Medical & Biological Engineering & Computing最新文献

Thyroid nodules often necessitate surgical intervention, where traditional retractors may cause muscle damage due to prolonged use. This study introduces a slippage-suppression robotic system for thyroid surgery, featuring a conformal force and torque sensing module integrated with a robotic manipulator for compliant force control. The system features five-dimensional (5DoF) contact force sensing, achieving accurate force measurement with a relative error of $\le 1.5\%$ . Experiments performed on phantoms and porcine tissues demonstrate the system's ability to suppress slippage effectively, ensure reliable force feedback, and improve safety and precision during thyroid surgery.

Currently, non-invasive continuous blood glucose monitoring technology remains insufficient in terms of clinical validation data. Existing approaches predominantly depend on statistical models to predict blood glucose levels, which often suffer from limited data samples. This leads to significant individual differences in non-invasive continuous glucose monitoring, limiting its scope and promotion. We propose a neural network that uses metabolic characteristics as inputs to predict the rate of insulin-facilitated glucose uptake by cells and postprandial glucose gradient changes (glucose gradient: the rate of change of blood glucose concentration within a unit of time (dG/dt), with the unit of mg/(dL × min), reflects the dynamic change trend of blood glucose levels). This neural network utilises non-invasive continuous glucose monitoring method based on the Bergman minimal model (BM-NCGM) while considering the effects of the glucose gradient, insulin action, and the digestion process on glucose changes, achieving non-invasive continuous glucose monitoring. This work involved 161 subjects in a controlled clinical trial, collecting over 15,000 valid data sets. The predictive results of BM-NCGM for glucose showed that the CEG A area accounted for 77.58% and the A + B area for 99.57%. The correlation coefficient (0.85), RMSE (1.48 mmol/L), and MARD (11.51%) showed an improvement of over 32% compared to the non-use of BM-NCGM. The dynamic time warping algorithm was used to calculate the distance between the predicted blood glucose spectrum and the reference blood glucose spectrum, with an average distance of 21.80, demonstrating the excellent blood glucose spectrum tracking ability of BM-NCGM. This study is the first to apply the Bergman minimum model to non-invasive continuous blood glucose monitoring research, supported by a large amount of clinical trial data, bringing non-invasive continuous blood glucose monitoring closer to its true application in daily blood glucose monitoring.   CLINICAL TRIAL REGISTRY NUMBER: ChiCTR1900028100.

Markerless tumor tracking in x-ray fluoroscopic images is an important technique for achieving precise dose delivery for moving lung tumors during radiation therapy. However, accurate tumor tracking is challenging due to the poor visibility of the target tumor overlapped by other organs such as rib bones. Dual-energy (DE) x-ray fluoroscopy can enhance tracking accuracy with improved tumor visibility by suppressing bones. However, DE x-ray imaging requires special hardware, limiting its clinical use. This study presents a deep learning-based DE subtraction (DES) synthesis method to avoid hardware limitations and enhance tracking accuracy. The proposed method employs a residual U-Net model trained on a simulated DES dataset from a digital phantom to synthesize DES from single-energy (SE) fluoroscopy. Experimental results using a digital phantom showed quantitative evaluation results of synthesis quality. Also, experimental results using clinical SE fluoroscopic images of ten lung cancer patients showed improved tumor tracking accuracy using synthesized DES images, reducing errors from 1.80 to 1.68 mm on average. The tracking success rate within a 25% movement range increased from 50.2% (SE) to 54.9% (DES). These findings indicate the feasibility of deep learning-based DES synthesis for markerless tumor tracking, offering a potential alternative to hardware-dependent DE imaging.

