BMC biomedical engineering最新文献

In musculoskeletal simulation, individualized joint axes enhance the accuracy and reliability of kinematic and kinetic simulation results. We investigated the correctness and performance of an analytical method for identifying the instantaneous axis of rotation between two bodies based on motion data in OpenSim. The instantaneous center of rotation is the point at which two bodies have the same velocity. The relative linear and angular velocity between the two bodies, as well as their relative position to each another, are required as inputs to calculate it. Using the instantaneous center of rotation, fixed or moving joint centers of rotation can be identified. To prove the general applicability of the method, the instantaneous centers of rotation of a revolute joint of a simple double pendulum model and the hip and knee joint of a more complex musculoskeletal model were investigated. The hip joint is defined as a ball joint. The knee joint is defined as an OpenSim custom joint which describes the motion of the child segment in relation to the parent segment as a function of generalized coordinates. To verify the correctness of the approach in OpenSim, the moving centers of rotation were calculated using synthetic noisefree data. The results were compared to the implementation of the respective joints in the model which act as the ground truth. White Gaussian noise was added to the synthetic data to analyze its effect on the quality of the calculated centers of rotation. We were able to correctly identify the center of rotation of each joint using noisefree data. In the case of noisy data, joint centers of rotation can be determined by applying additional filtering or optimization methods to the calculated instantaneous centers of rotation. Consequently, we are able to determine the center of rotation for arbitrary joints based on noisy synthetic data. This approach is applicable for both fixed and moving centers of rotation which distinguishes it from commonly used methods in the field of biomechanical simulation.

The mechanical properties of tumor tissue differ from those of healthy tissue. Therefore, surgeons palpate accessible surgical sites to determine tumor boundaries prior to resection. However, palpation is not possible during minimally invasive surgery, so instrumented palpation is required instead. This study investigates the suitability of an engineering method that combines mechanical object scanning and indentation to determine Young's modulus of soft, tissue-like materials. To establish a defined reference, we tested our concept on silicone phantoms containing stiff tumor-like inclusions. We used a sensor consisting of a load cell connected to a rigid probe with a spherical indenter tip. Young's modulus was calculated by measured force, indentation depth, and indenter geometry. These results were compared with those of a palpation experiment on the same specimens, conducted with surgeons. Validation results reflect the accuracy of the method. Error in estimation of Young's modulus is: soft material 6.7%, stiff material 44.9%. Repeatability is high, with a standard deviation < 7%. By scanning a phantom and creating a stiffness image, we were able to identify the location and shape of the inclusion more clearly than experienced surgeons could using manual palpation. Looking ahead, the prospect of miniaturizing the presented technique for localizing tumor boundaries during surgery seems promising.

Diabetic retinopathy (DR) stands as a leading cause of global blindness. Early identification and prompt treatment are crucial in preventing vision impairment caused by diabetic retinopathy (DR). Manual screening of retinal fundus images is challenging and time-consuming. Additionally, there is a significant gap between the number of DR patients and the number of medical experts. Integrating machine learning (ML) and deep learning (DL) techniques is becoming a viable alternative to traditional DR screening techniques. However, the absence of a retinal dataset with standardized quality, the complexity of DL models, and the need for high computational resources are challenges. Therefore, in this study, we studied and analyzed the research landscape in integrating ML techniques in DR screening. In this regard, our work contributes significantly in several aspects. Initially, we identify and characterize images of the retinal fundus that are readily available. Then, we discuss commonly used preprocessing techniques in DR screening. In addition, we analyze the progress of ML techniques in DR screening. Lastly, we discussed existing challenges and showed future directions.

