ArticleScientific reports2025
Artificial intelligence-based non-invasive bilirubin prediction for neonatal jaundice using 1D convolutional neural network.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- TabPFN-based explainable machine learning for predicting neonatal hyperbilirubinemia: A decade-long retrospective cohort study.Medicine · 2026Article
- Neonatal jaundice detection using a vision transformer-based deep learning model.Scientific reports · 2026Article
- Agreement and diagnostic performance of a smartphone-based bilirubin assessment tool for screening neonatal hyperbilirubinemia.Frontiers in pediatrics · 2026Article
- Evolution and contemporary landscape of neonatal hyperbilirubinemia management guidelines: a narrative review.Frontiers in pediatrics · 2026Review
- Multimodal feature analysis for automated neonatal jaundice assessment using machine learning.JAMIA open · 2025Article
- Enhanced YOLOv11 Framework for Accurate Multi-Fault Detection in UAV Photovoltaic Inspection.Sensors (Basel, Switzerland) · 2025Article
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Authors and funding
1 author.
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Abstract
Neonatal jaundice, characterized by elevated bilirubin levels causing yellow discoloration of the skin and eyes in newborns, is a critical condition requiring accurate and timely diagnosis. This study proposes a novel approach using 1D Convolutional Neural Networks (1DCNN) for estimating bilirubin levels from RGB, HSV, LAB, and YCbCr color channels extracted from infant images. Initially, each color channel is treated as a time series input to a 1DCNN model, facilitating bilirubin level prediction through regression analysis. Subsequently, RGB feature maps are combined with those derived from HSV, LAB, and YCbCr channels to enhance prediction performance. The effectiveness of these methods is evaluated based on Root Mean Squared Error (RMSE), R-squared (R
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