Invention of automated numerical algorithm adopting binarization for the evaluation of scalp hair coverage: An image analysis providing a substitute for phototrichogram and global photography assessment for hair diseases.

BACKGROUND: The efficacy of therapeutic modalities for hair disease can be evaluated globally by photo assessment and more precisely by phototrichogram (PTG). However, the latter procedure is laborious, time consuming, subject to inter-observer variation, and requires hair clipping. OBJECTIVE: To establish an automated and patient/investigator friendly methodology enabling quantitative hair amount evaluation for daily clinical practice. METHODS: A novel automated numerical algorithm (aNA) adopting digital image binarization (i.e., black and white color conversion) was invented to evaluate hair coverage and measure PTG parameters in scalp images. Step-by-step improvement of aNA was attempted through comparative analyses of the data obtained respectively by the novel approach and conventional PTG/global photography assessment (GPA). RESULTS: For measuring scalp hair coverage, the initial version of aNA generally agreed with the cumulative hair diameter as assessed using PTG, showing a coefficient of 0.60. However, these outcomes were influenced by the angle of hair near the parting line. By integrating an angle compensation formula, the standard deviation of aNA data decreased from 5.7% to 1.2%. Consequently, the coefficient of determination for hair coverage calculated using the modified aNA and cumulative hair diameter assessed by PTG increased to 0.90. Furthermore, the change in hair coverage as determined by the modified aNA protocol correlated well with changes in the GPA score of images obtained using clinical trials. CONCLUSION: The novel aNA method provides a valuable tool for enabling simple and accurate evaluation of hair growth and volume for clinical trials and for treatment of hair disease.

Socioeconomic factors, stress, hair cortisol, and white matter microstructure in children.

Socioeconomic disadvantage has been linked to increased stress exposure in children and adults. Exposure to stress in childhood has been associated with deleterious effects on cognitive development and well-being throughout the lifespan. Further, exposure to stress has been associated with differences in brain development in children, both in cortical and subcortical gray matter. However, less is known about the associations among socioeconomic disadvantage, stress, and children's white matter development. In this study, we investigated whether socioeconomic disparities would be associated with differences in white matter microstructure in the cingulum bundle, as has been previously reported. We additionally investigated whether any such differences could be explained by differences in stress exposure and/or physiological stress levels. White matter tracts were measured via diffusion tensor imaging in 58 children aged 5-9 years. Results indicated that greater exposure to stressful life events was associated with higher child hair cortisol concentrations. Further, physiological stress, as indexed by hair cortisol concentrations, were associated with higher fractional anisotropy in the cingulum bundle. These results have implications for better understanding how perceived and physiological stress may alter neural development during childhood.