This study aims to develop an abdominal acupoint localization system based on computer vision and convolutional neural networks (CNNs). To address the challenge of abdominal acupoint localization, a multi-task CNNs architecture was constructed and trained to locate the Shenque (CV8) and human body boundaries. Based on the identified Shenque (CV8), the system further deduces key characteristics of four acupoints: Shangwan (CV13), Qugu (CV2), and bilateral Daheng (SP15). An affine transformation matrix is applied to accurately map image coordinates to an acupoint template space, achieving precise localization of abdominal acupoints. Testing has verified that this system can accurately identify and locate abdominal acupoints in images. The development of this localization system provides technical support for TCM remote education, diagnostic assistance, and advanced TCM equipment, such as intelligent acupuncture robots, facilitating the standardization and intelligent advancement of acupuncture.
Acupuncture Points ; Humans ; Deep Learning ; Abdomen/diagnostic imaging* ; Neural Networks, Computer ; Acupuncture Therapy ; Image Processing, Computer-Assisted

OBJECTIVE: To screen the population for acupuncture treatment of neck pain, using functional magnetic resonance imaging (fMRI) technology and based on machine learning algorithms. METHODS: Eighty patients with neck pain were recruited. Using FPX25 handheld pressure algometer, the tender points were detected in the areas with high-frequent onset of neck pain and high degree of acupoint sensitization. Acupuncture was delivered at 4 tender points with the lowest pain threshold, once every two days; and the treatment was given 3 times a week and for 2 consecutive weeks. The amplitude of low-frequency fluctuation (ALFF) of the brain before treatment was taken as a predictive feature to construct support vector machine (SVM), logistic regression (LR), and K-nearest neighbors (KNN) models to predict the responses of neck pain patients to acupuncture treatment. A longitudinal analysis of the ALFF features was performed before and after treatment to reveal the potential biological markers of the reactivity to the acupuncture therapy. RESULTS: The SVM model could successfully distinguish high responders (48 cases) and low responders (32 cases) to acupuncture treatment, and its accuracy rate reached 82.5%. Based on the SVM model, the ALFF values of 4 brain regions were identified as the consistent predictive features, including the right middle temporal gyrus, the right superior occipital gyrus, and the bilateral posterior cingulate gyrus. In the patients with high acupuncture response, the ALFF value in the left posterior cingulate gyrus decreased after treatment (P<0.05), whereas in the patients with low acupuncture response, the ALFF value in the right superior occipital gyrus increased after treatment (P<0.01). The longitudinal functional connectivity (FC) analysis found that compared with those before treatment, the high responders showed the enhanced FC after treatment between the left posterior cingulate gyrus and various regions, including the bilateral Crus1 of the cerebellum, the right insula, the bilateral angular gyrus, the left medial superior frontal gyrus, and the left middle cingulate gyrus (GRF: corrected, voxel level: P<0.05, mass level: P<0.05). In contrast, the low responders exhibited the enhanced FC between the left posterior cingulate gyrus and the left Crus2 of the cerebellum, the left middle temporal gyrus, the right posterior cingulate gyrus, and the left angular gyrus; besides, FC was reduced in low responders between the left posterior cingulate gyrus and the right supramarginal gyrus (GRF: corrected, voxel level: P<0.05, mass level: P<0.05). CONCLUSION This study validates the practicality of pre-treatment ALFF feature prediction for acupuncture efficacy on neck pain. The therapeutic effect of acupuncture on neck pain is potentially associated with its impact on the default mode network, and then, alter the pain perception and emotional regulation.
Humans ; Neck Pain/physiopathology* ; Acupuncture Therapy ; Female ; Male ; Adult ; Middle Aged ; Machine Learning ; Magnetic Resonance Imaging ; Young Adult ; Brain/physiopathology* ; Acupuncture Points ; Aged

