Unraveling genetic predisposition and oxidative stress in vitiligo development and the role of artificial intelligence (AI) in diagnosis and management - Kocić Hristina; Lotti Torello; Jevtović-Stoimenov Tatjana; Wollina Uwe; Valle Yan; Lukić Stevo; Klisić Aleksandra (Journal of Medical Biochemistry)
Vitiligo is an autoimmune disorder with a complex genetic and epigenetic aetiology, characterised by progressive skin depigmentation. 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 personalised therapies. Genome-wide association studies (GWAS) have identified approximately 50 vitiligo-susceptibility genes, including PTPN1, PTPN22, NLRP1, FASLG, and TYR. These genes influence the immune response and melanocyte function, with the transcription factor Nuclear Factor kappa B (NF-kB), 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 personalised treatment strategies. By analysing 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 optimise therapeutic monitoring, enabling real-time treatment efficacy and disease progression assessment. Integrating AI in vitiligo genetic diagnostics can revolutionise the monitoring of the disorder, improving patient outcomes through personalised, data-driven interventions.