Review of Precision Medicine and Diagnosis of Neonatal Illness

• Published in: Diagnostics (Basel) Vol. 15; no. 4; p. 478
• Main Authors: ELMeneza, Safaa, Agaba, Naglaa, Fawaz, Rasha Abd El Samad, Abd Elgawad, Salwa Samir
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 16.02.2025; MDPI
• Subjects: Artificial intelligence; Brain damage; Brain research; Cerebral palsy; Disease prevention; Genomes; Genomics; Hemodynamics; hyperbilirubinemia; Hypoxia; Ischemia; Kidneys; Medical prognosis; neonatal brain injury; neonatal hemodynamics; neonatal RDS; neonatal sepsis; Newborn babies; Precision medicine; Respiratory distress syndrome; Review; Sepsis; Trauma; Traumatic brain injury; neonatal hemodynamics; BPD; hyperbilirubinemia; pharmacokinetics; NICU; precision medicine; neonatal sepsis; neonatal brain injury; neonatal RDS; AKI
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics15040478

Background/Objectives: Precision medicine is a state-of-the-art medicine tactic that tailors information about people’s genes, environment, and lifestyle to aid the prevention, diagnosis, and treatment of various diseases to provide an overview of the currently available knowledge and applicability of precision medicine in the diagnosis of different cases admitted to the NICU, such as encephalopathies, respiratory distress syndrome of prematurity, hemodynamic instability, acute kidney injury, sepsis, and hyperbilirubinemia. Methods: The authors searched databases, such as PubMed and PubMed Central, for the terms neonatal “precision medicine”, “personalized medicine”, “genomics”, and “metabolomics”, all related to precision medicine in the diagnosis of neonatal illness. The related studies were collected. Results: The review highlights the diagnostic approach that serves to implement precision medicine in the NICU and provide precision diagnosis, monitoring, and treatment. Conclusions: In this review, we projected several diagnostic approaches that provide precision identification of health problems among sick neonates with complex illnesses in the NICU; some are noninvasive and available in ordinary healthcare settings, while others are invasive or not feasible or still in ongoing research as machine learning algorithms. Future studies are needed for the wide implementation of artificial intelligence tools in the diagnosis of neonatal illnesses.