The machine learning is used increasingly and widely in acupuncture prescription optimization, intelligent treatment and precision medicine, and has obtained a certain achievement. But, there are still some problems remained to be solved such as the poor interpretability of the model, the inconsistency of data quality of acupuncture research, and the clinical application of constructed models. Researches in future should focus on the acquisition of high-quality clinical and experimental data sets, take various machine learning algorithms as the basis, and construct professional models to solve various problems, so as to drive the high-quality development of acupuncture research.
Acupuncture Therapy/trends* ; Machine Learning ; Humans ; Algorithms

OBJECTIVE: To construct a prediction model for the clinical effect of eye acupuncture combined with rehabilitation therapy on ischemic stroke based on interpretable machine learning. METHODS: From January 1st, 2020 to October 1st, 2024, the clinical data of 470 patients with ischemic stroke were collected in the the Second Department of Encephalopathy Rehabilitation of the Affiliated Hospital of Liaoning University of TCM. The modified Barthel index (MBI) score before and after treatment was used to divide the patients into an effect group (291 cases) and a non-effect group (179 cases). Random forest and recursive feature elimination with cross-validation were combined to screen the predictors of the therapeutic effect of patients. Seven representative machine learning models with different principles were established according to the screening results. The predictive effect of the best model was evaluated by receiver operating characteristics (ROC), calibration, and clinical decision-making (DCA) curves. Finally, the Shapley additive explanation (SHAP) framework was used to interpret the prediction results of the best model. RESULT: ①All the machine learning models presented the area under curve (AUC) to be above 85%. Of these models, the random forest model showed the best prediction ability, with AUC of 0.96 and the precision of 0.87. ②The prediction probability of calibration curve and the actual probability showed a good prediction consistency. ③The net benefit rate of DCA curve in the range of 0.1 to 1.0 was higher than the risk threshold, indicating a good effect of model. ④SHAP explained the characteristic values of variables that affected the prediction effect of the model, meaning, more days of treatment, lower MBI score before treatment, lower level of fibrinogen, shorter days of onset and younger age. These values demonstrated the better effect of eye acupuncture rehabilitation therapy. CONCLUSION The rehabilitation effect prediction model constructed in this study presents a good performance, which is conductive to assisting doctors in formulating targeted personalized rehabilitation programs, and identifying the benefit groups of eye acupuncture combined with rehabilitation therapy and finding the advantageous groups with clinical effect. It provides more ideas for the treatment of ischemic stroke with eye acupuncture combined with rehabilitation therapy.
Humans ; Acupuncture Therapy ; Machine Learning ; Male ; Female ; Middle Aged ; Ischemic Stroke/rehabilitation* ; Aged ; Stroke Rehabilitation ; Adult ; Eye

This paper reviews the published articles of recent years on the application of deep learning methods in automatic localization of acupoint, and summarizes it from 3 key links, i.e. the dataset construction, the neural network model design, and the accuracy evaluation of acupoint localization. The significant progress has been obtained in the field of deep learning for acupoint localization, but the scale of acupoint detection needs to be expanded and the precision, the generalization ability, and the real-time performance of the model be advanced. The future research should focus on the support of standardized datasets, and the integration of 3D modeling and multimodal data fusion, so as to increase the accuracy and strengthen the personalization of acupoint localization.
Deep Learning ; Acupuncture Points ; Humans ; Neural Networks, Computer

OBJECTIVE: To develop an objective quantitative evaluative model of manual acupuncture manipulation (MAM) using three-dimensional motion tracking technology and machine learning, so as to provide a new approach to the study on acupuncture and moxibustion education and manipulation standardization. METHODS: A total of 120 undergraduate students in the major of acupuncture-moxibustion and tuina were recruited. The Simi Motion Ver.8.5 motion tracking system was used to collect the data of three types of MAM, balanced reinforcing and reducing by twisting, reinforcing technique by twisting and reducing technique by twisting. Eight quantitative parameters covering movement performance and stability were established. With 5 types of machine learning algorithms (logistic regression, random forest, support vector machine, K-nearest neighbor, and decision tree) adopted, the evaluative model was constructed, and the feature importance analyzed. RESULTS: In the evaluation of different types of MAM, the support vector machine presented the best for the effects of the balanced reinforcing and reducing by twisting, and the reducing by twisting (accuracy rates were both 0.88); and the logistic regression algorithm showed the optimal performance in evaluating the reinforcing by twisting (1.00 of accuracy rate). Feature importance analysis revealed that twisting velocity was the dominant parameter for evaluating the balanced reinforcing-reducing manipulation. The reinforcing and reducing of acupuncture techniques were more dependent on the left-hand twisting parameters and comprehensive performances, respectively. CONCLUSION The objective evaluative model of MAM based on three-dimensional motion tracking technology and machine learning demonstrates a reliable evaluative performance, providing a new technical approach to standardized assessment in acupuncture and moxibustion education.
Humans ; Male ; Acupuncture Therapy/instrumentation* ; Machine Learning ; Female ; Young Adult ; Adult ; Motion

