Biomedizinische Technik. Biomedical engineering最新文献

Objectives: Dental caries is a prevalent oral health issue around the world that leads to tooth aches, root canal infections, and even tooth extractions. Existing dental caries diagnosis models may misdiagnose the disorder and take more time to segment the caries. This research work aims to provide an in-depth analysis of spatial and channel attention mechanism techniques used for semantic segmentation in an encoder-decoder network. For effective performance, the research implements novel techniques to segment the dental caries accurately.

Methods: Deep Fully Connected Residual Block (DFCR) is designed to provide relevant features without the loss of significant information. A novel Hybrid Spatial Channel Attention (HSCA) module is developed for combining significant features with the help of multi-scale spatial features and cross-dimensional channel features.

Results: The proposed methodology performs better than other cutting-edge algorithms by achieving 96.63 % accuracy, 95.77 % dice score, 96.28 % Intersection over Union (IOU) score for the caries dataset, and 96.93 % accuracy, 95.21 % dice value, and 96.1 % IOU for the Tufts dental dataset.

Conclusions: The developed model facilitates in detection of cavities precisely at an earlier stage with the help of dental images. The semantic segmentation of dental caries provides accurate diagnosis by assisting medical professionals.

Objectives: The aim of this study was to clarify the applied initial forces and moments by different aligners of various materials and manufacturing methods.

Methods: The finite-element-method was used to analyze the forces and moments generated by the aligners on a maloccluded tooth. Plaster models of dental arches with a mesiorotated tooth 11 were fabricated, digitized and virtually analyzed. Four types of aligners with various layer thicknesses were selected: two splints with novel shape memory properties: a printable aligner made of the resin Tera Harz TC-85 DAC (Graphy Inc., South Korea) and a self-manufactured aligner consisting of the components polypropylene carbonate and thermoplastic polyurethane. The other two aligners were conventional, thermoformable aligners: CA^® Pro Clear Aligner (Scheu Dental GmbH, Germany) and Erkodur-al (Erkodent Erich Kopp GmbH, Germany).

Results: The force and moment analyses showed that the thermoformable CA^® Pro Clear Aligner exhibited the highest values. The thermoformed Erkodur-al aligner showed the lowest force loads for all layer thicknesses. The Graphy printed splint showed similar results compared to Erkodur-al at layer thicknesses of 0.40 mm and 0.50 mm.

Conclusions: To avoid periodontal overloading, aligners with lower force and moment delivery should be chosen for this type of tooth movement.

Objectives: In recent years, significant progress has been made in the research of gesture recognition using surface electromyography (sEMG) signals based on machine learning and deep learning techniques. The main motivation for sEMG gesture recognition research is to provide more natural, convenient, and personalized human-computer interaction, which makes research in this field have considerable application prospects in rehabilitation technology. However, the existing gesture recognition algorithms still need to be further improved in terms of global feature capture, model computational complexity, and generalizability.

Methods: This paper proposes a fusion model of Squeeze-and-Excitation Networks (SE) and DenseNet, inserting attention mechanism between DenseBlock and Transition to focus on the most important information, improving feature representation ability, and effectively solving the problem of gradient vanishing.

Results: This proposed method was tested on the electromyographic gesture datasets NinaPro DB2 and DB4, achieving accuracies of 85.93 and 82.39 % respectively. Through ablation experiments, it was found that the method based on DenseNet-101 as the backbone model produced the best results.

Conclusions: Compared with existing models, this proposed method has better robustness and generalizability in gesture recognition, providing new ideas for the development of sEMG signal gesture recognition applications in the future.

Objectives: The aim of the present study was to investigate, whether polyetherketoneketone (PEKK) secondary crowns could be considered as alternative to gold standard in terms of their physical properties and manufacturing costs.

Methods: An upper jaw model with six implants was used. Frameworks with either 6 PEKK- or 6 electroplated secondary crowns were cemented in a wear simulator. A total of 20 specimens (10 PEKK, 10 gold) run 10,000 cycles in the wear simulator with a lubricant. Additionally, 10,000 cycles in the thermocycling baths with 5 °C and 55 °C have gone through, before running extra 10,000 cycles in the wear simulator again. Finally, the abutments were analysed for signs of wear under the electron microscope.

Results: The mean pulling out force value for PEKK was 21 N. For the electroplated gold secondary crowns an average of 19 N was measured. Multiple fluctuations were observed in the gold series of tests. After 20,000 cycles in the wear simulator and 10,000 cycles in the thermocycling machine, there were no major losses to be measured in terms of wear for both materials. In the microscopic analysis of the abutments, traces of wear could be seen in pull-out direction, mainly in the gold samples.

