生物医学工程学杂志最新文献

Prosthesis loosening is the leading cause of postoperative revision in unicompartmental knee arthroplasty (UKA). The deviation of medial and lateral translational installation of the prosthesis during surgery is a common clinical phenomenon and an important factor in increasing the risk of prosthesis loosening. This study established a UKA finite element model and a bone-prosthesis fixation interface micromotion prediction model. The predicted medial contact force and joint motion of the knee joint from a patient-specific lower extremity musculoskeletal multibody dynamics model of UKA were used as boundary conditions. The effects of 9 femoral component medial and lateral translational installation deviations on the Von Mises stress of the proximal tibia, the contact stress, and the micro-motion of the bone prosthesis fixation interface were quantitatively studied. It was found that compared with the neutral position (a/A of 0.492), the lateral translational deviation of the femoral component significantly increased the tibial Von Mises stress and the bone-prosthesis fixation interface contact stress. The maximum Von Mises stress and the maximum contact stress of the fixation interface increased by 14.08% and 143.15%, respectively, when a/A was 0.361. The medial translational deviation of the femoral component significantly increased the bone-prosthesis fixation interface micro-motion. The maximum value of micromotion under the conditions of femoral neutral and medial translation deviation was in the range of 20-50 μm, which is suitable for osseointegration. Therefore, based on considerations such as the micromotion range suitable for osseointegration reported in the literature, the risk of reducing prosthesis loosening, and factors that may induce pain, it is recommended that clinicians control the mounting position of the femoral component during surgery within the safe range of 0-4 mm medial translation deviation.

Traditional manual testing of ventilator performance is labor-intensive, time-consuming, and prone to errors in data recording, making it difficult to meet the current demands for testing efficiency in the development and manufacturing of ventilators. Therefore, in this study we designed an automated testing system for essential performance parameters of ventilators. The system mainly comprises a ventilator airflow analyzer, an automated switch module for simulated lungs, and a test control platform. Under the control of testing software, this system can perform automated tests of critical performance parameters of ventilators and generate a final test report. To validate the effectiveness of the designed system, tests were conducted on two different brands of ventilators under four different operating conditions, comparing tidal volume, oxygen concentration, and positive end expiratory pressure accuracy using both the automated testing system and traditional manual methods. Bland-Altman statistical analysis indicated good consistency between the accuracy of automated tests and manual tests for all respiratory parameters. In terms of testing efficiency, the automated testing system required approximately one-third of the time needed for manual testing. These results demonstrate that the designed automated testing system provides a novel approach and means for quality inspection and measurement calibration of ventilators, showing broad application prospects.

During transfer tasks, the dual-arm nursing-care robot require a human-robot mechanics model to determine the balance region to support the patient safely and stably. Previous studies utilized human-robot two-dimensional static equilibrium models, ignoring the human body volume and muscle torques, which decreased model accuracy and confined the robot ability to adjust the patient's posture in three-dimensional spatial. Therefore, this study proposes a three-dimensional spatial mechanics modeling method based on individualized human musculoskeletal multibody dynamics. Firstly, based on the mechanical features of dual-arm support, this study constructed a foundational three-dimensional human-robot mechanics model including body posture, contact position and body force. With the computed tomography data from subjects, a three-dimensional femur-pelvis-sacrum model was reconstructed, and the individualized musculoskeletal dynamics was analyzed using the ergonomics software, which derived the human joint forces and completed the mechanic model. Then, this study established a dual-arm robot transfer platform to conduct subject transfer experiments, showing that the constructed mechanics model possessed higher accuracy than previous methods. In summary, this study provides a three-dimensional human-robot mechanics model adapting to individual transfers, which has potential application in various scenarios such as nursing-care and rehabilitating robots.

