompositions through successive layers yields a hierarchical, multi-scale representation. The decoder uses inverse DWT for full-resolution reconstruction. Skip connections are equipped with an Interactive Coefficient Selection (ICS) module that learns attention masks to suppress Doppler noise and motion artefacts in the detail stream while amplifying diagnostically salient transients. To enhance spectral diversity, five complementary wavelet bases (db4, db6, sym4, sym5, bior3.5) operate in parallel; a simple voting layer fuses their outputs, eliminating manual basis tuning. Evaluated on four FHR datasets, WaveFHR-VNet achieved state-of-the-art performance, with improvements of up to 5.3995% Dice, 5.4758% IoU, and 4.6263% accuracy over the best baselines on LCU-DB, the most widely used public benchmark. It also demonstrates strong cross-dataset generalization, consistently outperforming all comparison models. These results suggest that WaveFHR-VNet can serve as a reliable tool for intrapartum monitoring.

Accurate segmentation of cervical structures in transvaginal ultrasound (TVS) is critical for assessing the risk of spontaneous preterm birth (PTB), yet the scarcity of labeled data limits the performance of supervised learning approaches. This paper introduces the Fetal Ultrasound Grand Challenge (FUGC), the first benchmark for semi-supervised learning in cervical segmentation, hosted at ISBI 2025. FUGC provides a dataset of 890 TVS images, including 500 training images, 90 validation images, and 300 test images. Methods were evaluated using the Dice Similarity Coefficient (DSC), Hausdorff Distance (HD), and runtime (RT), with a weighted combination of 0.4/0.4/0.2. The challenge attracted 10 teams with 82 participants submitting innovative solutions. The best-performing methods for each individual metric achieved 90.26% mDSC, 38.88 mHD, and 32.85 ms RT, respectively. FUGC establishes a standardized benchmark for cervical segmentation, demonstrates the efficacy of semi-supervised methods with limited labeled data, and provides a foundation for AI-assisted clinical PTB risk assessment.

QRS complex detection is a crucial preprocessing step in ECG-based arrhythmia recognition. Traditional methods often fail to maintain stability when confronted with noisy interference, baseline drift, and high inter-patient variability in ECG signals. Although deep learning approaches achieve superior detection performance, their high computational complexity hinders deployment on mobile and edge devices. Therefore, this study proposes a lightweight encoder-decoder network named MSCRAG for robust QRS complex detection. The architecture integrates joint time and frequency analysis with multi-scale convolutional to enable adaptive frequency decomposition, and incorporates a channel attention to enhance QRS time and frequency feature representation. In the encoder and decoder, an improved GhostV2Block (GhostV2BlockMS) reduces model parameters and computational cost while preserving feature extraction efficiency. Furthermore, the Multi-Scale Dynamic Feature Convolution Plus (MSDFC+) module which introduces in the bottleneck enhances the global receptive field and provides dynamic feature modulation, allowing the network to adaptively focus on critical temporal regions and capture long-range dependencies in ECG signals. Evaluations on standard ECG datasets demonstrate that our method achieves 99.1 % accuracy, 97.2 % sensitivity, 99.6 % positive predictive value, and 99.3% F1-score, outperforming existing state-of-the-art approaches. Its high accuracy, lightweight design, and low computational cost render it suitable for both static analysis and real-time cardiac monitoring in dynamic and wearable applications, offering significant potential for clinical and portable healthcare scenarios.

Aortic dissection (AD) is a life-threatening cardiovascular condition requiring precise segmentation of the true lumen (TL) and false lumen (FL) for diagnosis and treatment planning. However, automated segmentation remains challenging due to intensity similarity between lumina, blurred boundaries, false lumen thrombus (FLT), and high anatomical variability. To address these issues, we propose CFVP-Net, a coarse-to-fine visual perception network with adaptive kernels. Inspired by human visual perception, the network integrates global context and local details through three subnets: Fine-Net, Overview-Net, and Focus-Net. Key modules include the Large Selective Kernel (LSK) Block, which adaptively aggregates multi-scale features to distinguish thrombus and calcification, and the Multi-scale Adaptive Spatial Attention Gate (MASAG), which enhances feature fusion and suppresses noise. Experiments on the ImageTBAD dataset demonstrate that CFVP-Net achieves state-of-the-art performance, with DSC scores of 0.959 (TL) and 0.910 (FL), recall of 0.959 (TL) and 0.896 (FL), and HD of 0.388 (TL) and 1.306 (FL), outperforming eight competitive models including UNet++, SwinUNETR, TransUNet, and SAM-Med. Ablation studies confirm the contribution of each component. Generalizability tests on two public datasets (Dongyang Hospital and KIDRADS) further show transferability. CFVP-Net provides an accurate, generalizable solution for AD segmentation, offering strong potential for clinical application.

