Unraveling genetic predisposition and oxidative stress in vitiligo development and the role of artificial intelligence (AI) in diagnosis and management: Artificial intelligence (AI) in the diagnosis and management of vitiligo - Kocic, Hristina; Lotti, Torello; Jevtovic-Stoimenov, Tatjana; Wollina, Uwe; Valle, Yan; Lukic, Stevo; Klisic, Aleksandra (Journal of Medical Biochemistry, 2025)
Vitiligo is an autoimmune disorder with a complex genetic and epigenetic etiology, characterized by the progressive depigmentation of the skin. Recent advancements in artificial intelligence (AI) have greatly impacted the understanding, diagnosis, and treatment of vitiligo. The genetic basis of vitiligo is linked to multiple single nucleotide polymorphisms (SNPs) in genes associated with immune function, apoptosis, and melanogenesis, necessitating the integration of AI for more efficient diagnostic tools and personalized therapies. Genome-wide association studies (GWAS) have identified approximately 50 vitiligo-susceptibility genes, including PTPN1, PTPN22, NLRP1, FASLG, and TYR, among others. These genes influence the immune response and melanocyte function, with the transcription factor Nuclear Factor kappa B (NF-κB), playing a central role in inflammatory responses and redox signaling induced by oxidative stress, in conjunction with antioxidant enzymes such as GPx, GST, SOD, and CAT. AI technologies offer a promising avenue for diagnosing vitiligo by combining genetic, clinical, and imaging data, allowing for more accurate classification and personalized treatment strategies. By analyzing vast datasets, AI algorithms can identify patterns within complex genetic markers and clinical features, facilitating earlier and more precise detection of vitiligo. Furthermore, AI-driven approaches can optimize therapeutic monitoring, enabling real-time assessment of treatment efficacy and disease progression. The integration of AI in vitiligo genetic diagnostics holds the potential to revolutionize the way the disorder can be monitored, improving patient outcomes through personalized, data-driven interventions.