Heart rate is a crucial indicator of human health with significant physiological importance. Traditional contact methods for measuring heart rate, such as electrocardiograph or wristbands, may not always meet the need for convenient health monitoring. Remote photoplethysmography (rPPG) provides a non-contact method for measuring heart rate and other physiological indicators by analyzing blood volume pulse signals. This approach is non-invasive, does not require direct contact, and allows for long-term healthcare monitoring. Deep learning has emerged as a powerful tool for processing complex image and video data, and has been increasingly employed to extract heart rate signals remotely. This article reviewed the latest research advancements in rPPG-based heart rate measurement using deep learning, summarized available public datasets, and explored future research directions and potential advancements in non-contact heart rate measurement.

The accurate segmentation of breast ultrasound images is an important precondition for the lesion determination. The existing segmentation approaches embrace massive parameters, sluggish inference speed, and huge memory consumption. To tackle this problem, we propose T ²KD Attention U-Net (dual-Teacher Knowledge Distillation Attention U-Net), a lightweight semantic segmentation method combined double-path joint distillation in breast ultrasound images. Primarily, we designed two teacher models to learn the fine-grained features from each class of images according to different feature representation and semantic information of benign and malignant breast lesions. Then we leveraged the joint distillation to train a lightweight student model. Finally, we constructed a novel weight balance loss to focus on the semantic feature of small objection, solving the unbalance problem of tumor and background. Specifically, the extensive experiments conducted on Dataset BUSI and Dataset B demonstrated that the T ²KD Attention U-Net outperformed various knowledge distillation counterparts. Concretely, the accuracy, recall, precision, Dice, and mIoU of proposed method were 95.26%, 86.23%, 85.09%, 83.59%and 77.78% on Dataset BUSI, respectively. And these performance indexes were 97.95%, 92.80%, 88.33%, 88.40% and 82.42% on Dataset B, respectively. Compared with other models, the performance of this model was significantly improved. Meanwhile, compared with the teacher model, the number, size, and complexity of student model were significantly reduced (2.2×10 ⁶ vs. 106.1×10 ⁶, 8.4 MB vs. 414 MB, 16.59 GFLOPs vs. 205.98 GFLOPs, respectively). Indeedy, the proposed model guarantees the performances while greatly decreasing the amount of computation, which provides a new method for the deployment of clinical medical scenarios.

Cardiotocography (CTG) is a non-invasive and important tool for diagnosing fetal distress during pregnancy. To meet the needs of intelligent fetal heart monitoring based on deep learning, this paper proposes a TWD-MOAL deep active learning algorithm based on the three-way decision (TWD) theory and multi-objective optimization Active Learning (MOAL). During the training process of a convolutional neural network (CNN) classification model, the algorithm incorporates the TWD theory to select high-confidence samples as pseudo-labeled samples in a fine-grained batch processing mode, meanwhile low-confidence samples annotated by obstetrics experts were also considered. The TWD-MOAL algorithm proposed in this paper was validated on a dataset of 16 355 prenatal CTG records collected by our group. Experimental results showed that the algorithm proposed in this paper achieved an accuracy of 80.63% using only 40% of the labeled samples, and in terms of various indicators, it performed better than the existing active learning algorithms under other frameworks. The study has shown that the intelligent fetal heart monitoring model based on TWD-MOAL proposed in this paper is reasonable and feasible. The algorithm significantly reduces the time and cost of labeling by obstetric experts and effectively solves the problem of data imbalance in CTG signal data in clinic, which is of great significance for assisting obstetrician in interpretations CTG signals and realizing intelligence fetal monitoring.

