Biomedizinische Technik. Biomedical engineering最新文献

Second-generation headless compression screws (HCSs) are commonly used for the fixation of small bones and articular fractures. However, there is a lack of biomechanical data regarding the application of such screws to radial head fractures. This study evaluated the mechanical properties of the fixation of radial head fractures using a single oblique HCS compared with those obtained using a standard locking radial head plate (LRHP) construct and a double cortical screw (DCS) construct. Radial synbone models were used for biomechanical tests of HCS, LRHP, and DCS constructs. All specimens were first cyclically loaded and then loaded to failure. The stiffness for the LRHP group was significantly higher than that for the other two groups, and that for the HCS group was significantly higher than that for the DCS group. The LRHP group had the greatest strength, followed by the HCS group and then the DCS group. The HCS construct demonstrated greater fixation strength than that of the commonly used cortical screws, although the plate group was the most stable. The present study revealed the feasibility of using a single oblique HCS, which has the advantages of being buried, requiring limited wound exposure, and having relatively easy operation, for treating simple radial head fractures.

The automatic segmentation of the abnormality region from the head MRI is a challenging task in the medical science domain. The abnormality in the form of the tumor comprises the uncontrolled growth of the cells. The automatic identification of the affected cells using computerized software systems is demanding in the past several years to provide a second opinion to radiologists. In this paper, a new clustering approach is introduced based on the machine learning aspect that clusters the tumor region from the input MRI using disjoint tree generation followed by tree merging. Further, the proposed algorithm is improved by introducing the theory of joint probabilities and nearest neighbors. Later, the proposed algorithm is automated to find the number of clusters required with its nearest neighbors to do semantic segmentation of the tumor cells. The proposed algorithm provides good semantic segmentation results having the DB index-0.11 and Dunn index-13.18 on the SMS dataset. While the experimentation with BRATS 2015 dataset yields Dice _complete=80.5 %, Dice _core=73.2 %, and Dice _enhanced=62.8 %. The comparative analysis of the proposed approach with benchmark models and algorithms proves the model's significance and its applicability to do semantic segmentation of the tumor cells with the average increment in the accuracy of around ±2.5 % with machine learning algorithms.

Objectives: Numerous studies indicate that deep breathing (DB) enhances wellbeing. Multiple deep breathing methods exist, but few employ audio to reach similar results. This study developed audio-guided DB and evaluated its immediate impacts on healthy population via self-created auditory Go/No-Go task, tidal volume changes, and psychological measures.

Methods: Audio-guided DB with natural sounds to guide the DB was developed. Meanwhile, audio-based Go/No-Go paradigm with Arduino was built to measure the attention level. Thirty-two healthy young adults (n=32) were recruited. Psychological questionnaires (Rosenberg's Self-Esteem Scale (RSES), Cognitive and Affective Mindfulness Scale-Revised (CAMS-R), Perceived Stress Scale (PSS)), objective measurements with tidal volume and attention level with auditory Go/No-Go task were conducted before and after 5 min of DB.

Results: Results showed a significant increment in tidal volume and task reaction time from baseline (p=0.003 and p=0.033, respectively). Significant correlations were acquired between (1) task accuracy with commission error (r=-0.905), (2) CAMS-R with task accuracy (r=-0.425), commission error (r=0.53), omission error (r=0.395) and PSS (r=-0.477), and (3) RSES with task reaction time (r=-0.47), task accuracy (r=-0.362), PSS (r=-0.552) and CAMS-R (r=0.591).

Conclusions: This pilot study suggests a link between it and young adults' wellbeing and proposes auditory Go/No-Go task for assessing attention across various groups while maintaining physical and mental wellness.

Objectives: Fatigue has a considerable impact on the driver's vehicle and even the driver's own operating ability.

Methods: An intelligent algorithm is proposed for the problem that it is difficult to classify the degree of drowsiness generated by the driver during the driving process. By studying the driver's electrocardiogram (ECG) during driving, two models were established to jointly classify the ECG signals as awake, stress, and fatigue or drowsiness states for drowsiness levels. Firstly, the deep learning method was used to establish the model_1 to predict the drowsiness of the original ECG, and model_2 was developed using the combination of principal component analysis (PCA) and weighted K-nearest neighbor (WKNN) algorithm to classify the heart rate variability characteristics. Then, the drowsiness prediction results of the two models were weighted according to certain rules, and the hybrid learning model combining dilated convolution and bidirectional long short-term memory network with PCA and WKNN algorithm was established, and the mixed model was denoted as DiCNN-BiLSTM and PCA-WKNN (DBPW). Finally, the validity of the DBPW model was verified by simulation of the public database.

Results: The experimental results show that the average accuracy, sensitivity and F1 score of the test model in the dataset containing multiple drivers are 98.79, 98.81, and 98.79 % respectively, and the recognition accuracy for drowsiness or drowsiness state is 99.33 %.

Conclusions: Using the proposed algorithm, it is possible to identify driver anomalies and provide new ideas for the development of intelligent vehicles.

Objectives: Arrhythmia is an important component of cardiovascular disease, and electrocardiogram (ECG) is a method to detect arrhythmia. Arrhythmia detection is often paroxysmal, and ECG signal analysis is time-consuming and expensive. We propose a model and device for convenient monitoring of arrhythmia at any time.

Methods: This work proposes a model combining residual block and bidirectional long-term short-term memory network (BiLSTM) to detect and classify ECG signals. Residual blocks can extract deep features and avoid performance degradation caused by convolutional networks. Combined with the feature of BiLSTM to strengthen the connection relationship of the local window, it can achieve a better classification and prediction effect.

