Biomedizinische Technik. Biomedical engineering最新文献

Objectives: One of the primary causes of the women death is breast cancer. Accurate and early breast cancer diagnosis plays an essential role in its treatment. Computer Aided Diagnosis (CAD) system can be used to help doctors in the diagnosis process. This study presents an efficient method to performance improvement of the breast cancer diagnosis CAD system using thermal images.

Methods: The research strategy in the proposed CAD system is using efficient algorithms in feature extraction and classification phases, and new efficient feature selection algorithm. In the feature extraction phase, the Segmentation Fractal Texture Analysis (SFTA) algorithm that is a texture analysis algorithm is used.This algorithm utilizes two-threshold binary decomposition. In the feature selection phase, the developed feature selection algorithm, which is hybrid of binary grey wolf optimization algorithm and firefly optimization algorithm, is applied to extracted features. Then, the kNN, SVM, and DTree classification techniques are applied to check whether the selected features are efficiently discriminated the group successfully with minimal misclassifications.

Results: The DMR database is utilized for performance evaluation of the proposed method. The results indicate that the obtained accuracy, specificity, sensitivity, and MCC are 97, 96, 98, and 94.17 %, respectively.

Conclusions: The developed breast cancer diagnosis method has advantages compared to other breast cancer diagnosis using thermal images.

Objectives: Helical plating is an established method for treating proximal humeral shaft fractures, mitigating the risk of iatrogenic radial nerve damage. However, biomechanical test data on helical plates under physiological load condition is limited. Hence, the aim of this study was to compare the biomechanical performance of helical and straight PHILOS^® Long plates in AO12C2 fractures using static and cyclic implant system testing.

Methods: Helical and straight PHILOS^® Long plates on artificial bone substitutes were tested under physiological axial static (n=6) and cyclic loading (n=12). The axial construct stiffness was the main parameter for comparing the biomechanical performance of the two groups. Mimicking a clinical scenario, the helical deformation was performed consecutively by an experienced surgeon using iron bending tools. The torsional angle was determined computationally from 3D-scanning models afterwards.

Results: Helical plating resulted in a significantly reduced axial construct stiffness in all test scenarios compared to conventional straight plating (static testing: p=0.012; cyclic testing: p≤0.010). No failure occurred within the range of physiological loading in both groups.

Conclusions: Helical plating favors multidimensional deformation of the test sample in lateral-ventral direction under axial loading, resulting in a reduced axial construct stiffness and in an increased interfragmentary movement. No biomechanical failure is to be expected within physiological load boundaries.

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.

Objectives: 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. 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: This study aims to develop a multimodal deep learning-based algorithm for detecting specific fetal heart rate (FHR) events, to enhance automatic monitoring and intelligent assessment of fetal well-being.

Methods: We analyzed FHR and uterine contraction signals by combining various feature extraction techniques, including morphological features, heart rate variability features, and nonlinear domain features, with deep learning algorithms. This approach enabled us to classify four specific FHR events (bradycardia, tachycardia, acceleration, and deceleration) as well as four distinct deceleration patterns (early, late, variable, and prolonged deceleration). We proposed a multi-model deep neural network and a pre-fusion deep learning model to accurately classify the multimodal parameters derived from Cardiotocography signals.

Results: These accuracy metrics were calculated based on expert-labeled data. The algorithm achieved a classification accuracy of 96.2 % for acceleration, 94.4 % for deceleration, 90.9 % for tachycardia, and 85.8 % for bradycardia. Additionally, it achieved 67.0 % accuracy in classifying the four distinct deceleration patterns, with 80.9 % accuracy for late deceleration and 98.9 % for prolonged deceleration.

Conclusions: The proposed multimodal deep learning algorithm serves as a reliable decision support tool for clinicians, significantly improving the detection and assessment of specific FHR events, which are crucial for fetal health monitoring.

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.