Biomedical Engineering and Computational Biology最新文献

Background: Operating Room Scheduling (ORS) is vital in healthcare management, impacting patient outcomes, economics, and the shift to value-based care. The academic literature offers various solutions with distinct pros and cons.

Aim: This study aims to (i) outline ORS challenges across surgical specialties; (ii) examine ORS's impact on healthcare goals, focusing on patient outcomes, value-based care, and economics; (iii) assess academic solutions' real-world applicability; and (iv) conduct a bibliometric analysis to track ORS research progression, pivotal works, and future directions.

Methods: We performed a comprehensive bibliometric analysis using Scopus data. Biblioshiny from Bibliometrix aided data mining and analysis, spanning 2000 to 2023, tracking publication trends, themes, co-occurrence, and co-citation networks.

Results: ORS publications steadily rose, notably post-2013, led by developed nations like the UK, Australia, the US, France, and Germany. Key themes included operating rooms, surgery, and humans. Seven primary research routes emerged, covering Surgery Duration, Allocation, Advanced Scheduling Integration, and Patient Flow Optimization. Citation analysis highlighted heuristic algorithms and integer programing as central ORS themes.

Conclusion: This study offers a panoramic ORS overview, advocating an integrated approach aligning patient outcomes, economics, and value-based care. Bibliometric analysis charts ORS research evolution guides future research, and holds significance for practitioners, policymakers, and academics, enhancing ORS paradigms and healthcare delivery.

Tiny Artificial Intelligence (Tiny AI) is transforming resource-constrained embedded systems, particularly in e-health applications, by introducing a shift in Tiny Machine Learning (TinyML) and its integration with the Internet of Things (IoT). Unlike conventional machine learning (ML), which demands substantial processing power, TinyML strategically delegates processing requirements to the cloud infrastructure, allowing lightweight models to run on embedded devices. This study aimed to (i) Develop a TinyML workflow that details the steps for model creation and deployment in resource-constrained environments and (ii) apply the workflow to e-health applications for the real-time detection of epileptic seizures using electroencephalography (EEG) data. The methodology employs a dataset of 4097 EEG recordings per patient, each 23.5 seconds long, from 500 patients, to develop a robust and resilient model. The model was deployed using TinyML on microcontrollers tailored to hardware with limited resources. TensorFlow Lite (TFLite) efficiently runs ML models on small devices, such wearables. Simulation outcomes demonstrated significant performance, particularly in predicting epileptic seizures, with the ExtraTrees Classifier achieving a notable 99.6% Area Under the Curve (AUC) on the validation set. Because of its superior performance, the ExtraTrees Classifier was selected as the preferred model. For the optimized TinyML model, the accuracy remained practically unchanged, whereas inference time was significantly reduced. Additionally, the converted model had a smaller size of 256 KB, approximately ten times smaller, making it suitable for microcontrollers with a capacity of no more than 1 MB. These findings highlight the potential of TinyML to significantly enhance healthcare applications by enabling real-time, energy-efficient decision-making directly on local devices. This is especially valuable in scenarios with limited computing resources or during emergencies, as it reduces latency, ensures privacy, and operates without reliance on cloud infrastructure. Moreover, by reducing the size of training datasets needed, TinyML helps lower overall costs and minimizes the risk of overfitting, making it an even more cost-effective and reliable solution for healthcare innovations.

Introduction: Early detection through routine screening methods, such as the Papanicolaou (Pap) test, is crucial for reducing cervical cancer mortality. However, the Pap smear method faces challenges including subjective interpretation, significant variability in diagnostic confidence, and high susceptibility to human errors-leading to both false negatives (missed abnormalities) and false positives (unnecessary follow-up procedures). Providing a first opinion could improve the screening examination pipeline and greatly aid the specialist's confidence in reporting. Artificial intelligence (AI)-based approaches have shown promise in automating cell classification, reducing human error, and identifying subtle abnormalities that may be missed by experts.