Background: Stiff-knee gait is a common movement disorder in individuals with stroke; however, standardized criteria for its identification remain lacking. This study aimed to examine suitable criteria for identifying stiff-knee stroke survivors to facilitate comparisons across studies. Twenty-four stroke survivors (45.2±13.7 years old) and 24 age- and sex-matched controls (45.5±13.5 years old) with no known gait impairment participated in this study. Participants walked along a 10-m walkway at a self-selected comfortable speed. A motion capture system recorded the trajectories of retroreflective markers placed on specific body landmarks. The following knee flexion parameters during gait cycle were analyzed: (1) peak knee flexion during the swing period, (2) total range of motion (RoM cycle), calculated as the difference between maximum and minimum knee excursion during gait cycle, (3) RoM from toe-off to peak knee flexion ("RoM swing"), and (4) timing of peak flexion. Comparisons were made among control, paretic, and non-paretic limbs.

Results: Among the 21 stroke survivors identified with stiff-knee gait, the paretic limb showed reduced peak swing, RoM swing, and RoM cycle compared to both the control and non-paretic limbs, as well as earlier timing compared to the non-paretic limb only.

Conclusions: Among the four examined criteria to identify stiff-knee gait in stroke survivors, the most suitable are peak knee flexion during the swing period of less than 40°, and knee range of motion from toe-off to peak knee flexion of less than 12°.

Introduction: Healthcare practitioners in low and middle-income countries encounter numerous challenges, including insufficient staffing, an unreliable electrical infrastructure, and constrained resources. Unstable availability of electricity constitutes a significant impediment to the efficacy of public health initiatives that depend on technology requiring electrical power. More than 95% of medical apparatus is procured from developed nations, with an only 13% of medical device manufacturers situated within LMIC. Non-profit organizations have developed innovative medical technologies that offer life-saving solutions previously inaccessible in the developing world.

Methods: The device is designed to be cost-effective, made from local resources, compact, portable, and operated without electricity. The device is designed to have replaceable parts, a waste container cut off, and be compatible with standard suction catheter/tubing. It includes user and maintenance training, displayed parameters, corrosion-resistant components, and pump pedal spring loading. A combination concept was created by combining the manufacturing capabilities of a diaphragm with the efficiency of a piston cylinder. The final design chosen uses a piston-cylinder assembly with a trash bag diaphragm for the seal, eliminating the need for exact tolerances and costly machining. A prototype was built on top of this combined design concept.

Result: The design concept involves the implementation of locally accessible resources for the pressure-generating mechanisms, as well as the adaptability and ease of assembly of the device. The ideal length of the cylinder assembly was determined through pressure readings using a digital manometer differential pressure sensor. Changing the capacity of the cylinder can adjust the pressure range. The ideal cylinder length was found to be 12.75 inches as it provided the desired pressure range, although longer cylinders were inconsistent and difficult for users to operate.

Conclusion: The Foot-Operated Suction Unit is particularly well suited for usage in distant places without power supply, rural health facilities, home care, field emergencies, and brief power outages due to its compact size, low weight, and simplicity of operation. Hospitals of all levels typically employ foot-operated suction units to execute abortions and to draw blood, pus, sputum, and other mucus during surgical operations.

Glaucoma is a leading cause of irreversible blindness, necessitating early and accurate diagnosis to prevent vision loss. Traditional diagnostic methods often suffer from subjectivity and variability, emphasizing the need for more reliable approaches. This study evaluates the application of machine learning (ML) techniques in glaucoma diagnosis, analyzing their effectiveness and identifying the most promising methods and datasets. A systematic review of five major databases was conducted, selecting 35 studies based on predefined criteria. The findings reveal that structured data, including optical coherence tomography (OCT), visual field (VF) tests, and demographic factors, significantly enhance diagnostic accuracy. ML models such as support vector machine (SVM), deep learning (DL), random forest, and ensemble methods demonstrated accuracy ranging from 76 to 98.3%, with AUC values between 52.5% and 99%. Despite these advancements, challenges such as data imbalance and limited sample sizes impact model generalizability. The results highlight the potential of ML to improve glaucoma detection, though further research is needed to enhance data quality and model validation for broader clinical applicability.

