Revealing Associations between Diagnosis Patterns and Acupoint Prescriptions Using Medical Data Extracted from Case Reports

Application of Virtual Reality Technology in Clinical Practice, Teaching, and Research in Complementary and Alternative Medicine

Background

The application of virtual reality (VR) in clinical settings is growing rapidly, with encouraging results. As VR has been introduced into complementary and alternative medicine (CAM), a systematic review must be undertaken to understand its current status.

Aim

This review aims to evaluate and summarize the current applications of VR in CAM, as well as to explore potential directions for future research and development.

Methods

After a brief description of VR technology, we discuss the past 20 years of clinical VR applications in the medical field. Then, we discuss the theoretical basis of the combination of VR technology and CAM, the research thus far, and practical factors regarding usability, etc., from the following three main aspects: clinical application, teaching, and scientific research. Finally, we summarize and propose hypotheses on the application of VR in CAM and its limitations.

Results

Our review of the theoretical underpinnings and research findings to date leads to the prediction that VR and CAM will have a significant impact on future research and practice.

Conclusion

Although there is still much research needed to advance the science in this area, we strongly believe that VR applications will become indispensable tools in the toolbox of CAM researchers and practitioners and will only grow in relevance and popularity in the era of digital health.

Deep autoencoder-powered pattern identification of sleep disturbance using multi-site cross-sectional survey data

Pattern identification (PI) is a diagnostic method used in Traditional East Asian medicine (TEAM) to select appropriate and personalized acupuncture points and herbal medicines for individual patients. Developing a reproducible PI model using clinical information is important as it would reflect the actual clinical setting and improve the effectiveness of TEAM treatment. In this paper, we suggest a novel deep learning-based PI model with feature extraction using a deep autoencoder and k-means clustering through a cross-sectional study of sleep disturbance patient data. The data were obtained from an anonymous electronic survey in the Republic of Korea Army (ROKA) members from August 16, 2021, to September 20, 2021. The survey instrument consisted of six sections: demographics, medical history, military duty, sleep-related assessments (Pittsburgh sleep quality index (PSQI), Berlin questionnaire, and sleeping environment), diet/nutrition-related assessments [dietary habit survey questionnaire and nutrition quotient (NQ)], and gastrointestinal-related assessments [gastrointestinal symptom rating scale (GSRS) and Bristol stool scale]. Principal component analysis (PCA) and a deep autoencoder were used to extract features, which were then clustered using the k-means clustering method. The Calinski-Harabasz index, silhouette coefficient, and within-cluster sum of squares were used for internal cluster validation and the final PSQI, Berlin questionnaire, GSRS, and NQ scores were used for external cluster validation. One-way analysis of variance followed by the Tukey test and chi-squared test were used for between-cluster comparisons. Among 4,869 survey responders, 2,579 patients with sleep disturbances were obtained after filtering using a PSQI score of >5. When comparing clustering performance using raw data and extracted features by PCA and the deep autoencoder, the best feature extraction method for clustering was the deep autoencoder (16 nodes for the first and third hidden layers, and two nodes for the second hidden layer). Our model could cluster three different PI types because the optimal number of clusters was determined to be three via the elbow method. After external cluster validation, three PI types were differentiated by changes in sleep quality, dietary habits, and concomitant gastrointestinal symptoms. This model may be applied to the development of artificial intelligence-based clinical decision support systems through electronic medical records and clinical trial protocols for evaluating the effectiveness of TEAM treatment.

Effects of Stimulating Local and Distal Acupoints on Diabetic Gastroparesis: A New Insight in Revealing Acupuncture Therapeutics

One of the most important aspects of clinical acupuncture practice, like diabetic gastroparesis, is the selection of suitable acupoints. Furthermore, it is critical to examine the therapeutic impact differences between distal and local acupoints, as well as the prescription of their combination. In this study, diabetic gastroparesis rats were treated by needling Zhongwan (CV12) and Zusanli (ST36), and then used Single Photon Emission Computed Tomography-CT (SPECT-CT) technology to assess the effects of promoting gastric motility. In addition, morphological observation, immunohistochemical examination, and biomarker assays, such as determination of growth factor 1, motilin, and ghrelin contents in serum samples, were performed to better understand the impact of certain various acupuncture treatments. All of the therapies improved the symptoms of diabetic gastroparesis rats, according to the findings. Stimulating these acupoints, on the other hand, can have a different therapeutic effect. In addition, needling local and distal acupoints together can have an antagonistic or synergistic impact on specific physiological and biochemical indexes such as gastric motility, ghrelin, gastrin, and growth factor 1, among others. Our findings demonstrated the benefits of acupoints and acupuncture in the management of diabetic gastroparesis, as well as a new insight into acupuncture therapeutics.

Diversity of Acupuncture Point Selections According to the Acupuncture Styles and Their Relations to Theoretical Elements in Traditional Asian Medicine: A Data-Mining-Based Literature Study

Acupuncture point (AP) selections can vary depending on clinicians’ acupuncture style, and therefore, acupuncture style is an important factor in determining the efficacy of acupuncture treatment. However, few studies have examined the differences in AP selections according to the acupuncture styles and theoretical backgrounds causing the differences. We compared the AP prescriptions used for 14 diseases in three classical medical textbooks, Dongeuibogam (DEBG), Saamdoinchimgooyogyeol (SADI), and Chimgoogyeongheombang (CGGHB), which represent unique acupuncture styles and have affected clinicians during this time. AP prescriptions showed more diversity between textbooks than between types of diseases. Among the three textbooks, AP prescriptions of SADI were most different compared to those of DEBG and CGGHB. Importantly, we found each style can be more clearly explained by AP attributes than by the APs per se. Specifically, SADI, DEBG, and CGGHB preferred five transport points located on the limbs, APs of the extra meridians, and source points, respectively. This suggests the possibility that the theoretical diversity of acupuncture styles results in the heterogeneity of AP selections.

