To clarify the distribution of traditional Chinese medicine (TCM) pattern and its associated risk factors after percutaneous coronary intervention (PCI), and evaluate the reporting quality of existing studies to guide future research standardization.
English databases including PubMed, Cochrane Library, and Web of Science, as well as Chinese databases including China National Knowledge Infrastructure (CNKI), China Scientific Journal Database (VIP), and Wanfang Database were searched to retrieve papers about PCI. The time span for the paper retrieval was set from the foundation of the databases to October 1, 2023. Statistical analyses were performed using Stata 12 and Python (V 3.9). The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement was used to assess the reporting quality of included studies.
Overall, 1 356 articles were selected, and 40 cross-sectional studies were included with 10 270 participants. The most common TCM patterns before, one to two weeks after, and six months to one year after PCI was Qi stagnation and blood stasis (n = 261, 36.45%), intertwined phlegm and blood stasis (n = 109, 27.18%), and Qi deficiency and blood stasis (n = 645, 37.03%), respectively. Smoking [odds ratio (OR) = 1.15, 95% confidence interval (CI) (0.83 – 1.47), I2= 24.7%, P = 0.257], pattern of congealing cold and Qi stagnation [OR = 4.62, 95% CI (1.37 – 7.86), I2 = 61.6%, P = 0.074], and low-density lipoprotein (LDL) [OR = 1.38, 95% CI (0.92 – 1.85), I2= 12.2%, P = 0.286] were risk factors for restenosis. Hypertension [OR = 7.26, 95% CI (3.54 – 14.88), I2= 91.6%, P = 0.001], and overweight [i.e., body mass index (BMI) > 23] [OR = 1.20, 95% CI (1.07 – 1.35), I2= 85.3%, P = 0.009] were significant risk factors of concomitant anxiety.
This systematic review and meta-analysis revealed that patients with different TCM pattern types have distinct characteristics and risk factors after PCI. More high-quality studies are warranted to provide supportive evidence for future research and clinical practice.
The unintentional retention of needles in patients can lead to severe consequences. To enhance acupuncture safety, the study aimed to develop a deep learning-based cloud system for automated process of counting acupuncture needles.
This project adopted transfer learning from a pre-trained Oriented Region-based Convolutional Neural Network (Oriented R-CNN) model to develop a detection algorithm that can automatically count the number of acupuncture needles in a camera picture. A training set with 590 pictures and a validation set with 1 025 pictures were accumulated for fine-tuning. Then, we deployed the MMRotate toolbox in a Google Colab environment with a NVIDIA Tesla T4 Graphics processing unit (GPU) to carry out the training task. Furthermore, we integrated the model with a newly-developed Telegram bot interface to determine the accuracy, precision, and recall of the needling counting system. The end-to-end inference time was also recorded to determine the speed of our cloud service system.
In a 20-needle scenario, our Oriented R-CNN detection model has achieved an accuracy of 96.49%, precision of 99.98%, and recall of 99.84%, with an average end-to-end inference time of 1.535 s
The speed, accuracy, and reliability advancements of this cloud service system innovation have demonstrated its potential of using object detection technique to improve acupuncture practice based on deep learning.
Image-based intelligent diagnosis represents a trending research direction in the field of tongue diagnosis in traditional Chinese medicine (TCM). In recent years, machine learning techniques, including convolutional neural networks (CNNs) and Transformers, have been widely used in the analysis of medical images, such as computed tomography (CT) and nuclear magnetic resonance imaging (MRI). These techniques have significantly enhanced the efficiency and accuracy of decision-making in TCM practices. Advanced artificial intelligence (AI) technologies have also provided new opportunities for the research and development of medical equipment and TCM tongue diagnosis, resulting in improved standardization and intelligence of the tongue diagnostic procedures. Although traditional image analysis methods could transform tongue images into scientific and analyzable data, recognizing and analyzing images that capture complicated tongue features such as tooth-marked tongue, tongue spots and prickles, fissured tongue, variations in coating thickness, tongue texture (curdy and greasy), and tongue presence (peeled coating) continues posing significant challenges in contemporary tongue diagnosis research. Therefore, the employment of machine learning techniques in the analysis of tongue shape and texture features as well as their applications in TCM diagnosis is the focus of this study. In the study, both traditional and deep learning image analysis techniques were summarized and analyzed to figure out their value in predicting disease risks by observing the tongue shapes and textures, aiming to open a new chapter for the development and application of AI in TCM tongue diagnosis research. In short, the combination of TCM tongue diagnosis and AI technologies, will not only enhance the scientific basis of tongue diagnosis but also improve its clinical applicability, thereby advancing the modernization of TCM diagnostic and therapeutic practices.
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