Dataset Information

Analysis of Sasang constitutional types using facial features with compensation for photographic distance.

ABSTRACT:

Background

Facial features are regarded as representative and reliable characteristics for diagnosing a person's Sasang Constitution (SC). However, the description of these features tends to depend on the interpretation and the opinion of the doctor that follows the SC approach. In this paper, we performed a facial feature analysis of SC types in an objective and quantitative manner. Here, site-to-site variability can be an obstacle to properly analyzing facial features when images are taken from various sites, which may have different experimental environments. A compensation technique to reduce the site-to-site variability was proposed before performing the feature analysis.

Methods

The frontal and profile images of 1464 patients recruited from various oriental medical clinics (19 sites) were used. Candidate feature variables were created, which were inspired by the facial characteristics of the SC types described in the Sasang constitutional medicine literature. To resolve the problems involved in processing data collected from various sites with heterogeneous experimental environments, a compensation technique was proposed. Statistical analysis techniques were employed to observe the differences among the SC types and to demonstrate how effectively the site-to-site variability was reduced.

Results

The facial features that were significant for diagnosing the SC types were identified by a statistical analysis, and it was verified that the compensation technique reduced the site-to-site variability produced by the differences in photographic distance.

Conclusion

It is noted that the significant facial features represent common characteristics of each SC type in the sense that we collected extensive opinions from many Sasang constitutional medicine doctors with various points of view. Additionally, a compensation method for the photographic distance is needed to find the significant facial features. We expect these findings and the related compensation technique to contribute to establishing a scientific basis for the precise diagnosis of SC types in clinical practice.

SUBMITTER: Do JH

PROVIDER: S-EPMC5481682 | biostudies-literature | 2012 Dec

REPOSITORIES: biostudies-literature

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Publications

Analysis of Sasang constitutional types using facial features with compensation for photographic distance.

Do Jun-Hyeong JH Ku Boncho B Jang Jun-Su JS Kim Honggie H Kim Jong Yeol JY

Integrative medicine research 20121016 1

<h4>Background</h4>Facial features are regarded as representative and reliable characteristics for diagnosing a person's Sasang Constitution (SC). However, the description of these features tends to depend on the interpretation and the opinion of the doctor that follows the SC approach. In this paper, we performed a facial feature analysis of SC types in an objective and quantitative manner. Here, site-to-site variability can be an obstacle to properly analyzing facial features when images are t ...[more]

PMID: 28664044

Similar Datasets

Project description:BackgroundSasang constitutional medicine (SCM) is a type of tailored medicine that divides human beings into four Sasang constitutional (SC) types. Diagnosis of SC types is crucial to proper treatment in SCM. Voice characteristics have been used as an essential clue for diagnosing SC types. In the past, many studies tried to extract quantitative vocal features to make diagnosis models; however, these studies were flawed by limited data collected from one or a few sites, long recording time, and low accuracy. We propose a practical diagnosis model having only a few variables, which decreases model complexity. This in turn, makes our model appropriate for clinical applications.MethodsA total of 2,341 participants' voice recordings were used in making a SC classification model and to test the generalization ability of the model. Although the voice data consisted of five vowels and two repeated sentences per participant, we used only the sentence part for our study. A total of 21 features were extracted, and an advanced feature selection method-the least absolute shrinkage and selection operator (LASSO)-was applied to reduce the number of variables for classifier learning. A SC classification model was developed using multinomial logistic regression via LASSO.ResultsWe compared the proposed classification model to the previous study, which used both sentences and five vowels from the same patient's group. The classification accuracies for the test set were 47.9% and 40.4% for male and female, respectively. Our result showed that the proposed method was superior to the previous study in that it required shorter voice recordings, is more applicable to practical use, and had better generalization performance.ConclusionsWe proposed a practical SC classification method and showed that our model having fewer variables outperformed the model having many variables in the generalization test. We attempted to reduce the number of variables in two ways: 1) the initial number of candidate features was decreased by considering shorter voice recording, and 2) LASSO was introduced for reducing model complexity. The proposed method is suitable for an actual clinical environment. Moreover, we expect it to yield more stable results because of the model's simplicity.

Dataset Information

Analysis of Sasang constitutional types using facial features with compensation for photographic distance.

Background

Methods

Results

Conclusion

Publications

Analysis of Sasang constitutional types using facial features with compensation for photographic distance.

Similar Datasets

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