Results: Model optimization experiments were performed on the MIT-BIH Atrial Fibrillation Database (AFDB) and MIT-BIH Arrhythmia Database (MITDB). The accuracy simulation results on both long and short signal was higher than 99 %. To further demonstrate the applicability of the model, validation experiments were conducted on MIT-BIH Normal Sinus Rhythm Database (NSRDB) and the Long-Term AF Database (LTAFDB) datasets, and the related recognition accuracy were 99.830 and 91.252 %, respectively. Additionally, we proposed a portable household detection system including an ECG and a blood pressure detection module. The detection accuracy was higher than 98 % using the collected data as testing set.

Conclusions: Hence, we thought our system can be used for practical application.

Tuberculosis caused by Mycobacterium tuberculosis have been a major challenge for medical and healthcare sectors in many underdeveloped countries with limited diagnosis tools. Tuberculosis can be detected from microscopic slides and chest X-ray but as a result of the high cases of tuberculosis, this method can be tedious for both microbiologist and Radiologist and can lead to miss-diagnosis. The main objective of this study is to addressed these challenges by employing Computer Aided Detection (CAD) using Artificial Intelligence-driven models which learn features based on convolution and result in an output with high accuracy. In this paper, we described automated discrimination of X-ray and microscopic slide images of tuberculosis into positive and negative cases using pretrained AlexNet Models. The study employed Chest X-ray dataset made available on Kaggle repository and microscopic slide images from both Near East university hospital and Kaggle repository. For classification of tuberculosis and healthy microscopic slide using AlexNet+Softmax, the model achieved accuracy of 98.14%. For classification of tuberculosis and healthy microscopic slide using AlexNet+SVM, the model achieved 98.73% accuracy. For classification of tuberculosis and healthy chest X-ray images using AlexNet+Softmax, the model achieved accuracy of 98.19%. For classification of tuberculosis and healthy chest X-ray images using AlexNet+SVM, the model achieved 98.38% accuracy. The result obtained has shown to outperformed several studies in the current literature. Future studies will attempt to integrate Internet of Medical Things (IoMT) for the design of IoMT/AI-enabled platform for detection of Tuberculosis from both X-ray and Microscopic slide images.

The utilization of nanotechnology in regenerative medicine has been globally proven to be the main solution to many issues faced with tissue engineering today, and the theoretical and empirical investigations of the association of nanomaterials with stem cells have made significant progress as well. For their ability to self-renew and differentiate into a variety of cell types, stem cells have become popular candidates for cell treatment in recent years, particularly in cartilage and Ocular regeneration. However, there are still several challenges to overcome before it may be used in a wide range of therapeutic contexts. This review paper provides a review of the various implications of nanomaterials in tissue and cell regeneration, the stem cell and scaffold application in novel treatments, and the basic developments in stem cell-based therapies, as well as the hurdles that must be solved for nanotechnology to be used in its full potential. Due to the increased interest in the continuously developing field of nanotechnology, demonstrating, and pinpointing the most recognized and used applications of nanotechnology in regenerative medicine became imperative to provide students, researchers, etc. who are interested.

Skin thickness, including the adipose layer, which varies from individual to individual, affects the bone density measurement using light. In this study, we proposed a method to measure skin thickness using light and to correct the bias caused by differences in skin thickness and verified the proposed method by experiments using a phantom. We measured simulated skin of different thicknesses and bovine trabecular bone of different bone mineral densities (BMDs) using an optical system consisting of lasers of 850 and 515 nm wavelengths, lenses, and slits. Although the slope of the light intensity distribution formed on the surface of the material when irradiated by the 850 nm laser is affected by the thickness of the skin phantom. The difference of the intensity distribution peaks (δ_y) between the 850 and 515 nm lasers was strongly correlated with the thickness of the skin phantom. The coefficient of determination between the measurements and the BMD was improved by correcting the 850 nm laser measurements with δ_y. This result suggests that the method is applicable to optical bone densitometry, which is insensitive to differences in skin thickness.

Organ-on-a-chip (OOC) is an emerging technology that has been proposed as a new powerful cell-based tool to imitate the pathophysiological environment of human organs. For most OOC systems, a pivotal step is to culture cells in microfluidic devices. In active cell capturing techniques, external actuators, such as electrokinetic, magnetic, acoustic, and optical forces, or a combination of these forces, can be applied to trap cells after ejecting cell suspension into the microchannel inlet. This review paper distinguishes the characteristics of biomaterials and evaluates microfluidic technology. Besides, various types of OOC and their fabrication techniques are reported and various active cell capture microstructures are analyzed. Furthermore, their constraints, challenges, and future perspectives are provided.

In vitro thrombogenicity tests for rotary blood pumps (RBPs) could benefit from assessing coagulation kinematics, as RBP design improves. In this feasibility study, we investigated if the method of thromboelastometry (TEM) is able to assess coagulation kinematics under the in vitro conditions of RBP tests. We conducted in vitro thrombogenicity tests (n=4) by placing Deltastream^® DP3 pumps into test loops that were filled with 150 mL of slightly anti-coagulated porcine blood, adjusted to an activated clotting time (ACT) well below clinically recommended levels. Blood samples were taken at certain time points during the experiment until a continuous decrease in pump flow indicated major thrombus formation. Blood samples were analyzed for ACT, platelet count (PLT), and several TEM parameters. While visible thrombus formation was observed in three pumps, ACT indicated an ongoing activation of coagulation, PLT might have indicated platelet consumption. Unexpectedly, most TEM results gave no clear indications. Nonetheless, TEM clotting time obtained by non-anticoagulated and chemically non-activated whole blood (HEPNATEM-CT) appeared to be more sensitive for the activation of coagulation in vitro than ACT, which might be of interest for future pump tests. However, more research regarding standardization of thrombogenicity pump tests is urgently required.