OBJECTIVE: To propose a machine learning-based method for predicting the trajectories during manual acupuncture manipulation (MAM), aiming to improve the precision and consistency of acupuncture practitioner' operation and provide the real-time suggestions on MAM error correction. METHODS: Computer vision technology was used to analyze the hand micromotion when holding needle during acupuncture, and provide a three-dimensional coordinate description method of the index finger joints of the holding hand. Focusing on the 4 typical motions of MAM, a machine learning-based MAM trajectory predictive model was designed. By integrating the changes of phalangeal joint angle and hand skeletal information of acupuncture practitioner, the motion trajectory of the index finger joint was predicted accurately. Besides, the roles of machine learning-based MAM trajectory predictive model in the skill transmission of acupuncture manipulation were verified by stratified randomized controlled trial. RESULTS: The performance of MAM trajectory predictive model, based on the long short-term memory network (LSTM), obtained the highest stability and precision, up to 98%. The learning effect was improved when the model applied to the skill transmission of acupuncture manipulation. CONCLUSION The machine learning-based MAM predictive model provides acupuncture practitioner with precise action prediction and feedback. It is valuable and significant for the inheritance and error correction of manual operation of acupuncture.
Humans ; Acupuncture Therapy/instrumentation* ; Machine Learning ; Adult ; Male ; Female

OBJECTIVE: To observe the effect of electroacupuncture at "Sishencong" (EX-HN1) and "Fengchi" (GB20) on hippocampal neuronal ferritinophagy mediated by the nuclear receptor coactivator 4 (NCOA4)/ferritin heavy chain 1 (FTH1) signaling pathway in vascular dementia (VD) rats, and to explore the potential mechanisms of electroacupuncture for VD. METHODS: A total of 60 male rats of SPF grade were randomly divided into a blank group (12 rats), a sham surgery group (12 rats) and a modeling group (36 rats). In the modeling group, the modified 4-vessel occlusion method was used to establish the VD model. The 24 successfully modeled rats were randomly divided into a model group and an electroacupuncture group, with 12 rats in each group. In the electroacupuncture group, electroacupuncture was applied at left and right "Sishencong" (EX-HN1), and bilateral "Fengchi" (GB20), with continuous wave, in frequency of 2 Hz and current intensity of 1 mA, 30 min a time, once daily for 21 consecutive days. The learning and memory abilities were assessed using the Morris water maze test before modeling, after modeling and after intervention, as well as the novel object recognition test after intervention. After intervention, the neuronal morphology in the hippocampus was observed by Nissl staining; the iron deposition was observed by Prussian blue staining; the reactive oxygen species (ROS) level was detected by dihydroethidium (DHE) fluorescence staining; the levels of iron, malondialdehyde (MDA) and superoxide dismutase (SOD) in the hippocampal tissue were measured by the colorimetric assay, TBA method, and WST-1 method, respectively; the positive expression of NCOA4, FTH1 and glutathione peroxidase 4 (GPX4) was detected by immunohistochemistry; the protein expression of NCOA4, FTH1, GPX4, and the ratio of microtubule-associated protein 1 light chain 3B (LC3B) Ⅱ/Ⅰ in the hippocampus were detected by Western blot. RESULTS: Compared with the sham surgery group, in the model group, the escape latency was prolonged, and the number of platform crossings reduced (P<0.01), the recognition index (RI) was decreased (P<0.01); the hippocampal neurons displayed a blurred laminar structure, disorganized cellular arrangement, and the number of Nissl bodies was decreased (P<0.01); the percentage of iron deposition area in the hippocampus was increased (P<0.01); in the hippocampus, the levels of ROS, iron, MDA, and the protein expression of NCOA4, as well as the LC3B Ⅱ/Ⅰ ratio were increased (P<0.01), the SOD level, and the protein expression of FTH1 and GPX4 were decreased (P<0.01). Compared with the model group, in the electroacupuncture group, the escape latency was shortened and the number of platform crossings was increased (P<0.01), the RI was increased (P<0.01); the hippocampal neurons exhibited more regular morphology, better-organized cellular structure, and the number of Nissl bodies was increased (P<0.05); the percentage of iron deposition area in the hippocampus reduced (P<0.01); in the hippocampus, the levels of ROS, iron, MDA, and the protein expression of NCOA4, as well as the LC3B Ⅱ/Ⅰ ratio were decreased (P<0.01, P<0.05), the SOD level, and the protein expression of FTH1 and GPX4 were increased (P<0.01). CONCLUSION Electroacupuncture at "Sishencong" (EX-HN1) and "Fengchi" (GB20) can improve learning and memory abilities in VD rats, and its mechanism may be associated with the regulation of the hippocampal NCOA4/FTH1 signaling pathway, inhibition of ferritinophagy, and alleviation of oxidative stress damage.
Animals ; Electroacupuncture ; Dementia, Vascular/genetics* ; Male ; Rats ; Signal Transduction ; Humans ; Memory ; Rats, Sprague-Dawley ; Nuclear Receptor Coactivators/genetics* ; Ferritins/genetics* ; Learning ; Hippocampus/metabolism* ; Acupuncture Points

