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This research explored the impact of chemotherapy on scalp conditions in cancer patients, finding significant textural differences through imaging analysis between those who experienced hair loss and those who retained hair, suggesting potential changes in scalp morphology during hair regrowth.

This study developed an automated image analysis framework for diagnosing hair disorders using trichoscopic images, reporting a Random Forest classifier as having an 86.67% accuracy in distinguishing between different scalp pathologies based on quantitative image features.

This study observed that indigenous South Africans with natural afro-textured hair have lower hair densities, varying shaft diameters, and distinct follicular unit patterns compared to other racial groups, with significant gender differences in some parameters, highlighting the need for tailored reference values in clinical settings.

This research explored the impact of anticancer drugs on the scalp and found significant differences in scalp texture and features associated with alopecia, suggesting potential for assessing scalp morphology and changes during the hair regrowth process through image analysis.

In this study conducted at a tertiary dermatological clinic, researchers identified and described the clinical, dermoscopic, and histopathological features of preclinical folliculitis keloidalis, including novel dermoscopic findings such as perifollicular scale, which was associated with fibrosis.