Artificial Intelligence in Phenylketonuria: Bridging Conventional Gaps in Diagnosis and Treatment- A Systematic Review.

INTRODUCTION: Phenylketonuria (PKU) is an autosomal recessive condition of phenylalanine metabolism, characterized by abnormally high phenylalanine concentrations that lead to brain damage. These illnesses are caused by mutations in genes that encode proteins or enzymes involved in metabolic processes. It causes harmful implications by accumulating in the blood and the brain. MATERIALS AND METHODS: The present review aims to compile detailed diagnostic and treatment methods for phenylketonuria. The sources of literature are PubMed, Google Scholar, SciFinder, Scopus, Elsevier, and Web of Science. RESULTS: The prevalence of phenylketonuria in the US is around 1 in 15,000 people. The incidence of phenylketonuria varies greatly across the globe. With 1/4000 live births, the project aims to develop and evaluate a time-aware, healthful food recommender system for PKU patients from birth to maturity using a variety of artificial intelligence and machine learning approaches. To determine the system's accuracy, usefulness, and effect on dietary adherence and health outcomes, more PKU patients will participate in a clinical trial. DISCUSSION: Phenylketonuria is increasingly well understood, diagnosed, and managed through the use of artificial intelligence. To forecast disease course, improve screening accuracy, and identify novel treatment targets, machine learning models are being developed. CONCLUSION: This study presents a comprehensive overview of pathogenesis, risk factors, pathophysiology, symptoms, diagnoses, and treatment. PKU has no known cure, especially in late-stage cases; however, if the illness is diagnosed earlier, treatment is more feasible. Lifelong dietary treatment aimed at preserving low phenylalanine levels and sufficient.