Biomedizinische Technik. Biomedical engineering最新文献

Objectives: In this work, it was aimed to employ machine learning (ML) algorithms to accurately forecast the radiation doses for phantoms while accounting for the most popular CT protocols.

Methods: A cloud-based software was utilized to calculate the effective doses from different CT protocols. To simulate a range of adult patients with different weights, eight entire body mesh-based computational phantom sets were used. The head, neck, and chest-abdomen-pelvis CT scan characteristics were combined to create a dataset with 33 rows for each phantom and 792 rows total. At the ML stage, linear (LR), random forest (RF) and support vector regression (SVR) were used. Mean absolute error, mean squared error and accuracy were used to evaluate the performances.

Results: The female phantoms received higher doses (7.8 %) than males. Furthermore, an average of 11 % more dose was taken to the normal weight phantom than to the overweight, the overweight in comparison to the obese I, and the obese I in comparison to the obese II. Among the ML algorithms, the LR showed 0 error rate and 100 % accuracy in predicting CT doses.

Conclusions: The LR was shown to be the best approach out of those used in the ML estimation of CT-induced doses.

Objectives: One of the worldwide public health issues mostly affecting children and expectant mothers is Anemia. Recently, non-invasive hemoglobin (Hb) measurements, such as machine learning (ML) algorithms, can diagnose Anemia more quickly and efficiently.

Methods: To diagnose Anemia using photoplethysmography (PPG), two tracks are investigated in this paper, based on clinical data and PPG signals. We use state-of-the-art data for Hb levels, extracted from PPG signals. This first track's methodology is divided into three stages: the labelling of the data as normal and abnormal; the data pre-processing; and applying ML algorithms based on four given features. We extracted nineteen features for red and infrared measurements in the second track. The second track's methodology is broken down into five stages: labelling of the data; data processing; signal augmentation; feature extraction; and applying ML algorithms. A five-fold cross-validation technique was applied for both tracks.

Results: We succeeded in classifying the anemic condition with 100 % classification accuracy. Our accurate detection of anemic status will promote preventive healthcare.

Conclusions: Ultimately, this proposed ML model in this paper validated the effectiveness of the ML algorithms as non-invasive techniques for identifying Anemia.

Objectives: This study evaluates micro-electro-mechanical systems (MEMS) devices comprising cantilever piezoelectric resonators with powder-based permanent magnets (micromagnets) at the tip. Fabricated using a well-known PowderMEMS process given by the Fraunhofer Institute for Silicon Technology, these devices function as magnetic field sensors based on the magnetic torque detection principle, which arises from the interaction between the given micromagnets' dipole moment and the to-be-measured magnetic field. The study investigates how the magnetic state of the micromagnets influences the overall sensitivity of the provided Prototype MEMS-devices.

Methods: The performance of the first prototypes of this narrow-band magnetic field sensor was evaluated using two approaches: (1) a Vibrating Sample Magnetometer (VSM) to analyze the magnetic hysteresis loop and (2) sensitivity measurements at resonance frequency to determine the provided sensitivity under a predefined external magnetic flux density.

Results: Among the four prototypes analyzed, the device with the highest remanence and coercivity demonstrated superior sensing performance, achieving a sensitivity of 1,090 kV/T at the resonance frequency. The analysis showcased substantial variations in noise amplitude spectral density, and sensitivity, emphasizing the importance of magnetic hysteresis properties in sensor performance.

Conclusions: These findings highlight the potential of MEMS-devices with enhanced coercivity and remanence for enhanced sensing capabilities in compact sensor designs, particularly useful for array sensor configurations in narrow-bandwith medical applications.

Objectives: Free gas micro-/nano-bubbles (MNBs) in water have demonstrated significant potential in various industrial applications, including water treatment, enhanced transport processes, and disinfection. However, the feasibility of utilizing MNBs water as a dispersed system for preparing ultrasound imaging vehicles is seldom explored. This study aims to investigate the potential of MNBs water for this purpose.

Methods: Initially, MNBs water containing sulfur hexafluoride (SF₆) was prepared and characterized. Subsequently, the potential of SF₆ MNBs water to form lipid-shelled bubbles for ultrasound imaging was evaluated. This involved the incubation of lyophilized phospholipids with SF₆ MNBs water.

Results: The study confirmed the presence of SF₆ MNBs in water. Through the incubation process, it was possible to obtain lipid-shelled bubbles with a nano-sized and narrow size distribution. These bubbles exhibited comparable echogenicity to those produced by conventional mechanical agitation methods during the initial 5 min of in vitro observation.

