The atrial repolarization (Ta wave) characteristics remains largely unexplored, given its inherently low amplitude and obscured by the QRS complex. Hence, this study aims to witness Ta wave within QRS complex. 10 s ECGs of 50 Sinus Rhythm (SR), 50 Sinus Tachycardia (SiT) and 20 Atrial Tachycardia (AT) were recorded using standard 12-lead. The datapoints were extracted from pre-processed Lead-II and three spline model interpolated hidden Ta wave post fiducial point detection. Further, validation analysis was performed with and without QRS complex to select the optimal spline model with the Ta wave of SiT Modified Limb Lead (MLL) & Atrio-Ventricular block (AVB) ECG. It was noted that the cubic spline interpolation model gave the best SSIM score of 0.85 and lowest power spectrum % difference of 0.1 % for Ta wave interpolation without QRS complex. Further, ECG-based Ta temporal and voltage features were crafted. Statistically significant features were used for five ML models multi-class classification. Extra-Trees model gave the best output with 99 % using P-Ta feature combined. Overall, the proposed method demonstrated that along with the existing P wave features, Ta wave features have potential in better classification of atrial arrhythmia, while interpolation model offers ease of implementation and adaptability to diverse clinical applications.
Laboratory automation enhances repeatability and throughput in scientific and industrial applications. An important aspect of automation is transportation of standardized components, such as microplates (MPs). Previously, we developed the compliant-mechanism-based gripper ("CrocoGrip"), enabling secure and contamination-free MP transport. However, we have yet to optimize the gripping jaws to increase the gripper's maximum load capacity (LC). In this paper, we optimize the design and surface of CrocoGrip's jaws to optimize its LC. CrocoGrip works like a torsion spring, so its opening width, which defines the CrocoGrip's deformation, its jaw arm length, and the jaw's surface material affect its LC. We tested the LC of six different jaw materials at different opening widths. The best-performing jaw surface was further evaluated at a second jaw arm length. Jaws equipped with silicone inserts performed the best (LC=2.93 N). Aluminum with different surface smoothness performed the worst (0.68 N≤LC≤1.56 N). Decreasing the jaw arm length from 81 mm to 66 mm increased the LC of the silicone-insert jaws to 3.71 N. The improved LC allows the safe handling of MP (weight ≤127 g), enabling the use of the CrocoGrip for a broader range of tasks.
Tendon and ligament injuries significantly affect patients' quality of life, with current treatments, such as surgery and tissue grafting, often resulting in recurrence and additional complications. Tissue engineering has emerged as a promising alternative by integrating cells, biomaterials, and bioactive cues. This review summarizes recent studies (2015 to present) on the application of polysaccharides in developing tissue-engineered tendons and ligaments, emphasizing the critical role of cell-material interactions in various stages of tissue engineering. Polysaccharide-based biomaterials have gained attention for their structural and functional versatility, biocompatibility, and abundance from renewable sources. Their resemblance to the native extracellular matrix of tendons makes them excellent candidates for scaffolding biomaterials in tendon and ligament tissue engineering. This review highlights advancements in using polysaccharides, demonstrating their potential to enhance regenerative outcomes by closely mimicking the native tissue environment.
Conventional cuff-based blood pressure (BP) measurement provides only intermittent readings, whereas photoplethysmography (PPG)-based methods enable continuous and noninvasive monitoring. This study aims to develop a deep learning framework for accurate, cuffless BP estimation using a single PPG signal. A hybrid deep neural network, termed ResNet-BiGRU, was developed by integrating residual convolutional blocks with bidirectional gated recurrent units to jointly capture morphological and temporal features. The UCI Cuff-Less Blood Pressure Estimation Dataset (a subset of MIMIC-II), which contains synchronized PPG and arterial blood pressure (ABP) signals from 942 subjects, was used for model training and validation. After applying a 0.5-8 Hz bandpass filter and segmenting into 5 s windows, the data were split 80/20 for training and validation. External evaluation was conducted using the VitalDB dataset, which provides synchronized PPG and ABP recordings from surgical patients under diverse physiological conditions. The model achieved mean absolute errors (MAE) of 4.78 mmHg for systolic BP (SBP) and 2.98 mmHg for diastolic BP (DBP) on UCI, and 8.15 mmHg for SBP and 4.59 mmHg for DBP on VitalDB. The ResNet-BiGRU model demonstrates accurate, robust, and generalizable cuffless BP estimation, showing strong potential for wearable health monitoring applications.