Methods: In this study, we present RunicNet, a CNN-based architecture with attention mechanisms designed to classify Pap smear cell images. RunicNet integrates attention mechanisms such as High-Frequency Attention Blocks-enhanced Residual Blocks for improved feature extraction, Pixel Attention for computational efficiency, and a Gated-Dconv Feed-Forward Network to refine image representation. The model was trained on a dataset of 85 080 cell images, employing data augmentation and class balancing techniques to address dataset imbalances.

Results: Evaluated on a separate testing dataset, RunicNet achieved a weighted F1-score of 0.78, significantly outperforming baseline models such as ResNet-18 (F1-score of 0.53) and a fully connected CNN (F1-score of 0.66).

Discussion: The findings support the potential of attention-based CNN models like RunicNet to significantly improve the accuracy and efficiency of cervical cancer screening. Integrating such AI systems into clinical workflows may enhance early detection and reduce diagnostic variability in Pap smear analysis.

Magnetic Resonance Imaging (MRI) is a non-invasive imaging method that utilizes radio waves and magnetic fields. This study focuses on reducing the acoustic noise produced inside the cylindrical shell of the scanner, where the patient is located. Vibration modes are generated by eddy currents in the cylindrical shell induced by gradient magnetic fields. Additionally, the scanner wall is typically joined to the gradient spiral cylinder, causing vibrations to be transmitted to the wall and thereby producing extra sound waves. The present study investigates methods for mitigating noise from the scanner wall and reducing the transmission noise from the spiral gradient cylinder. Numerical methods and practical solutions for lowering acoustic noise in MRI gradient coils are explored. A 20 mm uniform absorber is demonstrated as an effective design for significantly reducing acoustic noise in the frequency range 0 to 3 kHz. Finally, numerical analysis of gradient cycles yields solutions that lower both vibration and noise levels.

Introduction: Coronary artery disease (CAD) is a major global cause of morbidity and mortality. Therefore, advances in early identification and individualized treatment plans are crucial.

Methods: This article presents machine learning (ML) based model that can recognize metabolomic compounds associated with CAD in the Qatari population for the early detection of CAD. We also identified statistically significant metabolic profiles and potential biomarkers using ML methods.

Results: Among all ML models, artificial neural network (ANN) outstands all with an accuracy of 91.67%, recall of 80.0%, and specificity of 100%. The results show that 173 metabolites (P < .05) are significantly associated with CAD. Of these metabolites, the majority (95/173, 54.91%) were high in CAD patients, while 45.09% (78/173) were high in the control group. Two metabolites 2-hydroxyhippurate (salicylurate) and salicylate were notably higher in CAD patients compared to the control group. Conversely, 4 metabolites, cholate, 3-hydroxybutyrate (BHBA), 4-allyl catechol sulfate, and indolepropionate, showed relatively higher level in the control group.

Conclusion: We believe our study will support in advancing personalized diagnosis plan for CAD patients by considering the metabolites involved in CAD.

Background: By combining existing Global Burden of Disease (GBD) data with the economic conditions of different regions, we can better understand disease trends and make more accurate estimations, facilitating effective public health interventions. Medical institutions can consequently allocate resources more efficiently. For patients, this helps lower disease risk and reduce the overall disease burden in affected areas.

Methods: We analyzed health patterns in 204 countries using GBD 2021 methodologies and conducted separate analyses of disease burden in China and worldwide. We estimated incidence, prevalence, and years lived with disability (YLDs). We further assessed disease status by incorporating Socio-Demographic Index (SDI) values. In addition, we used Mendelian randomization to identify factors leading from falls to thoracic rib fractures, and we investigated the key protein involved in thoracic rib fractures through detection of 4907 plasma proteins.

Results: From 1990 to 2021, the age-standardized incidence rate (ASIR) and age-standardized prevalence rate (ASPR) generally showed an upward trend, although male ASIR, and ASPR displayed a slight decline. In China, however, ASIR and ASPR reached a turning point in 2000, dipped in 2005, then trended upward again. Morbidity and prevalence were negatively correlated with SDI. Based on Mendelian randomization analyses, falls leading to thoracic rib fractures were linked to education level and osteoporosis. Moreover, HAMP was identified as the key protein in thoracic rib fractures.

