Artificial intelligence-based non-invasive bilirubin prediction for neonatal jaundice using 1D convolutional neural network
Fatemeh Makhloughi
Abstract
score of 0.91. Moreover, the model achieved an impressive accuracy of 96.87% in classifying jaundice status into three categories. This study provides a promising non-invasive alternative for neonatal jaundice detection, potentially improving early diagnosis and management in clinical settings.
Topics & Concepts
Convolutional neural networkJaundiceComputer scienceBilirubinArtificial intelligenceMedicineInternal medicineNeonatal Health and BiochemistryHyperglycemia and glycemic control in critically ill and hospitalized patientsNeonatal and fetal brain pathology