Non-invasive Methods for Diagnosing Jaundice in Newborns: A Review
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Keywords:
Neonatal jaundice, Machine Learning, bilirubin, Total Serum Bilirubin, Transcutaneous BilirubinAbstract
Neonatal jaundice, characterized by the manifestation of yellowing in the skin and eyes as a
result of augmented levels of bilirubin, presents a substantial peril to the well-being and duration of life in
neonates, conceivably influencing their comprehensive health and longevity. Its early onset, typically
within the initial days, demands prompt attention, especially when it arises physiologically on the second
or third day. Elevated bilirubin, stemming from red blood cell breakdown, presents a challenge for
newborns as they struggle to naturally eliminate this pigment. Left untreated, jaundice can lead to severe
outcomes like kernicterus, causing irreversible brain damage due to heightened bilirubin levels. This study
aims to comprehensively assess various non-invasive frameworks for identifying neonatal jaundice. The
review scrutinizes innovative, non-invasive approaches, comparing methods based on clinical data to
predict serum bilirubin levels. Challenges in using machine learning for jaundice detection are also
highlighted. Non-invasive methods have shown remarkable success across diagnostic, supportive, research,
and clinical domains in managing neonatal jaundice. This ongoing exploration sets the stage for improved
neonatal care, underscoring the importance of timely diagnosis and intervention to prevent enduring
neurological damage resulting from acute bilirubin encephalopathy. The conclusions drawn from this
research hold great importance, as they emphasize the possibility of non-invasive methods to revolutionize
neonatal healthcare, guaranteeing a safer and more efficient approach to monitoring and treating jaundice
in infants.
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