Biomedizinische Technik. Biomedical engineering最新文献

Objectives: No standard, objective diagnostic procedure exists for most neurological diseases causing tremors. Therefore, drawing tests have been widely analyzed to support diagnostic procedures. In this study, we examine the comparison of Archimedean spiral and line drawings, the possibilities of their joint application, and the relevance of displaying pressure on the drawings to recognize Parkinsonism and cerebellar dysfunction. We further attempted to use an automatic processing and evaluation system.

Methods: Digital images were developed from raw data by adding or omitting pressure data. Pre-trained (MobileNet, Xception, ResNet50) models and a Baseline (from scratch) model were applied for binary classification with a fold cross-validation procedure. Predictions were analyzed separately by drawing tasks and in combination.

Results: The neurological diseases presented here can be recognized with a significantly higher macro f1 score from the spiral drawing task (up to 95.7 %) than lines (up to 84.3 %). A significant improvement can be achieved if the spiral is supplemented with line drawing. The pressure inclusion in the images did not result in significant information gain.

Conclusions: The spiral drawing has a robust recognition power and can be supplemented with a line drawing task to increase the correct recognition. Moreover, X and Y coordinates appeared sufficient without pressure with this methodology.

Introduction: Individuals change walking speed by regulating step frequency (SF), stride length (SL), or a combination of both (FL combinations). However, existing methods of walking speed estimation ignore this regulatory mechanism.

Objectives: This paper aims to achieve accurate walking speed estimation while enabling adaptation to inter-individual speed regulation strategies.

Methods: We first extracted thigh features closely related to individual speed regulation based on a single thigh mounted IMU. Next, an interval type-2 fuzzy inference system was used to infer and quantify the individuals' speed regulation intentions, enabling speed estimation independent of inter-individual gait patterns. Experiments with five subjects walking on a treadmill at different speeds and with different gait patterns validated our method.

Results: The overall root mean square error (RMSE) for speed estimation was 0.0704 ± 0.0087 m/s, and the RMSE for different gait patterns was no more than 0.074 ± 0.005 m/s.

Conclusions: The proposed method provides high-accuracy speed estimation. Moreover, our method can be adapted to different FL combinations without the need for individualised tuning or training of individuals with varying limb lengths and gait habits. We anticipate that the proposed method will help provide more intuitive speed adaptive control for rehabilitation robots, especially intelligent lower limb prostheses.

Large defects in bone tissue due to trauma, tumors, or developmental abnormalities usually require surgical treatment for repair. Numerous studies have shown that current bone repair and regeneration treatments have certain complications and limitations. With the in-depth understanding of bone regeneration mechanisms and biological tissue materials, a variety of materials with desirable physicochemical properties and biological functions have emerged in the field of bone regeneration in recent years. Among them, hydrogels have been widely used in bone regeneration research due to their biocompatibility, unique swelling properties, and ease of fabrication. In this paper, the development and classification of hydrogels were introduced, and the mechanism of hydrogels in promoting bone regeneration was described in detail, including the promotion of bone marrow mesenchymal stem cell differentiation, the promotion of angiogenesis, the enhancement of the activity of bone morphogenetic proteins, and the regulation of the microenvironment of bone regeneration tissues. In addition, the future research direction of hydrogel in bone tissue engineering was discussed.

Objectives: Accurate preoperative differentiation between non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC) is crucial for surgical decision-making in bladder cancer (BCa) patients. MIBC diagnosis relies on the Vesical Imaging-Reporting and Data System (VI-RADS) in clinical using multi-parametric MRI (mp-MRI). Given the absence of some sequences in practice, this study aims to optimize the existing T2-weighted imaging (T2WI) sequence to assess MIBC accurately.

Methods: We analyzed T2WI images from 615 BCa patients and developed a multi-view fusion self-distillation (MVSD) model that integrates transverse and sagittal views to classify MIBC and NMIBC. This 3D image classification method leverages z-axis information from 3D MRI volume, combining information from adjacent slices for comprehensive features extraction. Multi-view fusion enhances global information by mutually complementing and constraining information from the transverse and sagittal planes. Self-distillation allows shallow classifiers to learn valuable knowledge from deep layers, boosting feature extraction capability of the backbone and achieving better classification performance.

Results: Compared to the performance of MVSD with classical deep learning methods and the state-of-the-art MRI-based BCa classification approaches, the proposed MVSD model achieves the highest area under the curve (AUC) 0.927 and accuracy (Acc) 0.880, respectively. DeLong's test shows that the AUC of the MVSD has statistically significant differences with the VGG16, Densenet, ResNet50, and 3D residual network. Furthermore, the Acc of the MVSD model is higher than that of the two urologists.

Conclusions: Our proposed MVSD model performs satisfactorily distinguishing between MIBC and NMIBC, indicating significant potential in facilitating preoperative BCa diagnosis for urologists.

Objectives: Since the quality of low dose CT (LDCT) images is often severely affected by noise and artifacts, it is very important to maintain high quality CT images while effectively reducing the radiation dose.

