Evaluating associations and metabolic parameters in adult acne and exploration of deep learning techniques for acne grading: A cross-sectional study

Prevalence and Impact: Acne affects approximately 9.4% of the global population, with

adolescents constituting about 85% of cases. It carries significant physical, psychological, and social burdens, straining the healthcare resources.

Epidemiological evidence shows a strong correlation between Insulin resistance and moderate to severe acne. Acne is more prevalent in metabolic disorders such as PCOS and metabolic syndrome, suggesting a role for metabolic dysfunction in its pathogenesis.

Therapeutic Implications: Early identification and management of metabolic abnormalities may enhance overall patient outcomes. Targeting insulin sensitivity (e.g., with metformin or dietary changes) could serve as an effective adjunct to traditional acne treatments.

Role of Artificial Intelligence (AI): AI can ensures objective, standardized, and reproducible acne grading, reducing the variability of traditional assessments. AI-driven models offer fast,

accurate, and personalized treatment recommendations and improve accessibility to

dermatological care, particularly in remote areas.