The processing mechanism of the human brain for speech information is a significant source of inspiration for the study of speech enhancement technology. Attention and lateral inhibition are key mechanisms in auditory information processing that can selectively enhance specific information. Building on this, the study introduces a dual-branch U-Net that integrates lateral inhibition and feedback-driven attention mechanisms. Noisy speech signals input into the first branch of the U-Net led to the selective feedback of time-frequency units with high confidence. The generated activation layer gradients, in conjunction with the lateral inhibition mechanism, were utilized to calculate attention maps. These maps were then concatenated to the second branch of the U-Net, directing the network's focus and achieving selective enhancement of auditory speech signals. The evaluation of the speech enhancement effect was conducted by utilising five metrics, including perceptual evaluation of speech quality. This method was compared horizontally with five other methods: Wiener, SEGAN, PHASEN, Demucs and GRN. The experimental results demonstrated that the proposed method improved speech signal enhancement capabilities in various noise scenarios by 18% to 21% compared to the baseline network across multiple performance metrics. This improvement was particularly notable in low signal-to-noise ratio conditions, where the proposed method exhibited a significant performance advantage over other methods. The speech enhancement technique based on lateral inhibition and feedback-driven attention mechanisms holds significant potential in auditory speech enhancement, making it suitable for clinical practices related to artificial cochleae and hearing aids.

The intensive care unit (ICU) is a highly equipment-intensive area with a wide variety of medical devices, and the accuracy and timeliness of medical equipment data collection are highly demanded. The integration of the Internet of Things (IoT) into ICU medical devices is of great significance for enhancing the quality of medical care and nursing, as well as for the advancement of digital and intelligent ICUs. This study focuses on the construction of the IOT for ICU medical devices and proposes innovative solutions, including the overall architecture design, devices connection, data collection, data standardization, platform construction and application implementation. The overall architecture was designed according to the perception layer, network layer, platform layer and application layer; three modes of device connection and data acquisition were proposed; data standardization based on Integrating the Healthcare Enterprise-Patient Care Device (IHE-PCD) was proposed. This study was practically verified in the Chinese People's Liberation Army General Hospital, a total of 122 devices in four ICU wards were connected to the IoT, storing 21.76 billion data items, with a data volume of 12.5 TB, which solved the problem of difficult systematic medical equipment data collection and data integration in ICUs. The remarkable results achieved proved the feasibility and reliability of this study. The research results of this paper provide a solution reference for the construction of hospital ICU IoT, offer more abundant data for medical big data analysis research, which can support the improvement of ICU medical services and promote the development of ICU to digitalization and intelligence.

Depression, a mental health disorder, has emerged as one of the significant challenges in the global public health domain. Investigating the pathogenesis of depression and accurately assessing the symptomatic changes are fundamental to formulating effective clinical diagnosis and treatment strategies. Utilizing non-invasive brain imaging technologies such as functional magnetic resonance imaging and scalp electroencephalography, existing studies have confirmed that the onset of depression is closely associated with abnormal neural activities and altered functional connectivity in multiple brain regions. Magnetoencephalography, unaffected by tissue conductivity and skull thickness, boasts high spatial resolution and signal-to-noise ratio, offering unique advantages and significant value in revealing the abnormal brain mechanisms and neural characteristics of depression. This review, starting from the rhythmic characteristics, nonlinear dynamic features, and connectivity characteristics of magnetoencephalography in depression patients, revisits the research progress on magnetoencephalography features related to depression, discusses current issues and future development trends, and provides insights for the study of pathophysiological mechanisms, as well as for clinical diagnosis and treatment of depression.

The gradient field, one of the core magnetic fields in magnetic resonance imaging (MRI) systems, is generated by gradient coils and plays a critical role in spatial encoding and the generation of echo signals. The uniformity or linearity of the gradient field directly impacts the quality and distortion level of MRI images. However, traditional point measurement methods lack accuracy in assessing the linearity of gradient fields, making it difficult to provide effective parameters for image distortion correction. This paper introduced a spherical measurement-based method that involved measuring the magnetic field distribution on a sphere, followed by detailed magnetic field calculations and linearity analysis. This study, applied to assess the nonlinearity of asymmetric head gradient coils, demonstrated more comprehensive and precise results compared to point measurement methods. This advancement not only strengthens the scientific basis for the design of gradient coils but also provides more reliable parameters and methods for the accurate correction of MRI image distortions.