Accurate segmentation of small anatomical structures like the pubic symphysis in intrapartum ultrasound images is critical for clinical diagnosis, yet remains challenging due to inherent noise, speckle artifacts, low target-background contrast, and blurred boundaries. Established methods often fail to capture long-range dependencies while preserving the spatial continuity of small targets, leading to inaccurate boundary localization. To address these issues, we propose an Edge-Aware Network with Large Kernel Attention (EAN_LKA). Our approach integrates three novel components: a Spatial-Continuous Encoder which capture long-range dependencies via shift operations in order to avoid structural fragmentation caused by traditional patch-based mechanisms and to maintain pixel-level boundary coherence; a Cross-Scale Fusion Attention module which enhances semantic discriminability between target and background by refining multi-scale features between the encoder and decoder in order to effectively mitigate low-contrast boundary ambiguity; and a Large Kernel Attention Decoder which leverages extensive contextual perception to suppress noise interference while guiding detail-preserving boundary reconstruction. Experiments on two public MICCAI challenge datasets demonstrate that the EAN_LKA model significantly outperforms existing methods in small target segmentation tasks, with an average Dice score improvement of 1.18 % and a relative reduction in average surface distance of$\mathbf{3. 8 7 \%}$, fully validating its overall superiority.

Atrial fibrillation (AF) significantly affects morbidity and mortality rates. Class III antiarrhythmic drugs (AADs) play a crucial role in managing AF but often exhibit gender-specific complications. Our study aims to identify gender-specific Class III AADs by integrating in vitro measurements, in silico models, and machine learning (ML). By simulating drug effects on a diverse cardiomyocyte model population (5,663 males and 6,184 females), we classified drugs based on changes in action potentials and calcium transients. Using sex-dependent Support Vector Machine (SVM) algorithms, we achieved high prediction accuracy (>89%) and F1 score (>87%). Key features included changes in resting membrane potential and action potential amplitude, duration and area. Gender differences in drug responses were attributed to lower IK1, INa, and Ito in females.

The right atrium (RA) is critical for cardiac hemodynamics but is often overlooked in clinical diagnostics. This study presents a benchmark framework for RA cavity segmentation from late gadolinium-enhanced magnetic resonance imaging (LGE-MRIs), leveraging a two-stage strategy and a novel 3D deep learning network, RASnet. The architecture addresses challenges in class imbalance and anatomical variability by incorporating multi-path input, multi-scale feature fusion modules, Vision Transformers, context interaction mechanisms, and deep supervision. Evaluated on datasets comprising 354 LGE-MRIs, RASnet achieves SOTA performance with a Dice score of 92.19% on a primary dataset and demonstrates robust generalizability on an independent dataset. The proposed framework establishes a benchmark for RA cavity segmentation, enabling accurate and efficient analysis for cardiac imaging applications. Open-source code (https://github.com/zjinw/RAS) and data (https://zenodo.org/records/15524472) are provided to facilitate further research and clinical adoption.

drug-induced arrhythmias have attracted less attention in the atrium, but they still have hazards that cannot be ignored and are associated with increased morbidity and mortality and decreased physical function and quality of life. There is clinical evidence that antiarrhythmic drugs (AADs) of class III, when used clinically for arrhythmia treatment, have a proarrhythmic risk in the atrium. However, the risk has not been systematically studied. In this paper, we aim to evaluate the relative risk of six class III AADs in causing atrial arrhythmias under SR and AF conditions. To achieve this, we first construct modeling populations of atrial cells from patients with SR or AF, and then simulate the effects of 6 class III AADs on these two populations. Finally, we systematically assess the relative risks of the six drugs under SR and AF conditions using the rate of arrhythmias induced by each drug in the population as metrics. Our results suggest that class III AADs do have the potential to induce atrial arrhythmias. Among the six drugs, vernakalant has the highest risk of proarrhythmic response in SR population, following by dronedarone and dofetilide. Ibutilide and sotalol show relatively intermediate risks, and amiodarone has the lowest risk. In AF population, dronedarone has the highest risk, vernakalant, dofetilide and ibutilide show relatively intermediate risks. Amiodarone and sotalol have relatively low risks. Comprehensive multicenter trials confirm the reliability of our results.

Accurate segmentation of the fetal head and pubic symphysis in intrapartum ultrasound images and measurement of fetal angle of progression (AoP) are critical to both outcome prediction and complication prevention in delivery. However, due to poor quality of perinatal ultrasound imaging with blurred target boundaries and the relatively small target of the public symphysis, fully automated and accurate segmentation remains challenging. In this paper, we propse a dual-path boundary-guided residual network (DBRN), which is a novel approach to tackle these challenges. The model contains a multi-scale weighted module (MWM) to gather global context information, and enhance the feature response within the target region by weighting the feature map. The model also incorporates an enhanced boundary module (EBM) to obtain more precise boundary information. Furthermore, the model introduces a boundary-guided dual-attention residual module (BDRM) for residual learning. BDRM leverages boundary information as prior knowledge and employs spatial attention to simultaneously focus on background and foreground information, in order to capture concealed details and improve segmentation accuracy. Extensive comparative experiments have been conducted on three datasets. The proposed method achieves average Dice score of 0.908$\pm$0.05 and average Hausdorff distance of 3.396$\pm$0.66 mm. Compared with state-of-the-art competitors, the proposed DBRN achieves better results. In addition, the average difference between the automatic measurement of AoPs based on this model and the manual measurement results is 6.157$^{\circ }$, which has good consistency and has broad application prospects in clinical practice.

