BackgroundDespite its shortcomings, body mass index (BMI) has traditionally been used to define obesity. Another recently introduced obesity measure, A Body Shape Index (ABSI), has been introduced to focus on abdominal obesity, but its applicability remains limited. We analyzed the statistical properties of the ABSI and propose a modified ABSI, the z-score of the log-transformed ABSI (LBSIZ), to improve its applicability. We also examined the sensitivity of the newly introduced index in diagnosing obesity based on the percentage of body fat and its ability to predict hypertension and impaired health-related quality of life (HRQOL).Methods and ResultsWe transformed the ABSI to the LBSIZ in order to create a standard normalized obesity measure. All available data from the Korea National Health and Nutrition Examination Survey (KNHANES) (1998–2012) have shown BMI to be highly correlated with weight (r = 0.85 for women, r = 0.87 for men) and waist circumference (WC) (r = 0.86 for women, r = 0.85 for men), but the LBSIZ was found to be weakly correlated with weight (r = 0.001 for women, r = 0.0001 for men) and moderately correlated with WC (r = 0.51 for women, r = 0.52 for men). BMI showed an inverted U-shaped pattern when plotted against age, but a linear pattern was observed for the LBSIZ, indicating they are different kinds of obesity measures. Logistic regression showed that the odds ratio of obesity for the LBSIZ was 1.86 (95% confidence interval [CI] = 1.73–2.00) for males and 1.32 (95% CI = 1.24–1.40) for females after adjusting for weight, height, age, and year of participation in the KNHANES. While both BMI and the LBSIZ were significantly related to hypertension, the LBSIZ alone was significantly associated with impaired HRQOL.ConclusionsThe LBSIZ is a standard normalized obesity measure independent of weight, height, and BMI. LBSIZ is a new measure of abdominal obesity with the ability to predict hypertension and impaired HRQOL, irrespective of BMI.

We apply our approach to the modified data on wood specific gravity analyzed in Rousseeuw and Leroy (1987). We fitted the linear regression using OLS, Least Median of Squares (LMS), Least Trimmed Squares (LTS), S estimator (Rousseeuw and Yohai, 1984), MM (Yohai, 1987), M (Huber's estimator, 1981). Figure 1 shows a scatter plot matrix (6 by 2) between the fitted value, Ŵ and W and between the fitted value and residuals, where Ŵ is obtained from OLS scatter plot between fitted value, Ŷ and Y and between the fitted value and residuals, where Ŷ is obtained from OLS in Figure 1. The second column consist of six scatter plots between the fitted value and residuals. We apply our approach to this data.

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Background This study evaluates the conformity of using a computer vision-based posture analysis system as a screening assessment for postural deformity detection in the spine that is easily applicable to clinical practice. Methods One hundred forty participants were enrolled for screening of the postural deformation. Factors that determine the presence or absence of spinal deformation, such as shoulder height difference (SHD), pelvic height difference (PHD), and leg length mismatch (LLD), were used as parameters for the clinical decision support system (CDSS) using a commercial computer vision-based posture analysis system. For conformity analysis, the probability of postural deformation provided by CDSS, the Cobb angle, the PHD, and the SHD was compared and analyzed between the system and radiographic parameters. A principal component analysis (PCA) of the CDSS and correlation analysis were conducted. Results The Cobb angles of the 140 participants ranged from 0° to 61°, with an average of 6.16° ± 8.50°. The postural deformation of CDSS showed 94% conformity correlated with radiographic assessment. The conformity assessment results were more accurate in the participants of postural deformation with normal (0–9°) and mild (10–25°) ranges of scoliosis. The referenced SHD and the SHD of the CDSS showed statistical significance (p < 0.001) on a paired t-test. SHD and PHD for PCA were the predominant factors (PC1 SHD for 79.97%, PC2 PHD for 19.86%). Conclusion The CDSS showed 94% conformity for the screening of postural spinal deformity. The main factors determining diagnostic suitability were two main variables: SHD and PHD. In conclusion, a computer vision-based posture analysis system can be utilized as a safe, efficient, and convenient CDSS for early diagnosis of spinal posture deformation, including scoliosis.

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