OBJECTIVE: To construct a predictive model for the reduction in methadone maintenance treatment (MMT) and evaluate the effects of different interventions and other clinical factors on methadone reduction using Shapley additive explanations (SHAP). METHODS: Two clinical trials of acupuncture for methadone reduction in MMT patients were analyzed, and the baseline data, MMT related information, intervention measures, the data related to dose-reduction outcomes were collected. The predictive model was constructed by means of 6 machine learning algorithms including support vector machine (SVM), K-nearest neighbors (KNN), logistic regression (LR), Naive Bayes (NB), random forest (RF) and categorical-boosting (CatBoost), and 2 integration methods, blending-ensemble method (Blending) and Stacking-ensemble method (Stacking). SHAP was employed for the interpretability analysis of the optimal model. RESULTS: A total of 251 MMT patients were included, 128 cases in the acupuncture group and 123 cases in the non-acupuncture group. CatBoost and Stacking performed optimally in the test set. CatBoost obtained an accuracy of 0.780 0±0.060 8, a precision of 0.500 0±0.120 0, a recall of 0.818 2±0.140 2, F1 score of 0.620 7±0.114 0, and receiver operating characteristic-area under curve (ROC-AUC) of 0.857 8±0.140 2 for the subjects. In MMT patients with acupuncture as an adjunctive therapy, the top 5 important features for methadone reduction, included intervention measures, body mass index (BMI), the duration of MMT, the history of opioid use and occupation; and SHAP values were 1.25, 0.36, 0.21, 0.19 and 0.12, respectively. The SHAP feature dependence plot showed that BMI, MMT duration and the history of opioid use presented a nonlinear negative correlation with the reduction effect. CONCLUSION In acupuncture as adjunctive therapy for methadone reduction, the clinical factors should be considered comprehensively; and the interpretable predictive model provides a scientific basis for it, which is conducive to the improvement of clinical strategy of acupuncture for methadone reduction and the development of personalized reduction scheme.
Humans ; Methadone/therapeutic use* ; Acupuncture Therapy ; Adult ; Female ; Male ; Machine Learning ; Middle Aged ; Opiate Substitution Treatment ; Young Adult

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