Diabetic foot is a prevalent and severe complication among diabetic patients, usually caused by sensory neuropathy and chronic mechanical stress overload. The structural characteristics of the tetrakaidecahedron porous structure are applied to insoles to optimize plantar pressure distribution, thereby minimizing abnormal plantar pressure in diabetic feet. Integrating plantar pressure zoning, finite element analysis, Grasshopper parametric modeling, and 3D printing technology, a customized pressure-relief insole for diabetic feet has been designed and validated using static standing plantar-pressure measurements. The insole employs a porous structure with adjustable porosity and specified regional elastic modulus to achieve customized plantar pressure relief. The designed insole (NPSI) increases the plantar contact area by approximately 30 % and reduces peak contact pressure by over 47 % in the high-pressure regions of M and H zones. The method proposed in this study effectively customizes pressure-relief insoles for diabetic feet, reducing the incidence and progression of diabetic foot ulcers. This approach is also applicable to the design of other assistive medical devices that require specific support and pressure relief.
An effective automatic system for ventricular segmentation from MRI is vital for diagnosing cardiovascular diseases, yet challenges persist due to anatomical variations and artifacts. We preprocess cardiac MRI with min-max normalization, then propose a hybrid segmentation network (ResFAU-net) integrating residual blocks, attention gates, and a Fused Accumulation Bridge module to delineate ventricle boundaries. The segmented regions are classified by the HAMC3 model, which combines cascaded capsule networks, CNNs, and hierarchical attention, with parameters optimized via the Coati Optimization Algorithm (COA). Rigorous assessment on our CMRI dataset using metrics (Dice, IoU, accuracy, precision, etc.) demonstrates the model's high performance in segmenting and classifying the left and right ventricles achieving an IoU of 96.29 % and accuracy of 99.03 %. The proposed ResFAU-net and HAMC3 framework offers a robust, end-to-end solution for precise ventricular cardiac analysis, demonstrating strong potential to automate and enhance the efficiency of cardiovascular diagnosis in clinical MRI workflows.
The objective of this study was to develop and characterize a novel low-cost, flexible sensor system for ground reaction force (GRF) measurements for biomedical applications. The system aims to provide GRF measurements across customizable areas up to 2 m2, suitable for integration into various medical and rehabilitation devices. The sensor system was constructed using multiple discrete resistive sensor modules. Each module had a quadratic shape and an edge length of 7.5 cm. The system utilized ESD packing-foam as resistive sensing material and conductive textile as electrodes. Measurements were conducted using an Arduino Nano microcontroller, a Wheatstone bridge circuit and analogue multiplexers. A demonstrator, integrating the sensor modules in a sports mat was built to show the functionality. The proposed system was capable of measuring forces up to 330 N. The sensor modules have an exponential force-resistance characteristic curve and showed inter-module and inter-day variability in the range of commercially available sensor systems' accuracy. The demonstrator enabled to visualize changes in weight distribution on its surface. The developed sensor system offers a reliable, flexible, and low-cost solution for GRF analysis in biomedical applications, providing data e.g. for rehabilitation feedback.
Diabetic Retinopathy (DR) causes major vision loss, requiring precise segmentation of retinal vessels and the Foveal Avascular Zone (FAZ). Accurate structural masks enable quantitative biomarkers that support early diagnosis and long-term monitoring. We propose a Retinal Graph Neural Network (RGNNNet) for OCTA segmentation. It combines multi-scale feature extraction with a graph representation, where node relations derive from an affinity matrix of feature maps. A symmetric normalization strategy stabilizes graph propagation and integrates local-global vascular context. A hybrid Dice-Focal loss refines fine-structure segmentation. On OCTA-500, RGNNNet achieved superior Dice and IoU to existing methods. For FAZ, it attained Dice values of 96.78 % (6 mm) and 98.02 % (3 mm), and maintained 0.915 on ROSE-0 without retraining. It outperformed baselines by 1-3 % Dice for other classes and remained lightweight (0.83 M params, 11.25 ms per 400 × 400 image). By coupling residual feature learning with graph-based relational reasoning, RGNNNet provides accurate structure-specific masks that can serve as a foundation for downstream biomarker extraction. Its compact design and stable generalization highlight its potential for large-scale ophthalmic screening and integration into clinical workflows.