Conclusion: As global populations age, analyzing the global burden of thoracic rib fractures caused by falls from 1990 to 2021 can help guide the development of effective public health prevention strategies and optimize the allocation of existing medical resources.

In this study, reliability methods were demonstrated as a promising approach in medical engineering by identifying the most significant muscle forces affecting femoral stress. First, the finite element method (FEM) in Abaqus software was used to model the effects of 10 muscle and joint forces across various regions of the femur. Then, using the response surface methodology (RSM), and examining the effect coefficients of each joint and muscle force, the hip joint reaction force with an impact coefficient of 210.97 was identified as the most effective force on bone stress. After that, the gluteus minimus and gluteus medius muscle forces were ranked second and third in terms of stress effect with coefficients of 66.6 and 34.47. This study showed that the anterior femoral muscles have a significant effect on stress compared to the posterior femoral muscles. RSM enables faster and more precise identification of joint and muscle forces influencing femoral stresses compared to conventional methods. This innovative approach not only increased the understanding of biomechanical phenomena, but also provided a more efficient tool for investigating and optimizing such processes in biomedical engineering applications.

Deep learning models are necessary in the field of healthcare for the diagnosis of cardiac rhythm diseases since the conventional ECG classification is based on hand-crafted feature engineering and traditional machine learning. Nevertheless, CNN and BiLSTM architectures provide automatic feature learning, enhancing ECG classification accuracy. The current research work puts forward a framework integrating CNN with CBAM and BiLSTM layers for the purpose of extracting valuable features and classifying ECG signals. The model classifies heartbeats according to the AAMI EC57 standard into 5 categories: normal beats (N), supraventricular ectopic beats (S), ventricular ectopic beats (V), fusion beats (F), and unknown beats (Q). To tackle uneven class distributions, SMOTE synthesizes new samples, making the model more robust. Evaluation on MIT-BIH arrhythmia database yields remarkable results with 99.20% accuracy, 97.50% sensitivity, 99.81% specificity, and 98.29% mean F1 score. Deep learning methods have great potential to alleviate clinicians' workload and improve diagnostic accuracy of cardiac diseases.

Background: The performance and genetic role in host response delineate investigative points of polymorphisms as potential biomarkers in viral infections.

Methods: Thus, this research aimed to map biomarkers and risk factors in the severity of COVID-19 in individuals in Western Amazon (n = 243).

Results: Patients aged 40 to 59 years showed an association with clinical progression (P = .003), also evidencing the relationship for individuals >60 years (P < .001), besides the non-vaccination influenced the pathology (P = .023). qPCR for human genotyping of the targets rs2070788, rs4702, rs76635825, rs540856718, rs35803318, rs12979860, and rs16899066, as well as for gene expression of ACE2, HLA-A, HLA-B, IFNL-3/2, IL-6, and TMPRSS2 was used. The rs12979860 (C > T) and rs2070788 (A > G) showed association among the analyzed groups (P < .05) with the allelic and genotypic frequency of rs12979860 (x ² < 3.84) and evolutionary pointing of rs2070788G allele among infected people, including deaths.

Conclusion: Gene expression showed high levels between the moderate and severe groups, with emphasis on TMPRSS2 and IL-6 genes that performed better. Thus, there is possibly an association regarding the role of the TMPRSS2 gene and rs2070788G, as well as age and IL-6 levels for COVID-19, pointing in parallel to the considerable influence of the vaccine on the SARS-CoV-2 pathway.

Although SARS-COV-2 started in 2019, its losses are still significant, and it takes victims. In the present study, the epidemic patterns of SARS-COV-2 disease have been investigated from the point of view of mathematical modeling. Also, the effect of quarantine has been considered. This mathematical model is designed in the form of fractional calculations along with a model predictive control (MPC) to monitor this model. The fractional-order model has the memory and hereditary properties of the system, which can provide more adjustable parameters to the designer. Because the MPC can predict future outputs, it can overcome the conditions and events that occur in the future. The results of the simulations show that the proposed nonlinear model predictive controller (NMPC) of fractional-order has a lower mean squared error in susceptible people compared to the optimal control of fractional-order (~3.6e-04 vs. 47.4). This proposed NMPC of fractional-order can be used for other models of epidemics.