OBJECTIVE: To investigate the effect of manual acupuncture manipulations (MAMs) on subcutaneous muscle tissue, by developing quantitative models of "lifting and thrusting" and "twisting and rotating", based on machine learning techniques. METHODS: A depth camera was used to capture the acupuncture operator's hand movements during "lifting and thrusting" and "twisting and rotating" of needle. Simultaneously, the ultrasound imaging was employed to record the muscle tissue responses of the participants. Amplitude and angular features were extracted from the movement data of operators, and muscle fascicle slope features were derived from the data of ultrasound images. The dynamic time warping barycenter averaging algorithm was adopted to align the dual-source data. Various machine learning techniques were applied to build quantitative models, and the performance of each model was compared. The most optimal model was further analyzed for its interpretability. RESULTS: Among the quantitative models built for the two types of MAMs, the random forest model demonstrated the best performance. For the quantitative model of the "lifting and thrusting" technique, the coefficient of determination (R²) was 0.825. For the "twisting and rotating" technique, R² reached 0.872. CONCLUSION Machine learning can be used to effectively develop the models and quantify the effects of MAMs on subcutaneous muscle tissue. It provides a new perspective to understand the mechanism of acupuncture therapy and lays a foundation for optimizing acupuncture technology and designing personalized treatment regimen in the future.
Humans ; Acupuncture Therapy/methods* ; Machine Learning ; Male ; Adult ; Female ; Subcutaneous Tissue/diagnostic imaging* ; Young Adult

Subcutaneous dirofilariasis is a parasitic infestation found in endemic areas in Mediterranean countries such as Italy. It is occasionally reported in India, mostly from the state of Kerala. Presentation in an infant is extremely rare. We report a case of subcutaneous dirofilariasis in a child that was diagnosed by ultrasonography and confirmed by surgery.
Connective Tissue Diseases ; diagnosis ; diagnostic imaging ; parasitology ; surgery ; Dirofilariasis ; diagnosis ; diagnostic imaging ; surgery ; Female ; Humans ; Infant ; Subcutaneous Tissue ; Ultrasonography

Based on a simple deconvolution model of multi-layer interfaces, the reasons of wave number variation of the interfacial echo signal were analyzed to explore a method for feature recognition of the superficial soft tissue interfaces. The interfacial echo signal data were decomposed and reconstructed by Mallat multisolution analysis, with the number of the reconstructed interface signal as the feature. The results showed that the deconvolution model was effective for extracting the interface echo signal features in the superficial soft tissue and allowed identification and location of tissue defects.
Animals ; Computer Simulation ; Connective Tissue ; diagnostic imaging ; Energy Transfer ; physiology ; Image Interpretation, Computer-Assisted ; Models, Theoretical ; Scattering, Radiation ; Skin ; diagnostic imaging ; Subcutaneous Tissue ; diagnostic imaging ; Swine ; Ultrasonography