Differences in Complexion between Cold‐ and Heat‐Prescription Groups in Sasang Medicine

Park, Young Joo; Do, Jun-Hyeong; Kim, Honggie; Kim, Jong Yeol

doi:10.1155/2017/9701978

Cited by 4 publications

(4 citation statements)

References 13 publications

(14 reference statements)

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“…In terms of facial variables used in our study, facial characteristics (shape) and complexion (color of face) were often used to diagnose diseases or prescribe herbal medicine, such as Taeumjowi-tang [35][36][37]. For example, Park et al [35] investigated the association of facial complexion with age, BMI, and systolic blood pressure for cold-and heat-prescription, such as Taeumjowi-tang, and documented significant differences in complexion between cold-prescription and heat-prescription subjects. This implies that prescriptions such as Taeumjowi-tang may differ according to individual patterns and that the efficacy of herbal medicine may differ according to individuals.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Efficacy of Taeumjowi-Tang for Treatment of Metabolic Risk Factors Based on Machine Learning

et al. 2021

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Herbal medicine is widely prescribed worldwide. To date, however, studies on the prediction of efficacy of herbal medicine based on machine learning have very rarely been reported. The objectives of this study are to predict the efficacy of Taeumjowi-tang (one of herbal medicines) and evaluate the prediction model in treating metabolic abnormalities. Subjects were divided into an improvement group and a non-improvement group based on the difference before and after oral administration of an herbal medicine. Efficacy models of triglyceride level, high-density lipoprotein (HDL) cholesterol level, systolic blood pressure (SBP), and diastolic blood pressure (DBP) were built using a least absolute shrinkage and selection operator (LASSO) based on variables extracted from face shape, face colors, body circumference, questionnaire, voice, and tongue color. In predicting efficacy for four metabolic risk factors, the efficacy model of HDL cholesterol level showed the best the area under the receiver operating characteristic curve (AUC) value among the four models (AUC = 0.785 (confidence interval = 0.693, 0.877)). The AUC value of the efficacy model of triglyceride level was 0.659 (0.551, 0.768). Efficacy models of DBP and SBP showed AUC values of 0.665 (0.551, 0.78) and 0.54 (0.385, 0.694), respectively. The results may provide a clue to predict whether a drug will be effective for each subject with phenotypic information and to reduce the use of an ineffective drug or its side effects.

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Section: Discussionmentioning

confidence: 99%

Prediction of Efficacy of Taeumjowi-Tang for Treatment of Metabolic Risk Factors Based on Machine Learning

et al. 2021

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“…A frontal full-face photograph was taken with a neutral expression using a Nikon D700/D5100 digital camera with an 85 mm lens under bilateral illumination at a fixed subject-camera distance of 1.6 m. A color chart used for color correction was attached to a ruler that was placed approximately 1 cm below the chin ( Figure 1 ) [ 23 ]. All subjects were requested to present their natural fresh-face without any makeup.…”

Section: Methodsmentioning

confidence: 99%

“… Three regions of facial complexion (forehead, cheek, and nose) [ 23 ]. Light green lines and nodes show the grid and facial landmarks used for the construction of facial regions, along with a color chart used for color correction placed below the chin.…”

Section: Figurementioning

confidence: 99%

Prediction of Hypertension Based on Facial Complexion

et al. 2021

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This study aims to investigate the association between hypertension and facial complexion and determine whether facial complexion is a predictor for hypertension. Using the Commission internationale de l’éclairage L*a*b* (CIELAB) color space, the facial complexion variables of 1099 subjects were extracted in three regions (forehead, cheek, and nose) and the total face. Logistic regression was performed to analyze the association between hypertension and individual color variables. Four variable selection methods were also used to assess the association between hypertension and combined complexion variables and to compare the predictive powers of the models. The a* (green-red) complexion variables were identified as strong predictors in all facial regions in the crude analysis for both genders. However, this association in men disappeared, and L* (lightness) variables in women became the strongest predictors after adjusting for age and body mass index. Among the four prediction models based on combined complexion variables, the Bayesian approach obtained the best predictive in men. In women, models using three different methods but not the stepwise Akaike information criterion (AIC) obtained similar AUC values between 0.82 and 0.83. The use of combined facial complexion variables slightly improved the predictive power of hypertension in all four of the models compared with the use of individual variables.

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“…In addition, SCM specialists had strongly accounted for facial features as one of the most reliable elements in distinguishing Sasang types [ 23 ]. Many studies had been conducted and facial features were shown to be highly employed in the determination of Sasang types [ 6 , 16 , 24 – 26 ]. Another study had also mentioned that face shape was found to be more utilized among all the facial elements.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Facial Features according to Sasang Types between Native Japanese and Native Korean Populations

Ang

Kim

Lee

2018

Evidence-Based Complementary and Alternative Medicine

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Background Facial diagnosis is a common practice and essential diagnostic method used in the Sasang Constitution Medicine (SCM). SCM is a kind of personalized medicine in Traditional Korean Medicine which categorizes people into four types, namely, Tae-Yang (TY) type, Tae-Eum type (TE), So-Yang (SY) type, and So-Eum (SE) type. This study was conducted to compare and analyze the differences in the facial feature across Sasang types among native Japanese and native Koreans. Methods A total of 843 subjects were recruited for this study, 127 native Japanese and 716 native Koreans, respectively. Facial feature points and the measurements of facial features were assigned and calculated automatically using a facial analysis program. Data of each Sasang type for both genders were also extracted and analyzed. Analysis of covariance was then used to examine the differences in facial feature variables among native Japanese and native Koreans and Sasang types. Results Significant differences were seen in the facial feature variables related to lower face area and eye shape. In males, TE types had wider mid-face and lower face as compared to other constitutions. Male TE types were also seen to have narrower eyes whereas male SY types had rounder eyes. In females, TE types had wider lower face width and area compared to SY types and SE types. Female SY types also had rounder eyes. Conclusions This study presented distinctive feature in the lower face area and eye shape among the Sasang types in both native Japanese and native Koreans. This proposed that facial feature variables can also be used as an objective tool in distinguishing the Sasang types in native Japanese. Further studies are needed in the future to generalize these results.

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Differences in Complexion between Cold‐ and Heat‐Prescription Groups in Sasang Medicine

Cited by 4 publications

References 13 publications

Prediction of Efficacy of Taeumjowi-Tang for Treatment of Metabolic Risk Factors Based on Machine Learning

Prediction of Efficacy of Taeumjowi-Tang for Treatment of Metabolic Risk Factors Based on Machine Learning

Prediction of Hypertension Based on Facial Complexion

Analysis of Facial Features according to Sasang Types between Native Japanese and Native Korean Populations

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