Traumatic brain injury (TBI) is one of the most prevalent health conditions, with severity assessment serving as an initial step for management, prognosis, and targeted therapy. Existing studies on automated outcome prediction using machine learning (ML) often overlook the importance of TBI features in decision-making and the challenges posed by limited and imbalanced training data. Furthermore, many attempts have focused on quantitatively evaluating ML algorithms without explaining the decisions, making the outcomes difficult to interpret and apply for less-experienced doctors. This study presents a novel supportive tool, named E-TBI (explainable outcome prediction after TBI), designed with a user-friendly web-based interface to assist doctors in outcome prediction after TBI using machine learning. The tool is developed with the capability to visualize rules applied in the decision-making process. At the tool's core is a feature selection and classification module that receives multimodal data from TBI patients (demographic data, clinical data, laboratory test results, and CT findings). It then infers one of four TBI severity levels. This research investigates various machine learning models and feature selection techniques, ultimately identifying the optimal combination of gradient boosting machine and random forest for the task, which we refer to as GBMRF. This method enabled us to identify a small set of essential features, reducing patient testing costs by 35%, while achieving the highest accuracy rates of 88.82% and 89.78% on two datasets (a public TBI dataset and our self-collected dataset, TBI_MH103). Classification modules are available at https://github.com/auverngo110/Traumatic_Brain_Injury_103 .

Reconfigurable cable-driven parallel robots (RCDPRs) have attracted much attention as a novel type of cable-driven robot that can change their cable anchor position. The reconfigurable cable-driven lower limb rehabilitation robot (RCDLR) employs RCDPRs in lower limb rehabilitation to achieve multiple training modes. This paper investigates the reconfiguration planning and structural parameter design of the RCDLR. The RCDLR aims to fulfill the requirements of early passive rehabilitation training. Therefore, motion capture data are analyzed and mapped to the target trajectory of the RCDLR. Through dynamics modeling, the Wrench-Feasible Anchor-point Space (WFAS) is defined, from which an objective function for optimal reconfiguration planning is derived. The genetic algorithm is used to solve the optimal reconfiguration planning problem. Additionally, we propose the reconfigurability and safety coefficients as components of a structure parameter design method aimed at satisfying multiple target rehabilitation trajectories. Finally, numerical simulations are implemented based on the instance data and target trajectories to compute the specific structure parameters and verify the effectiveness of the reconfiguration planning method.

Echocardiography sequence segmentation is vital in modern cardiology. While the Segment Anything Model (SAM) excels in general segmentation, its direct use in echocardiography faces challenges due to complex cardiac anatomy and subtle ultrasound boundaries. We introduce SAID (Segment Anything with Implicit Decoding), a novel framework integrating implicit neural representations (INR) with SAM to enhance accuracy, adaptability, and robustness. SAID employs a Hiera-based encoder for multi-scale feature extraction and a Mask Unit Attention Decoder for fine detail capture, critical for cardiac delineation. Orthogonalization boosts feature diversity, and I $_{}^2$ Net improves handling of misaligned contextual features. Tested on CAMUS and EchoNet-Dynamics datasets, SAID outperforms state-of-the-art methods, achieving a Dice Similarity Coefficient (DSC) of 93.2% and Hausdorff Distance (HD95) of 5.02 mm on CAMUS, and a DSC of 92.3% and HD95 of 4.05 mm on EchoNet-Dynamics, confirming its efficacy and robustness for echocardiography sequence segmentation.

Bone fractures are a common medical condition requiring accurate simulation for diagnosis and treatment planning. This study introduces a comprehensive method for simulating bone fractures using two-dimensional fracture patterns and real fractured bones applied to three-dimensional bone models. The approach begins with selecting and adjusting a fracture pattern, projecting it onto a 3D bone model and applying triangulation guided by quality metrics to simulate the cortical layer. Perturbation techniques add irregularities to the fracture surface, enhancing realism. Validation involved comparing simulated fragments with real fragments obtained from CT scans to ensure accuracy. Fracture patterns derived from real fragments were applied to non-fractured bone models to generate simulated fragments. A comparison of real and simulated fracture zones verified the minimal deviation in the results. Specifically, the distance between MMAR and MMAS scaled values varies between $-$ 0.36 and 1.44, confirming the accuracy of the simulation. The resulting models have diverse applications, such as accurate surgical planning, enhanced training, and medical simulation. These models also support personalized medicine by improving patient-specific surgical interventions. This advancement has the potential to significantly enhance fracture treatment strategies and elevate overall patient care.