Patient-specific 3D-printed jigs improve surgical outcomes, yet their use in high tibial osteotomy (HTO) lacks widespread acceptance due to cost-related scepticism and workflow adaptation challenges. This work aims to facilitate the adoption of 3D printed patient-specific instrumentation by demonstrating the precision of jigs produced using affordable resin 3D printing. Full-length tibial CT scans were used for 3D modelling, virtual HTO planning and designing of patient-specific jigs. The jigs were 3D printed using a ∼$550 resin printer, whereas the bones were printed in a ∼$600 filament printer. Achieved vs. planned corrections were compared using the 3D scanning superimposition method. Accuracy was assessed with paired t-tests, Bland-Altman plots, linear regression, and two one-sided t-tests (TOST). For Medial Proximal Tibial Angle (MPTA), the mean error was -0.05° ± 1.32° with no systematic bias (p=0.912), whereas for Posterior Proximal Tibial Angle (PPTA), it was 0.57° ± 0.38°, having a significant over-correction (p=0.004). Strong to excellent correlations were observed (R2: 0.77 for MPTA, 0.99 for PPTA). Corrections were equivalent within ±1° (TOST: p=0.042 and p=0.007). Affordable 3D-printed jigs could achieve acceptable corrections in a preclinical simulation setting, offering cost-effective preoperative planning and surgical training.
Existing diaphragm pacing (DP) system use an open-loop control method with a fixed stimulation mode to control breathing. It requires doctors to manually adjust stimulation parameters to meet the patient's ventilation needs. A neural network adaptive controller was tested to control breathing in DP system. For the diaphragm motion model, the respiratory airflow of healthy adults and rabbits was collected and compared with the simulation calculation results to verify the accuracy of the model. The performance of the adaptive controller was evaluated comparatively with that of the PID controller. Adaptive controller consists of a neural network and adaptively adjusts stimulation parameters to produce the desired respiratory volume waveform. Superiority of the output performance of the adaptive controller was further studied by setting various diaphragm model parameters. We further verified the feasibility of the adaptive controller through animal testing. The adaptive controller is better than the PID controller in maintaining the stability of the desired breath volume. Application of the adaptive controller can reduce the root mean square (RMS) error between the desired breath volume and the actual value to less than 6 %. This study demonstrates the potential application of adaptive controllers in closed-loop DP systems.
Accurate liver and tumor segmentation from CT is fundamental for diagnosis, treatment planning, and longitudinal monitoring of liver cancer. Although U-Net variants with popular encoder backbones are widely used, the coupled effects of encoder selection, training duration, and computational cost, as well as comparisons against volumetric architectures such as V-Net, remain insufficiently standardized. We propose a unified benchmarking framework that evaluates a family of multi-encoder 2D U-Net models together with an optional 3D V-Net baseline under the same preprocessing, input construction, and 3-fold cross-validation protocol on LiTS17. Multiple backbones (VGG16/VGG19/ResNet34/ResNet50/ResNet101/MobileNetV2) are assessed under 15/50/100-epoch schedules, and performance is reported using overlap and detection metrics (Dice, IoU, precision, recall) alongside efficiency indicators (training time and model complexity when available) to characterize the accuracy-efficiency trade-off. Results show liver segmentation rapidly reaches near-ceiling performance across models, while tumor segmentation benefits markedly from longer training and stronger encoders, especially for small or low-contrast lesions. Overall, the study provides a reproducible protocol and practical guidance for selecting segmentation models that balance accuracy, robustness, and deployment cost.
To predict abnormal pulmonary artery hemodynamics caused by ventricular septal defect (VSD) using Physics-Informed Neural Networks (PINN) and address the challenges of high computational cost in traditional Computational Fluid Dynamics (CFD) and difficulty in obtaining measurement data. The PINN model was trained using boundary conditions and scattered clinical CFD data, with dynamic weighting factors incorporated to enhance training efficiency and optimize predictions. Model outputs for blood flow velocity and pressure were subsequently evaluated against CFD simulation results. The PINN accurately reproduced velocity and pressure fields across pulmonary artery models using only boundary conditions and sparse internal measurements. For velocity prediction, the average RMSE, MAE, and MRE for components u, v, and w ranged from 0.274 to 0.832 %, 0.448-1.096 %, and 0.833-1.341 %, respectively. For pressure prediction, the average RMSE, MAE, and MRE ranged from 2.953 to 5.145 %, 3.264-5.679 %, and 0.376-0.565 %, respectively. These findings demonstrate that the framework generalizes well and provides reliable hemodynamic estimation with limited input data. The PINN model compensates for incomplete measurement data through physical constraints, enabling rapid and accurate prediction of pulmonary artery hemodynamics and offering a promising non-invasive alternative for pulmonary artery pressure measurement.