The sinoatrial node (SAN), the primary pacemaker of the heart, is responsible for the initiation and robust regulation of sinus rhythm. 3D mapping studies of the ex-vivo human heart suggested that the robust regulation of sinus rhythm relies on specialized fibrotically-insulated pacemaker compartments (head, center and tail) with heterogeneous expressions of key ion channels and receptors. They also revealed up to five sinoatrial conduction pathways (SACPs), which electrically connect the SAN with neighboring right atrium (RA). To elucidate the role of these structural-molecular factors in the functional robustness of human SAN, we developed comprehensive biophysical computer models of the SAN based on 3D structural, functional and molecular mapping of ex-vivo human hearts. Our key finding is that the electrical insulation of the SAN except SACPs, the heterogeneous expression of If, INa currents and adenosine A1 receptors (A1R) across SAN pacemaker-conduction compartments are required to experimentally reproduce observed SAN activation patterns and important phenomena such as shifts of the leading pacemaker and preferential SACP. In particular, we found that the insulating border between the SAN and RA, is required for robust SAN function and protection from SAN arrest during adenosine challenge. The heterogeneity in the expression of A1R within the human SAN compartments underlies the direction of pacemaker shift and preferential SACPs in the presence of adenosine. Alterations of INa current and fibrotic remodelling in SACPs can significantly modulate SAN conduction and shift the preferential SACP/exit from SAN. Finally, we show that disease-induced fibrotic remodeling, INa suppression or increased adenosine make the human SAN vulnerable to pacing-induced exit blocks and reentrant arrhythmia. In summary, our computer model recapitulates the structural and functional features of the human SAN and can be a valuable tool for investigating mechanisms of SAN automaticity and conduction as well as SAN arrhythmia mechanisms under different pathophysiological conditions.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and is a major cause of stroke and morbidity. Recent genome-wide association studies have shown that paired-like homeodomain transcription factor 2 (Pitx2) to be strongly associated with AF. However, the mechanisms underlying Pitx2 modulated arrhythmogenesis and variable effectiveness of antiarrhythmic drugs (AADs) in patients in the presence or absence of impaired Pitx2 expression remain unclear. We have developed multi-scale computer models, ranging from a single cell to tissue level, to mimic control and Pitx2-knockout atria by incorporating recent experimental data on Pitx2-induced electrical and structural remodeling in humans, as well as the effects of AADs. The key findings of this study are twofold. We have demonstrated that shortened action potential duration, slow conduction and triggered activity occur due to electrical and structural remodelling under Pitx2 deficiency conditions. Notably, the elevated function of calcium transport ATPase increases sarcoplasmic reticulum Ca2+ concentration, thereby enhancing susceptibility to triggered activity. Furthermore, heterogeneity is further elevated due to Pitx2 deficiency: 1) Electrical heterogeneity between left and right atria increases; and 2) Increased fibrosis and decreased cell-cell coupling due to structural remodelling slow electrical propagation and provide obstacles to attract re-entry, facilitating the initiation of re-entrant circuits. Secondly, our study suggests that flecainide has antiarrhythmic effects on AF due to impaired Pitx2 by preventing spontaneous calcium release and increasing wavelength. Furthermore, our study suggests that Na+ channel effects alone are insufficient to explain the efficacy of flecainide. Our study may provide the mechanisms underlying Pitx2-induced AF and possible explanation behind the AAD effects of flecainide in patients with Pitx2 deficiency.

Motivation: Long QT syndromes (LQTS) are characterized by early after depolarizations (EADs), repolarization dispersion and tachyarrhythmias. However, mechanisms by which these substrates promote tachyarrhythmias remain to be fully elucidated. This study sought to test the hypothesis that EADs induced by abrupt rate accelerations can occur and investigate how this abrupt rate accelerations is related to the mechanisms of reentrant excitations.Methods: The TP06 model for human ventricular cell was modified to model experimental conditions in LQTS. Then, the normal and EADs cell models were incorporated into homogeneous multicellular 1D and 2D tissue models to study the mechanism underlying the generation of reentrant events. Results and conclusions: In single cell simulations, abrupt accelerations in the heart rate prolonged action potential duration and favored to the genesis of EADs. In the ID simulations, an EADs region increased tissue's vulnerability to unidirectional conduction block in response to abrupt rate accelerations. In the 2D idealized tissue simulations, abrupt rate accelerations induced initiation of spiral waves due to an increase in repolarization gradients caused by an EADs region. These computer simulations suggest that abrupt rate accelerations can favor to the genesis of EADs and an EADs region can enhance the susceptibility of arrhythmias by increasing dispersion of repolarization. Thus, the increased regional repolarization dispersion caused by abrupt rate accelerations is a primary factor that may primarily contribute to the genesis of tachyarrhythmias in LQTS.