Electroencephalography (EEG) usage in emotion recognition has garnered significant interest in brain-computer interface (BCI) research. Nevertheless, in order to develop an effective model for emotion identification, features need to be extracted from EEG data in terms of multi-view. In order to tackle the problems of multi-feature interaction and domain adaptation, we suggest an innovative network, IF-MMCL, which leverages multi-modal data in multi-view representation and integrates an individual focused network. In our approach, we build an individual focused network with multi-view that utilizes individual focused contrastive learning to improve model generalization. The network employs different structures for multi-view feature extraction and uses multi-feature relationship computation to identify the relationships between features from various views and modalities. Our model is validated using four public emotion datasets, each containing various emotion classification tasks. In leave-one-subject-out experiments, IF-MMCL performs better than the previous methods in model generalization with limited data.

Early identification of internal carotid artery (ICA) system diseases is critical for preventing stroke and other cerebrovascular events. Traditional diagnostic methods rely heavily on clinician expertise and costly imaging, limiting accessibility. This study aims to develop an interpretable machine learning (ML) model using common carotid artery (CCA) features to predict ICA disease risk, enabling efficient screening. Clinical data from 1612 patients (806 high-risk vs. 806 low-risk ICA disease) were analyzed. CCA features-blood flow, intima-media thickness, internal diameter, age, and gender-were used to train five ML models. Model performance was evaluated via accuracy, sensitivity, specificity, AUC-ROC, and F1 score. SHAP analysis identified key predictors. The support vector machine (SVM) achieved optimal performance (accuracy, 84.9%; AUC, 92.6%), outperforming neural networks (accuracy, 81.4%; AUC, 89.8%). SHAP analysis revealed CCA blood flow (negative correlation) and intima-media thickness (positive correlation) as dominant predictors. This study demonstrates that CCA hemodynamic and structural features, combined with interpretable ML models, can effectively predict ICA disease risk. The SVM-based framework offers a cost-effective screening tool for early intervention, particularly in resource-limited settings. Future work will validate these findings in multi-center cohorts.

Trauma has become a major cause of increased morbidity and mortality worldwide. In emergency response, the classification of injuries is crucial as it helps to quickly determine the criticality of the injured, allocate rescue resources rationally, and decide the priority order of treatment. However, emergency scenes are often chaotic environments, making it difficult for rescue personnel to collect complete and accurate information about the injured in a short period. The combination of artificial intelligence and emergency rescue is gradually changing the rescue model, improving the efficiency of rescue operations. We selected data from 26,810 trauma patients admitted to Chongqing Daping Hospital between 2013 and 2024. We propose a fast tiered medical treatment method with a two-layer structure under emergency limited data conditions, which integrates natural language processing (NLP) and machine learning (ML) techniques. The tiered medical treatment model utilizes NLP to capture semantic features of unstructured text data, while utilizing four ML algorithms to process structured numerical data. Additionally, we conducted external validation using 245 data entries from the Chongqing Emergency Center. The experimental results show that gradient boosting and logistic regression have the best performance in the two-layer ML algorithms. Based on these two algorithms, our model outperformed the multilayer perceptron (MLP) model on the test dataset, achieving an accuracy of 91.17%, which is 4.33% higher than that of the MLP model. The specificity, F1-score, and AUC of our model were 97.06%, 86.85%, and 0.949, respectively. For the external dataset, the model achieved accuracy, specificity, F1-score, and AUC of 87.35%, 95.78%, 80.37%, and 0.848, respectively. These results demonstrate the model's high generalizability and prediction accuracy. A model integrating NLP and ML techniques enables rapid tiered medical treatment based on limited data from the emergency scene, with significant advantages in terms of prediction accuracy.