Conclusions: PEKK secondary crowns have lower costs, more stable retention force values and are easier to produce than the gold standard. On average, the pull-out force values were 11 N higher than recommended.

Globally, the prevalence of stroke is significant and increasing annually. This growth has led to a demand for rehabilitation services that far exceeds the supply, leaving many stroke survivors without adequate rehabilitative care. In response to this challenge, this study introduces a portable exoskeleton system that integrates neural control mechanisms governing human arm movements. This design leverages neuroplasticity principles to simulate natural movements, aiming to reactivate and strengthen neuromuscular connections and thus enhance rehabilitation outcomes. A tailored musculoskeletal model of the human arm and an associated cost function were developed to accurately replicate the planar motion trajectories of a healthy human arm across 32 directions. The application of a Proportional-Derivative (PD) controller enables precise tracking of these trajectories by the exoskeleton. Individual testing has demonstrated high consistency between the exoskeleton-driven motion paths and the simulated trajectories, especially in trajectory accuracy along the X and Y axes. These findings support the efficacy of integrating advanced neural control strategies with practical exoskeleton designs in stroke rehabilitation.

Objectives: Diabetic retinopathy (DR) is associated with long-term diabetes and is a leading cause of blindness if it is not diagnosed early. The rapid growth of deep learning eases the clinicians' DR diagnosing procedure. It automatically extracts the features and performs the grading. However, training the image toward the majority of background pixels can impact the accuracy and efficiency of grading tasks. This paper proposes an auto-thresholding algorithm that reduces the negative impact of considering the background pixels for feature extraction which highly affects the grading process.

Methods: The PSO-based thresholding algorithm for retinal segmentation is proposed in this paper, and its efficacy is evaluated against the Otsu, histogram-based sigma, and entropy algorithms. In addition, the importance of retinal segmentation is analyzed using Explainable AI (XAI) to understand how each feature impacts the model's performance. For evaluating the accuracy of the grading, ResNet50 was employed.

Results: The experiments were conducted using the IDRiD fundus dataset. Despite the limited data, the retinal segmentation approach provides significant accuracy than the non-segmented approach, with a substantial accuracy of 83.70 % on unseen data.

Conclusions: The result shows that the proposed PSO-based approach helps automatically determine the threshold value and improves the model's accuracy.

Objectives: The actions and decisions of pilots are directly related to aviation safety. Therefore, understanding the neurological and cognitive processes of pilots during flight is essential. This study aims to investigate the EEG signals of pilots to understand the characteristic changes during the climb and descent stages of flight.

Methods: By performing wavelet packet decomposition on the EEG signals, we examined EEG maps during these critical phases and analyzed changes in signal intensity. To delve deeper, we calculated the log-transformed power of electroencephalograms to investigate the EEG responses under different flight conditions. Additionally, we conducted EEG spectral coherence analysis to evaluate the degree of synchronization between different electrodes during climb and descent.

Results: This analysis helps us understand the functional connectivity changes in various brain regions during these phases. Understanding these complex interactions is crucial, as it provides insights into the cognitive processes of pilots during the critical climb and descent stages of flight, contributing to enhanced aviation safety.

Conclusions: By identifying how brain activity fluctuates during these phases, we can better comprehend pilots' decision-making processes, ultimately leading to the development of more effective training programs and safety protocols. This research underscores the importance of neurological studies in safety.

Objectives: Nonenzymatic biosensor-based-conductive polymers like polyaniline are highly electrochemically stable, cheap, and easy to synthesize biosensors, which is the main objective of research as well as testing applied in different pH conditions to get optimum sensitivity.

Methods: A nonenzymatic glucose biosensor based on polyaniline was electrochemically deposited on a glassy carbon electrode; the cyclic voltammetry under range applied voltage -0.2 to 1.2 V vs. Ag/AgCl was employed to synthesize the biosensor electrode.

Results: The polyaniline biosensor electrode properties were characterized, and the morphology surface photographic confirmed mesoporous architecture with many accessible pores, while chemical bonding analysis confirmed the synthesis of polyaniline. The initial investigation examined the pH levels of phosphate-buffered saline, including 5, 5.5, 6, and 6.5. The cyclic voltammetry measurement revealed that the pH=5.5 provides excellent sensitivity toward glucose detection. The sensitivity of pH=5.5 is 68.7 μA mM^-1 cm^-2, and the low detection limit is 1 µM.

Conclusions: The findings above indicate that the biosensor could be an excellent candidate for application in electrochemical glucose sensing under pH=5.5 conditions of phosphate-buffered saline.

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.