Establishment and verification of a method for analyzing nasal blackheads images.

Abstract

As people pay more attention to their skin health and the demand of developing skin care products for facial blackheads grows, the value of objective and efficient image recognition methods for blackheads is becoming more evident. Inspired by this current situation, this study attempted to analyze the number of blackheads of different severity automatically on the nose using an object recognition method on photographs of the nasal blackheads of subjects. This study collected 350 subjects' facial photos in the laboratory environment, who aged 18-60, with blackhead symptoms in the nasal region. And expert assessment was used as a reference for machine learning to verify the performance of the nasal blackhead image recognition model through consistency and correlation analysis. The study concluded that the algorithm accuracy reached above 0.9, the model itself was effective, and the consistency between the model and the expert assessor assessment results was good, with the number of nasal blackheads, the count of blackheads of different severity, and the intra-group correlation coefficient ICC of blackhead severity all above 0.9, indicating that the deep learning-based assessment model had high overall performance and the evaluation results were comparable to those of the expert assessor. The recognition and analyzing model of nasal blackhead images provides a scientifically objective and accurate method for identifying the number and evaluating the severity of nasal blackheads. By using this model, the efficiency of evaluating nasal blackhead images in the cosmetics clinical trial will be improved. The assessment result of nasal blackheads will be objective and stable, and not only rely on the professional knowledge and clinical experience of assessors. The model can try to be applied in cosmetics efficacy testing and continuously optimized.