Patient Unique Stigmatization Holistic tool in dermatology (PUSH-D): Development and validation of a dermatology-specific stigmatization assessment tool.

BACKGROUND: The skin plays an important role in establishing interpersonal relationships, and thus visible skin disorders, which have a significant impact on physical appearance, influence other people's behaviours and attitudes. OBJECTIVE: To develop and validate a dermatologic-specific questionnaire to evaluate stigmatization in individuals with visible skin conditions. METHODS: Items were generated by a verbatim report based on qualitative interviews with patients with various dermatologic conditions. Subsequently, a study was implemented for psychometric analysis. A dermatology-specific stigmatization questionnaire (PUSH-D) was refined via item reduction according to inter-question correlations, consensus among experts and exploratory factor analysis. Internal consistency was determined by calculating Cronbach's α. Concurrent validity was determined by calculating the correlation between PUSH-D and the Dermatology Life Quality Index (DLQI) and the Rosenberg Self-Esteem Scale (RSES). RESULTS: From a primary list of 22 items, PUSH-D was reduced to a 17-item questionnaire, covering two pertinent dimensions based on the exploratory factor analysis. Construct validity was demonstrated, and PUSH-D showed good internal consistency (Cronbach's α = 0.9). PUSH-D correlated strongly with the DLQI 0.72 (p < 0.001) and moderately with the RSES 0.49 (p < 0.001). CONCLUSION: PUSH-D allows a comprehensive view of the degree of stigmatization in visible skin disorders, as well as the comparability of stigmatization levels across various skin conditions.

The continuous development of a complete and objective automatic grading system of facial signs from selfie pictures: Asian validation study and application to women of three ethnic origins, differently aged.

OBJECTIVE: To evaluate the capacity of the automatic detection system to accurately grade, from smartphones' selfie pictures, the severity of seven new facial signs added to the nine previously integrated. METHODS: A two-step approach was conducted: first, to check on 112 Korean women, how the AI-based automatic grading system may correlate with dermatological assessments, taken as reference; second, to confirm on 1140 women of three ancestries (African, Asian, and Caucasian) the relevance of the newly input facial signs. RESULTS: The sixteen specific Asian facial signs, detected automatically, were found significantly (P < .0001) highly correlated with the clinical evaluations made by two Korean dermatologists (wrinkles: r = .90; sagging: r = .75-.95; vascular: r = .85; pores: r = .60; pigmentation: r = .50-.80). When applied at a larger scale on women of different ethnicities, new signs were found of good accuracy and reproducibility, albeit depending on ethnicity. Due to contrast with the innate skin complexion, the facial signs dealing with skin pigmentation were found of a much higher relevance among Asian women than African or Caucasian women. The automatic gradings were even found of a slightly higher accuracy than the clinical gradings. CONCLUSION: The previously used automatic grading system is now completed by adding new facial signs apt at being detected. The continuous development is now integrating some limitations with regard to the constitutive skin complexion of the self-pictured subjects. Presenting reproducible assessments, highly correlated with medical grading, this system could change tremendously clinical researches, like in epidemiological studies, where it offers an easy, fast, affordable, and confidential approach in the objective quantification of facial signs.