Volume 3, Issue 4

Objectives: To investigate the feasibility of using eddy current damping (ECD) technology as a portable and rapid alternative for detecting the location of cerebral hemorrhage. Methods: This study leveraged the significant conductivity difference between hemorrhagic and normal brain tissue. ECD technology involved the application of alternating current to a coil, generating a time-varying magnetic field that induces eddy currents in conductive tissues such as hemorrhagic regions. These eddy currents produced a secondary magnetic field that opposes the original, resulting in measurable changes in the ECD signal. Using COMSOL simulations, detailed models of the head, brain, hemorrhage, and coil were constructed to analyze electromagnetic coupling. Changes in coil resistance and inductance due to hemorrhagic tissue were quantified to evaluate the detectability of ECD signals. Results: Simulation results confirmed that cerebral hemorrhages generate measurable eddy current responses,  which alter the resistance and inductance of the detection coil. These changes provide quantifiable ECD signals that can potentially indicate both the location and volume of hemorrhagic lesions. Conclusions: The findings demonstrate the feasibility of using ECD technology as a portable, rapid, and non-invasive method for detecting cerebral hemorrhage. Pending clinical validation, this approach could serve as a practical alternative to conventional imaging, particularly in emergency settings where speed and mobility are essential.

Objective: This study aimed to apply machine learning approaches to the Kellgren-Lawrence (KL) grading of knee osteoarthritis, develop an effective automatic KL grading technique, and provide a methodological reference for clinical diagnosis and research. Methods: Data were obtained from the Osteoarthritis Initiative (OAI) knee X-ray image dataset, comprising 8,110 images from the folders of auto_test, train, and val. All images were first subjected to inversion processing, followed by extraction of two-dimensional radiomic features. Feature selection was then conducted using a combination of variance thresholding and analysis of variance (ANOVA), yielding 18 key features. To address class imbalance in the original dataset, this synthetic minority over-sampling technique (SMOTE)  and class weight balancing were jointly applied. Eight machine learning models-Decision Trees (DT), Logistic Regression (LR), Random Forests (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Light Gradient Boosting Machine (LightGBM)-were trained for KL grading of knee osteoarthritis. Model performance was evaluated using accuracy, precision, recall, F1-score, and the area under the curve (AUC). For the optimal SVM model, global and local interpretability analy-ses were further conducted using SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) to identify the key factors influencing model decisions. Results: The support vector machine  (SVM) model achieves the best performance. Conclusion: This study establishes an effective machine learningbased method for automatic KL grading of knee osteoarthritis, providing valuable support for clinical diagnosis and research applications.

Objective: The morphological complexity of dermatologic diseases poses considerable challenges to clinical diagnosis. Conventional manual interpretation of skin images is time-consuming and influenced by subjective variability, which limits diagnostic accuracy. Hence, developing advanced medical image segmentation techniques through multi-method fusion is of particular importance. Methods: A comprehensive dataset of dermatologic images was utilized and rigorously preprocessed to ensure reliability and consistency. Two representative deep learning models, SegNet and U-Net, were optimized to achieve precise delineation of lesion areas. Building upon their complementary strengths, a novel fusion-based image segmentation framework was proposed, integrating both models to enhance performance through synergistic learning. The effectiveness of the ensemble strategy was validated through extensive experiments using standard evaluation metrics, including the Dice coefficient and Intersection over Union. Results: Compared with each individual model, the Ensemble Model yielded consistent improvements across all evaluation indices, with a notable reduction in loss values. These enhancements indicate markedly better learning efficiency and generalization in dermatologic image segmentation tasks. Conclusion: By integrating multiple deep learning algorithms, this fusion technique solves the misclassification and omission issues observed in single-model segmentation. It significantly improves overall segmentation accuracy and demonstrates superior performance, particularly in edge detection of complex skin lesions.

Vascular anastomosis, as one of the core surgical techniques, directly determines clinical efficacy in trauma repair, organ transplantation, and vascular reconstruction. This paper systematically reviews the development and current research status of vascular anastomosis techniques such as traditional manual sutures, robotic-assisted technologies, biomedical adhesives, and energy welding. Traditional manual sutures, regarded as the gold standard of vascular anastomosis, have achieved ultra-precise anastomosis at the 0.1-mm level through advancements in microsurgical techniques, making them the most clinically prevalent method. However, they are limited by issues such as foreign body retention, high demands on surgeon, prolonged operative times, and high postoperative stenosis rates. Robotic-assisted systems offer enhanced precision in complex anatomical regions, achieving submillimeter accuracy. However, their widespread adoption is constrained by high costs, reliance on suturing, and steep learning curves. Biomedical adhesives and energy welding techniques significantly reduce operative time but are not yet clinically applicable due to insufficient anastomotic strength. Although the GEM Coupler stapling devices have been clinically applied, other stapling technologies remain limited in scope, with ongoing research in structural designs and biodegradable materials. Future advances in vascular anastomosis are expected to focus on three directions: material innovation, technological breakthroughs, and clinical translation.

Electrosurgery is widely applied for precise tissue cutting and coagulation through high-frequency electrical energy, yet it carries the risk of collateral thermal injury. This review examines emerging strategies to mitigate such  damage, including cooling systems, real-time temperature monitoring, pulsed cutting, and laser- or ultrasoundassisted techniques. By improving temperature regulation, enhancing feedback accuracy, and enabling adaptive power control, these innovations reduce heat diffusion and tissue charring, thereby enhancing surgical safety and outcomes.

Objective: To propose a method for predicting immunotherapy outcome in non-small cell lung cancer based on computed tomography images before and after treatment. Methods: An improved U-net model incorporating Efficient Channel Attention was used to segment lesions. Radiomic features of lesions were extracted using PyRadiomics package and combined with biological indicators. Feature selection and dimensionality reduction were performed using linear discriminant analysis and Pearson correlation algorithms. A support vector machine was used to establish the predictive model. Results: The proposed segmentation model achieved a Dice coefficient of 90.09%, a positive predictive value of 89.23%, and an intersection over union of 82.15%, outperforming mainstream segmentation models. The proposed predictive model achieved an area under the curve of 85.05%, accuracy of 77.59%, specificity of 81.68% and sensitivity of 73.52%, all superior to models based solely on single-time computed tomography images or lacking biological features. Conclusion: The proposed method provides an effective approach for predicting the efficacy of immunotherapy in non-small cell lung cancer patients and offers a  valuable tool to support clinical decision-making.