Biomedizinische Technik. Biomedical engineering最新文献

Objectives: An effective automatic system for ventricular segmentation from MRI is vital for diagnosing cardiovascular diseases, yet challenges persist due to anatomical variations and artifacts.

Methods: 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).

Results: 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 %.

Conclusions: 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.

Objectives: Hypertensive intracerebral hemorrhage (HICH) is a frequently encountered and highly lethal cerebrovascular disorder, and postoperative rebleeding remains one of its most feared complications.

Methods: We developed a nomogram featuring a hybrid architecture that integrates radiomics-derived signatures and deep learning representations from CT imaging together with routine clinical parameters, with the goal of enhancing the individualized prediction of rebleeding risk following surgical intervention in HICH patients. A total of 151 individuals diagnosed with HICH were prospectively enrolled and randomly assigned to a training set (n=105) and a validation set (n=46) following a 7:3 ratio.

Results: The resulting model outperformed single-domain approaches relying solely on traditional clinical indicators or deep learning features. In the training cohort, the nomogram yielded an AUC of 0.993 (95 % CI: 0.982-1.000), while in the internal testing cohort, the AUC reached 0.860 (95 % CI: 0.745-0.974). The model highlighted several key predictors associated with postoperative rebleeding.

Conclusions: Overall, the integrated nomogram, embedding clinical data, radiomic phenotypes, and deep learning markers, exhibited robust predictive capability in assessing rebleeding risk among patients with HICH. Ongoing research is needed to further refine and validate the model in broader clinical settings.

Objectives: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Objectives: Accurate identification of Parkinson's disease (PD), particularly during its prodromal stage, remains a major clinical challenge due to heterogeneous symptom presentation and overlapping neurological patterns. This study proposes an LLM-Guided Multimodal Attention Network (LLM-MAN) to improve PD staging by jointly modeling structural MRI and clinical/cognitive metadata.

Methods: We develop a unified multimodal framework that encodes structural MRI using a ResNet-18 backbone enhanced with Convolutional Block Attention Modules (CBAM) for discriminative neuroimaging feature extraction, and represents clinical/cognitive metadata using an LLM-based text encoder (pre-trained BERT) for contextualized semantic modeling. A Meta-Guided Cross-Attention (MGCA) module is introduced to align clinical semantic knowledge with imaging features, enabling robust cross-modal fusion for multiclass classification (Normal Control, prodromal PD, and diagnosed PD). The model is evaluated on the Parkinson's Progression Markers Initiative (PPMI) dataset and further validated on an independent external cohort.

Results: On the PPMI dataset, LLM-MAN achieved an accuracy of 95.68 % for distinguishing Normal Control, prodromal PD, and diagnosed PD. External validation on an independent cohort yielded 94.10 % accuracy, indicating strong generalization performance across datasets.

Conclusions: LLM-guided multimodal fusion via MGCA provides reliable and interpretable approach for PD staging, substantially improving prodromal PD identification by integrating semantic clinical knowledge with neuroimaging representations.

Objectives: 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.

Methods: 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.

Results: 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.

Conclusions: Sr-BCP1 offers a promising balance between structural stability, favorable cytocompatibility, and controlled Sr²⁺ ion delivery, supporting its potential for bone applications.

Sit-To-Stand (SitTS) training is often performed by patients who may require arm assistance to perform it successfully. This study examines the role of arms in performing the SitTS maneuver to better understand their contribution and clinical significance. This review synthesizes the reported outcome measures of arm-assisted SitTS exercise programs in people with physical impairments. Web of Science (WoS) and Scopus™ databases were searched for relevant studies up to May 2025. Inclusion and exclusion criteria were used to screen the titles and abstracts of articles identified using key search terms related to SitTS activity and arm support. Only articles that met the key terms were extracted and analyzed in this study. A total of 47 studies reported arm support measures during SitTS, either in clinical or biomechanical contexts. These studies highlighted various methods and tools used to assess the body's state during the rising movement. Joint angle and SitTS duration are clinically applicable and more easily measured compared to seat, foot, and arm forces which require specialized instrumentation. Balancing clinical measures with detailed biomechanical assessments is important in enabling practitioners and researchers to evaluate and enhance SitTS interventions that involve arm assistance, ultimately improving rehabilitation outcomes.

Objectives: 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.

Methods: 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).

Results: 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.

Conclusions: HRV serves as a potential tool for non-invasive monitoring and assessment of autonomic function during physical therapy.

Objectives: 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.

Methods: 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.

Results: 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.

Conclusions: The ResNet-BiGRU model demonstrates accurate, robust, and generalizable cuffless BP estimation, showing strong potential for wearable health monitoring applications.

Objectives: 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).

Methods: 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.

Results: 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.

Conclusions: 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.

Objectives: 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.

Methods: 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.

Results: 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.

Conclusions: The improved LC allows the safe handling of MP (weight ≤127 g), enabling the use of the CrocoGrip for a broader range of tasks.