Anastomotic stenosis in arteriovenous fistulas (AVFs) is a significant issue for hemodialysis patients. This study uses computational fluid dynamics (CFD) simulations to evaluate the effects of different AVF configurations, comparing the RADAR technique with conventional AVF configurations in terms of hemodynamics, flow disturbances, and wall shear stress (WSS). Echo-color Doppler (ECD) imaging and CFD simulations assessed disturbed hemodynamics in different AVF configurations. Large eddy simulations (LES) captured turbulence transition at the anastomosis. Hemodynamic parameters, including velocity distribution, vortex formation, WSS, wall displacement, and stress distribution, were analyzed. A one-way fluid-structure interaction (FSI) approach was used to compute fluid-induced wall forces and assess stress distribution and deformation. The RADAR configuration showed superior hemodynamic performance with higher blood flow velocity, reduced turbulence, and a more favorable WSS environment, potentially reducing stenosis risk and improving long-term patency. Higher venous inner wall stress in RADAR configurations may aid vascular remodeling. Optimizing AVF configurations and anastomosis angles can enhance AVF functionality, reduce complications, and improve hemodialysis outcomes for patients with end-stage renal disease. The RADAR technique may improve AVF maturation and reduce complications.
This paper presents a systematic literature research and review of ventilator systems developed during the COVID-19 pandemic. Peer-reviewed journal and conference articles published through January 16th, 2025 were screened, and eligible systems were classified by actuation principle. Performance criteria were derived from Emergency Use Authorization requirements and used to generate a score-based ranking for each class. Performance was analyzed within and across classes to identify the situations in which each actuation principle is most advantageous. As an indicator of study quality, we evaluated the testing modalities reported for each device. Valve-based ventilators emerged as the most mature class in terms of ventilation functionality and testing. An emerging class of bag-based systems performed remarkably well compared with established valve- and blower-based designs. Across all three classes, the most frequent shortcomings concerned oxygen dosage of the inspired gas and the implementation of monitoring and alarm functions. Finally, we provide recommendations on development processes, testing procedures, and mitigation of supply-chain vulnerabilities that may support ventilator development in future pandemics.
To evaluate a multimodal deep learning model integrating preoperative transvaginal ultrasound (TVUS)-based radiomics features and clinical indicators for predicting 1-year postoperative recurrence of endometrial polyps (EP) after hysteroscopic polypectomy. A total of 116 patients with pathologically confirmed EP were assigned to a training cohort (n=81) and validation cohort (n=35). Radiomics features were extracted from TVUS images, and deep learning features were obtained using ResNet-based networks. These features, with clinical variables, were combined to build a multimodal model. Feature selection in the training cohort used reproducibility filtering (intraclass correlation coefficient [ICC] >0.80), univariate analysis, Pearson correlation (|r|>0.90), and least absolute shrinkage and selection operator (LASSO) regression. Model performance was evaluated by receiver operating characteristic (ROC) curves, area under the curve (AUC), calibration, and decision curve analysis (DCA). The multimodal model achieved AUCs of 0.941 (95 % CI: 0.897-0.985) and 0.922 (95 % CI: 0.852-0.992) in training and validation cohorts, outperforming clinical (0.812, 0.791) and radiomics-only models (0.871, 0.843). DeLong tests were significant (p<0.05). DCA showed higher clinical net benefit. This multimodal model effectively predicts 1-year recurrence after hysteroscopic EP resection, supporting individualized postoperative management.
Cardiopulmonary bypass (CPB) and extracorporeal membrane oxygenation (ECMO) or life support (ECLS) circuits are built from polymers and might release polymeric micro- and nanoparticles (MNP) into the circulation. MNPs seem to provoke inflammation, oxidative stress, and apoptosis, which are also side effects of extracorporeal circulation. Thus, we investigated the MNP release from CPB and ECMO/ECLS circuits. A CPB and ECMO/ECLS circuit was filled with saline solution, and circulation was initiated for 5 h and 7 d, respectively. Samples were taken from both circuits and filtered through a silicon membrane. MNPs were detected and quantified using optical microscopy and micro Raman spectroscopy. During circulation, polyvinyl chloride (PVC) and polymethyl methacrylate (PMMA) were detected in the CPB perfusate. After 5 h of circulation, polyethylene terephthalate (PET) was detected. In the ECMO/ECLS circuit, time-dependent accumulation of polymeric fragments was detected. Finally, particles of polyethylene (PE), polystyrene (PS), PET, and PVC were identified. The particle-size distribution extended from initially <2 µm to finally >10 µm with increasing circulation time. CPB and ECMO/ECLS circuits release MNPs. The number of MNPs increases over the period of use. A larger number of circuits and of health effects of identified MNPs, should be investigated.
