Volume 2, Issue 3

Atrial fibrillation is a common cardiac arrhythmia with an annually increasing global prevalence. Ablation of atrial  fibrillation is a minimally invasive procedure that treats atrial fibrillation by using a catheter to deliver radiofrequency energy to heart tissues generating abnormal electrical potentials. The success of this procedure relies significantly on the adhesion between the catheter and the heart tissue, presenting a challenge in accurately assessing  the contact force (CF) during surgery. To improve the safety and success rate of surgery, researchers are committed to developing various methods to evaluate or detect catheter-tissue CF. Among these, some studies integrated  optical fibers or magnetic elements into the catheter tip to create CF sensing catheters that monitor CF in real  time; other studies used impedance measurement, electrical coupling index, local impedance and other methods  to evaluate the CF between the catheter and the tissue by measuring changes in electrical signals. These methods  have achieved certain success in clinical practice, offering new ways to improve the effectiveness and safety of  cardiac radiofrequency ablation surgery.

The growing demand for healthcare has brought Traditional Chinese Medicine (TCM) massage therapy into the spotlight in academic circles. Numerous studies have underscored the effectiveness of TCM massage in health promotion, disease amelioration, and quality of life enhancement. However, the field faces challenges such as inconsistent training and inadequate transfer of experiential knowledge. Recently, machine learning has shown potential in the medical field and its application in TCM massage therapy offers new developmental opportunities. This paper reviews key research areas exploring the synergy between machine learning and Chinese massage therapy, including acupoint localization and identification, massage practice, and personalized treatment plans. It summarizes progress and identifies the challenges in integrating these technologies. Despite potential risks, merging these technologies is poised to be a trend in future healthcare, driven by advances in computer technology and the needs of TCM practitioners.

Purpose: This study investigates the effects of varying inner diameter of a vascular clamp made from an Mg–Nd–Zn–Zr alloy on its functional performance. The primary objectives are to optimize the clamp’s structure, assess its performance across different inner diameters, and ultimately determine the optimal configuration. Methods: We developed a V-shaped vascular clamp equipped with a locking mechanism and transverse teeth. The study involved comparing vascular clamps with various inner diameters (0.35 mm, 0.4 mm, 0.5 mm and 0.6 mm, denoted as R0.35, R0.4, R0.5 and R0.6, respectively), achieved by modifying the clamp design. Finite element analysis simulated the closure process of these clamps, both with and without blood vessels, to analyze stress and strain distribution. Subsequently, we manufactured a clamp with the optimized design and conducted performance evaluations, including a closing strength test and an in vitro immersion test. Results: Among the tested vascular clamps, the R0.5 clamp demonstrated the lowest strain (0.50798) and minimal stress on blood vessels (0.7629 MPa). Notably, the R0.5 clamp remained intact during clamping fracture experiments and demonstrated a maximum closing force of 334.98±15.4 mmHg. Regarding corrosion resistance, the clamped position showed a higher corrosion rate (0.179±0.00551 mg.cm^-2.day^-1) compared to the open clamp (0.161±0.00306 mg.cm^-2.day^-1). Conclusion: The R0.5 clamp demonstrated superior performance in finite element analysis, showing effective vascular closure, strong clamping force, and uniform corrosion behavior. Overall, these results highlight its potential as an effective tool for vascular closure.

Objective: Machine learning can automatically extract valuable insights from vast datasets, predict and classify diseases, and evaluate drug efficacy. To assess the effectiveness of machine learning algorithms in analyzing non-formed components in urine, real medical data were processed and annotated. Methods: Five models, including K-Nearest Neighbors, Decision Trees, Random Forests, Support Vector Machines, and Gaussian distributions,were constructed to quantitatively analyze 12 non-formed urine components, such as vitamin C, white blood cells, and urinary bilirubin. The efficacy of these models was then compared. Results: It was found that the RandomForest model outperformed others, achieving the lowest mean squared error, high recall rate, accuracy, and areaunder the curve. Conclusions: These findings indicate that machine learning offers significant potential for studying non-formed urine components, potentially enhancing the precision and effectiveness of disease detection andproviding valuable support for clinical decision-making.

Objective: To construct a prediction model for fatty liver disease (FLD) among elderly residents in community using machine learning (ML) algorithms and evaluate its effectiveness. Methods: The physical examination data of 4989 elderly people (aged over 60 years) in a street of Shanghai from 2019 to 2023 were collected. The subjects were divided into a training set and a testing set in a 7:3 ratio. Using feature selection and importance sorting methods, eight indicators were selected, including high-density lipoprotein cholesterol, body mass index, uric acid, triglycerides, albumin, red blood cell, white blood cell, and alanine aminotransferase. Six ML models, including Categorical Features Gradient Boosting, eXtreme Gradient Boosting, Light Gradient Boosting Machine, Random Forest, Decision Tree, and Logistic Regression, were constricted, and their predictive performances were compared via accuracy, precision, recall, F1 score, and Area Under Receiver Operating Characteristic Curve. Results: Among the six ML models, the Categorical Features Gradient Boosting model demonstrated the highest prediction accuracy of 0.74 for FLD in elderly community population, along with a precision of 0.70, a recall of 0.73, a F1 score of 0.71, and an area under the curve of 0.74. Conclusions: In the context of rapid development of artificial intelligence, a community-based elderly FLD prediction model constructed using ML algorithms aid family general practitioners in the early diagnosis, early treatment, and health management of local FLD patients.