Electrocardiography (ECG) is a non-invasive tool used to identify abnormalities in heart rhythm. It is used to evaluate dysfunctions in the electrical system of the heart. It offers a mechanism that does not cause any harm to patients. Being affordable makes it accessible. It provides a comprehensive assessment of the condition of the heart. Although it provides a successful analysis opportunity for arrhythmia detection, it is time-consuming and depends on the clinician's experience. In addition, since the ECG patterns in pediatric patients are different from the ECG patterns in adults, physicians consider it a difficult and complex task. For this reason, a custom dataset of pediatric patients was created in this study. This dataset consists of 1318 abnormal beats and 1403 normal beats. MobileNetv2 transfer learning architecture was used to classify this balanced dataset. However, the stability of the results is a valuable. Therefore, the optimization algorithm that minimizes the loss function and the regularization method that controls the complexity of the model are proposed. In this direction, Proposed Optimization Algorithm V5 and Proposed Regularization Method V5 approaches have been integrated into the MobileNetv2 transfer learning model. The accuracy rates produced in the training and test datasets are 0.9801 and 0.9509, respectively. These results have acceptable improvement and stability compared to the accuracies of 0.9633 and 0.9399 produced by the original MobileNetv2 architecture on the training and test dataset, respectively. However, performance values provide limited information about the generalizability of the model. Therefore, the same processes were repeated on a more complex dataset with 6 categories. As a result of the classification, the accuracy rates for the training and test data sets were obtained as 0.9200% and 0.8975%, respectively. Training was performed under the same conditions as the training performed on 2-category datasets. Therefore, it is normal for the test dataset to experience a decrease of approximately 5%. The results obtained show that generalizations can be made for comprehensive, highly diverse and rich datasets.

Background: Gait kinetics explains dynamics of gait deviations, which inform surgical and non-surgical clinical-decision-making to enhance walking performance of children with cerebral palsy. Kinetic gait profile of children with lesser crouch angle is known; however lower-extremity gait kinetics of ambulatory children at a further continuum of the spectrum with greater crouch angle is unclear. Therefore, present cross-sectional study evaluated influence of varying crouch angle on gait kinetics and walk distance.

Method: Following ethical approval and signed informed consent of parents, 3-D gait of 33 ambulatory children with CP(10.4 year) and 31 age-matched typically-developing children was studied to compute the magnitude and timing of lower-extremity external net joint moments and power during stance phase. An average of 3gait trials walked bare-feet at self-selected pace was considered for analyses. Walk distance was measured with 2-min walk test. Typically developing children were classified as Group I, children with mild crouch-angle (mean knee flexion angle during stance)[Formula: see text]16.8⁰and ≤ 25⁰ were classified as Group II(n = 17), whereas children with severe crouch-angle i.e.[Formula: see text] 25⁰ throughout stance phase were classified as Group III(n = 16). Three groups were compared with one-way-ANOVA(p ≤ 0.05). Bonferroni adjustment was made for post-hoc analyses (p ≤ 0.01).

Results: Gait speed, cadence and 2-minute walk distance decreased from Group I to II to III(p ≤ 0.01). Hip flexion, extension and adduction; knee flexion and ankle dorsiflexion moments were significantly different between three groups(p ≤ 0.01)). Rise in crouch-angle was associated with an increase in peak hip flexion moment and increase in power generated at hip and decrease in power generated at knee and ankle (p ≤ 0.01). The timing of peak hip and knee moments during stance phase also differed across the 3 groups (p ≤ 0.01) indicating a delay in the occurrence of peak hip flexion-extension; abduction-adduction and knee flexion moment with a rise in crouch angle.

Conclusion: Present findings inform lower-extremity joint kinetics during gait across the spectrum of mild to severe crouch angle with reference to typically-developing children. Precise knowledge of magnitude and pattern of net joint moments and power along with the timing of moments and decline in walking distance in children with severe crouch, can guide therapeutic interventions to restore the optimum dynamic lever arm function for improved walking performance.

Trial registration: CTRI registration no. CTRI/22/12/048524/27/12/2022.

Trial registry: CTRI/22/12.

Trial registration number: 048524. Trial registration date: 27th December 2022.