Methods: In recent years, the representation of diffusion models to produce high quality images and stable trainability has attracted wide attention. With the extension of the cold diffusion model to the classical diffusion model, its application has greater flexibility. Inspired by the cold diffusion model, we proposes a low dose CT image denoising method, called CECDM, based on the combination of edge enhancement and cold diffusion model. The LDCT image is taken as the end point (forward) of the diffusion process and the starting point (reverse) of the sampling process. Improved sobel operator and Convolution Block Attention Module are added to the network, and compound loss function is adopted.

Results: The experimental results show that CECDM can effectively remove noise and artifacts from LDCT images while the inference time of a single image is reduced to 0.41 s.

Conclusions: Compared with the existing LDCT image post-processing methods, CECDM has a significant improvement in all indexes.

Objectives: Dielectric materials play a crucial role in assessing and refining the measurement performance of dielectric properties for specific tasks. The availability of viable and standardized dielectric materials could greatly enhance medical applications related to dielectric properties. However, obtaining reliable phantoms with designated dielectric properties across a specified frequency range remains challenging. In this study, we propose software to easily determine the components of dielectric materials in the frequency range of 16 MHz to 3 GHz.

Methods: A total of 184 phantoms were fabricated and measured using open-ended coaxial probe method. The relationship among dielectric properties, frequency, and the components of dielectric materials was fitted through feedforward neural networks. Software was developed to quickly calculate the composition of dielectric materials.

Results: We performed validation experiments including blood, muscle, skin, and lung tissue phantoms at 128 MHz, 298 MHz, 915 MHz, and 2.45 GHz. Compared with literature values, the relative errors of dielectric properties are less than 15 %.

Conclusions: This study establishes a reliable method for fabricating dielectric materials with designated dielectric properties and frequency through the development of the software. This research holds significant importance in enhancing medical research and applications that rely on tissue simulation using dielectric phantoms.

Objectives: Discussion of a telemedical supervision system for anesthesiology in the operating room using the interoperable communication protocol SDC. Validation of a first conceptual demonstrator and highlight of strengths and weaknesses.

Methods: The system includes relevant medical devices, a central anesthesia workstation (AN-WS), and a remote supervision workstation (SV-WS) and the concept uses the interoperability standard ISO/IEEE 11073 SDC. The validation method involves a human patient simulator, and the system is tested in an intervention study with 16 resident anesthetists supervised by a senior anesthetist.

Results: This study presents a novel tele-supervision system that enables remote patient monitoring and communication between anesthesia providers and supervisors. It is composed of connected medical devices via SDC, a central AN-WS and a mobile remote SV-WS. The system is designed to handle multiple ORs and route the data to a single SV-WS. It enables audio/video connections and text chatting between the workstations and offers the supervisor to switch between cameras in the OR. Through a validation study the feasibility and usefulness of the system was assessed.

Conclusions: Validation results highlighted, that such system might not replace physically present supervisors but is able to provide supervision for scenarios where supervision is currently not available or only under adverse circumstances.

Objectives: COVID-19 is one of the recent major epidemics, which accelerates its mortality and prevalence worldwide. Most literature on chest X-ray-based COVID-19 analysis has focused on multi-case classification (COVID-19, pneumonia, and normal) by the advantages of Deep Learning. However, the limited number of chest X-rays with COVID-19 is a prominent deficiency for clinical relevance. This study aims at evaluating COVID-19 identification performances using adaptive histogram equalization (AHE) to feed the ConvNet architectures with reliable lung anatomy of airways.

Methods: We experimented with balanced small- and large-scale COVID-19 databases using left lung, right lung, and complete chest X-rays with various AHE parameters. On multiple strategies, we applied transfer learning on four ConvNet architectures (MobileNet, DarkNet19, VGG16, and AlexNet).

Results: Whereas DarkNet19 reached the highest multi-case identification performance with an accuracy rate of 98.26 % on the small-scale dataset, VGG16 achieved the best generalization performance with an accuracy rate of 95.04 % on the large-scale dataset.

Conclusions: Our study is one of the pioneering approaches that analyses 3615 COVID-19 cases and specifies the most responsible AHE parameters for ConvNet architectures in the multi-case classification.

Mechanomyography (MMG) may be used to quantify very small motor responses resulting from muscle activation, voluntary or involuntary. The purpose of this study was to investigate the MMG mean peak amplitude (MPA) and area under the curve (AUC) and the corresponding mechanical responses following delivery of transcranial magnetic stimulation (TMS) to the knee extensors. Fourteen adults (23 ± 1 years) received single TMS pulses at intensities from 30-80 % maximum stimulator output to elicit muscle responses in the relaxed knee extensors while seated. An accelerometer-based sensor was placed on the rectus femoris (RF) and vastus lateralis (VL) muscle bellies to measure the MMG signal. Pearson correlation revealed a positive linear relationship between MMG MPA and TMS intensity for RF (r=0.569; p<0.001) and VL (r=0.618; p<0.001). TMS intensity of ≥60 % maximum stimulator output produced significantly higher MPA than at 30 % TMS intensity and evoked measurable movement at the knee joint. MMG MPA was positively correlated to AUC (r=0.957 for RF and r=0.603 for VL; both p<0.001) and knee extension angle (r=0.596 for RF and r=0.675 for VL; both p<0.001). In conclusion, MMG captured knee extensor mechanical responses at all TMS intensities with the response increasing with increasing TMS intensity. These findings suggest that MMG can be an additional tool for assessing muscle activation.