Sit-to-stand (STS) exercises are commonly incorporated in functional rehabilitation due to their simplicity, relevance to daily mobility and more recently, cardiac fitness. While generally considered safe for most clinical populations, its effect on autonomic stability remains underexplored - particularly in those with autonomic vulnerability such as individuals with amyotrophic lateral sclerosis (ALS). This study investigates the suitability of STS exercises for individuals with ALS, with specific focus on establishing baseline heart rate variability (HRV) data during rest and transient STS movement. Heart rate (HR) and HRV (RMSSD and HF) were assessed across three cohorts; healthy young adults (n=29), individuals living with ALS (n=8), and their age-matched controls (n=9), under resting condition and two STS protocols: Timed up and go (TUG) and five times sit-to-stand test (FTSST). All groups exhibited significant increase in mean HR during STS compared to rest (p<0.05), whereas no statistically significant differences were observed in RMSSD and HF. These results indicate that STS exercises elicit measurable cardiovascular exertion without triggering acute autonomic dysfunction in ALS individuals, supporting its role in safe rehabilitation for early-mid stages ALS. HRV serves as a potential tool for non-invasive monitoring and assessment of autonomic function during physical therapy.
Soft-tissue knee abnormalities are common, yet first-line radiography provides limited soft-tissue contrast, whereas MRI or arthroscopy is more resource-intensive. We developed DeepKneeXR as a single-center, retrospective proof-of-concept AI workflow for generating probability scores for key knee abnormalities from anterior-posterior knee X-rays. This retrospective study included 3,200 adult patients selected from 5,000 initially screened cases at one medical center after predefined exclusions. Reference labels were assigned using a composite clinical-imaging standard based on clinical history, physical examination, MRI findings, and arthroscopy when clinically indicated. A unified YOLOv8 model was trained to perform knee localization and multi-label probability prediction in a single forward pass. The model generated a knee bounding box and probability scores for meniscus tears (MENI), medial collateral ligament injuries (MCL), and joint effusion (EFFU). DeepKneeXR achieved excellent knee localization (mAP@0.5=0.995). Multi-label screening performance was moderate and should be interpreted as preliminary, particularly for EFFU, whose validation AUC was limited. This proof-of-concept study shows that a unified YOLOv8 model can generate knee-localization outputs and abnormality probability scores from AP radiographs. However, prospective multi-center validation, standardized reference labeling, and clinician-facing workflow evaluation are required before clinical use can be considered.
To investigate how phase composition influences the physicochemical properties, Sr2+ ion release behavior, and cytocompatibility of Strontium (Sr)-doped calcium phosphate (CaP) materials, focusing on Sr-HA, Sr-β-TCP, and three Sr-BCP compositions. This work focuses on Sr-HA, Sr-β-TCP, and Sr-BCP powders with HA/β-TCP ratios (60:40 (BCP1), 30:70 (BCP2), and 20:80 (BCP3)) that were synthesized by wet chemical precipitation followed by calcination. The effect of CaP phase compositions on physicochemical characteristics, Sr2+ release, and cytocompatibility was investigated by using ICP-OES, FTIR, XRD, SEM-EDX, and MTT assays. EDX confirmed the Ca/P ratios, and both FTIR and XRD indicated successful phase formation without secondary phases. The Sr-BCP samples demonstrated enhanced cell viability after 48 h in MTT assays, highlighting biological responses associated with the biphasic structure. ICP-OES analysis indicated composition-dependent Sr2+ release, with Sr-BCP1 showing the highest initial and sustained ion release. Sr-BCP1 offers a promising balance between structural stability, favorable cytocompatibility, and controlled Sr2+ ion delivery, supporting its potential for bone applications.
Existing medical image generation tasks primarily employ Generative Adversarial Networks (GANs), which perform poorly on datasets with temporal characteristics and suffer from slow generation speed and mode collapse. In response to this question, this study puts forward a temporal conditional diffusion model based on a dual U-Net structure, which leverages the dual U-Net to extract rich detail information within a denoising diffusion framework while incorporating temporal information as a condition to guide the generation of 4D cardiac datasets with temporal features. Additionally, a deformation field is utilized to accelerate medical image generation. Experimental results show that compared to existing methods, the proposed approach can generate dynamic scan time frames while maintaining strong continuity and temporal consistency in both transverse and longitudinal spatial dimensions. In addition, the synthesized images are highly similar to those captured in reality. The proposed method effectively preserves anatomical structural details, making it highly suitable for medical image generation tasks.