Progeria, Sanfilippo Syndrome, Epidermolysis Bullosa, & Cdkl5 Deficiency: AI-Driven 3D Facial Morphometry, Advanced Gene Therapies, Biomarkers, Genetic Pathologies, Etiology, Epidemiology, Diagnostic Odyssey, Machine Learning

Abstract

Rare genetic disorders, such as Hutchinson–Gilford Progeria Syndrome (HGPS), Sanfilippo Syndrome (MPS III), Epidermolysis Bullosa (EB) and CDKL5 Deficiency Disorder (CDKL5), are often genetically complex, phenotypically variable and late diagnosed, leading to difficulties in diagnosis and treatment. The use of recent advances in artificial intelligence (AI), machine learning (ML), multi-omics technologies, and the discovery of biomarkers has enhanced early detection of disease, genotype–phenotype correlation, and precision diagnostics. This review covers the latest advancements in AI-assisted diagnosis, genetic pathology, biomarker identification, and the development of novel therapeutic strategies like gene editing, gene replacement therapy, RNA-based therapeutics, and AI-driven drug discovery. The review also touches upon the relevance of explainable AI, clinical decision support systems and integrated multi-omics for personalized medicine. Although progress has been made, there are still many obstacles, including small patient cohorts, disease diversity, efficacy and safety of therapy, ethical issues and regulatory standardization. In conclusion, the combination of AI and cutting-edge molecular medicine holds significant promise for improving the diagnosis, management, and individualized treatment of rare genetic diseases.