Conclusions: SF₆ MNBs water represents a novel dispersion medium for generating nano-sized lipid-shelled bubbles. This approach offers a promising new method for extravascular ultrasound imaging and drug delivery, potentially expanding the applications of MNBs in medical imaging and therapeutic delivery systems.

Objectives: Helical plating is an established alternative to straight plating for humeral shaft fractures in order to prevent iatrogenic radial nerve damage. However, a previous biomechanical investigation indicated differences in fracture healing for helical plating due to a potential shift of interfragmentary movements compared to straight plating. Therefore, fracture healing simulations were performed to assess any differences in bone healing of helical vs. straight plating.

Methods: A systematic workflow for fracture healing analytics was created, covering essential steps of bone modelling, implant modelling, finite element modelling, fracture healing simulation and result analysis. Computational humerus models with an AO12C2 fracture and straight and helical osteosynthesis were created. An established fracture healing model was used to simulate callus formation over 112 days under physiological loading. The predicted tissue differentiation and interfragmentary movement (IFM) was tracked over the entire simulated healing course.

Results: Helical plating resulted in larger interfragmentary movements for compression and shear components, and in a greater proportion of near and far cortical movement. Vascularization and tissue formation were deferred, but cortical bridging was achieved.

Conclusions: Helical plating resulted in slightly deferred bone healing due to larger interfragmentary shear movements. Considering the advantage of helical plating in clinical context, a slightly deferred bone healing is justifiable.

Objectives: The operating room is a fast-paced and demanding environment. Among the various factors involved in its optimization, predicting surgery duration is critical for scheduling and resource organization, ultimately resulting in improved quality of surgical care.

Methods: We design, implement and evaluate a semi-automated machine learning method that takes as input the current phase and tools employed and provides prediction of the Remain Surgery Duration (RSD) in laparoscopic cholecystectomy operations. We use the annotated information of tools and phases provided in the publicly available dataset Cholec80. The method is based on a Random Forest regression model that considers two data streams: the surgical phase and the type of tools employed, at each time-frame of the operation. The data were split into Training-, Validation- and Test-sets. The Mean Absolute Error (MAE) was used as the performance metric for the various models examined.

Results: Our approach managed to achieve a MAE=5.89 min across the overall duration of the surgeries in the test-set and MAE=4.61 min at 20 min before the end of the operation.

Conclusions: The employment of two separate regression models switched at a specific elapsed time threshold provides significant improvement in RSD prediction compared to other methods that process the video from the endoscope.

Objectives: Transcranial electrical stimulation (tES) has been widely used in neuroscience research, and the spatial focusing and penetration of the process are currently the main constraints on the effectiveness of treatment.

Methods: A high-definition electrical stimulation (HD-tES) device with envelope waves was designed. The device utilized a 4 × 1 electrode structure and was designed with an impedance adjustment circuit to evenly distribute the current among the four return channels. The output performance and safety of the device were verified in in vitro experiments. The spatial focusing of the 4 × 1 electrode structure and the high penetration advantage of envelope waves are explored through simulations. Finally, experiments were performed on 10 healthy adults.

Results: The 4 × 1 electrode structure has the best spatial focusing effect. Current frequencies above 1 kHz may have higher tissue penetration. In addition, the safety of envelope wave stimulation has been verified in human trials, and no adverse reactions occurred during stimulation.

Conclusions: The low and medium frequency (<10 kHz) envelope wave HD-tES device is expected to have a positive impact in the field of medicine and neuroscience.

Objectives: In view of the gradual rejuvenation and acceleration of lumbar spondylosis, a wearable powered lumbar exoskeleton based on a 6-SPU/SP parallel mechanism is designed based on the rehabilitation treatment method of lumbar forward flexion/extension, left/right lateral flexion and rotation.

Methods: First, the changes in human lumbar muscles are analyzed based on human biomechanics, and then the prototype design of the powered lumbar exoskeleton is implemented, including the mechanical mechanism design, and hardware module design. Finally, the simulation experiment of muscle force and output sensitivity test in the resistive mode are conducted.

Results: The simulation results show that the external oblique muscle can be relieved about 20 % and the iliopsoas muscle can be decreased by 33 % when wearing the powered lumbar exoskeleton in the lateral flexion. The pressure sensors can measure the output force of each actuator in real-time when the resistance force reaches the set value of 15 N at the resistive model.

Conclusions: The results show that the powered lumbar exoskeleton can assist the human lumbar spine in rehabilitation training of traction, forward flexion and extension, left and right lateral flexion, and rotation. This research provides new ideas for future clinical research.

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.