Identification of Acupoint Indication from Reverse Inference: Data Mining of Randomized Controlled Clinical Trials

The specificity of acupoint indication (i.e., reverse inference—diseases for which an acupoint could be used) might differ from the specificity of acupoint selection (i.e., forward inference—acupoints used for a disease). In this study, we explore acupoint specificity through reverse inferences from the dataset of prescribed acupoints for a certain disease in clinical trials. We searched acupuncture treatment regimens in randomized controlled trials included in the Cochrane Database of Systematic Reviews. For forward inference, the acupoints prescribed for each disease were quantified. For reverse inference, diseases for each acupoint were quantified. Data were normalized using Z-scores. Bayes factor correction was performed to adjust for the prior probability of diseases. The specificities of acupoint selections in 30 diseases were determined using forward inference. The specificities of acupoint indications regarding 49 acupoints were identified using reverse inference and then subjected to Bayes factor correction. Two types of acupoint indications were identified for 24 acupoints: regional and distal. Our approach suggests that the specificity of acupoint indication can be inferred from clinical data using reverse inference. Acupoint indication will improve our understanding of acupoint specificity and will lead to the establishment of a new model of analysis and educational resources for acupoint characteristics.

Commonality and Specificity of Acupuncture Point Selections

OBJECTIVE

Because individual acupoints have a wide variety of indications, it is difficult to accurately identify the associations between acupoints and specific diseases. Thus, the present study aimed at revealing the commonality and specificity of acupoint selections using virtual medical diagnoses based on several cases.

METHODS

Eighty currently practicing Korean Medicine doctors were asked to prescribe acupoints for virtual acupuncture treatment after being presented with medical information extracted from 10 case reports. The acupoints prescribed for each case were quantified; the data were normalised and compared among the 10 cases using z-scores. A hierarchical cluster analysis was conducted to categorise diseases treated based on the acupoint prescription patterns. Additionally, network analyses were performed on the acupoint prescriptions, at the individual case and cluster level.

RESULTS

Acupoints ST36, LI4, and LR3 were most commonly prescribed across all diseases. Regarding the specific acupoints prescribed in each cluster, acupoints around the disease site (knee and lower back) were frequently used in cluster A (musculoskeletal symptoms), acupoints LI4, LR3, PC6, and KI3 were frequently used in cluster B (psychiatric symptoms), and acupoints ST36, LI4, LR3, PC6, CV12, and SP6 were frequently used in cluster C (several symptoms of diseases of internal medicine).

CONCLUSIONS

The present study identified the commonality and specificity of acupoint selections based on virtual acupuncture treatments prescribed by practicing clinicians. Acupoint selection patterns, which were defined using a top-down approach in previous studies and classical medical texts, may be further elucidated using a bottom-up approach based on patient medical records.

A Key Node Mining Method Based on Acupoint-Disease Network (ADN): A New Perspective for Exploring Acupoint Specificity

In the process of treating pro-diseases with acupuncture, traditional Chinese medicine (TCM) doctors may fine-tune acupuncture prescriptions according to different prior experiences. Different prescriptions will affect the efficiency and effect of acupuncture treatment, and even excessive acupoint selection may cause psychological pressure on patients. We still lack an effective means to analyze the meridian system and acupoint specificity to clarify the mapping relationship between acupoints and diseases. Given the inability of modern medical technology to provide effective evidence support for meridians and acupoints, we combined acupuncture theory with network science for an interdisciplinary discussion. In this paper, we constructed a weighted undirected acupoint-disease network (ADN) based on clinical acupuncture prescription literature and proposed a high-specificity key node mining method based on ADN. Combined with the principle of acupoint selection in TCM, the proposed method balanced the contribution of local areas to the network based on the distribution characteristics of meridians and selected 30 key acupoints with high influence on the global topology according to the evaluation index of key nodes. Finally, we compared the proposed method with the other six classical node importance evaluation algorithms in terms of resolution, network loss, and accuracy. The comprehensive results show that the marked key acupoint nodes make outstanding contributions to the connectivity, topological structure, and weighted benefits of the network, and the stability and specificity of the algorithm guarantee the reliability of the key acupoint nodes. We consider that these key acupoints with high centrality in ADN can be used as core acupoints to help researchers explore targeted and high-impact acupoint combinations under resource constraints and optimize existing acupuncture prescriptions.

Statistical inference of acupoint specificity: forward and reverse inference

Background

The acupoint specificity has been considered important issue in acupuncture research. In clinical aspects, it is essential to identify which acupoints are associated specifically with a particular disease. The present study aimed to identify the specificity of acupoint selection (forward inference) and the specificity of acupoint indication (reverse inference) from the online virtual diagnosis experiment.

Methods

Eighty Korean Medicine doctors conducted the virtual medical diagnoses provided for 10 different case reports. For forward inference, the acupoints prescribed for each disease were quantified and the data were normalised among 30 frequently used acupoints using Z-scores. For reverse inference, diseases for each acupoint were quantified and the data were normalized among 10 disease using Z-scores.

Results

Using forward inference we demonstrated the specificity of acupoint selection in each disease. Using reverse inference we identified the specificity of acupoint indication in each acupoint. In general, a certain acupoint can be selected specifically for a particular disease, and it has a specific indication for the disease. However, the specificity of acupoint indication and the specificity of acupoint selection are not always identical.

Conclusions

The selection of an acupoint for a particular disease does not imply that the acupoint has specific indications for that disease. Inferring the specificity of acupoint indication from clinical observations should be considered.