Artificial intelligence-based non-invasive bilirubin prediction for neonatal jaundice using 1D convolutional neural network.

Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE

Original Publication: London : Nature Publishing Group, copyright 2011-

Jaundice, Neonatal*/diagnosis ; Jaundice, Neonatal*/blood ; Bilirubin*/blood ; Neural Networks, Computer* ; Artificial Intelligence*; Humans ; Infant, Newborn ; Convolutional Neural Networks

Competing Interests: Declarations. Competing interests: The authors declare no competing interests.
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 ² ), and Mean Absolute Error (MAE). Additionally, the best-performing model is adapted for classification of jaundice status. The results show that the integration of RGB and HSV color spaces yields the best performance, with an RMSE of 1.13 and an R ² 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.
(© 2025. The Author(s).)

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Keywords: Bilirubin level prediction; Image processing; Neonatal jaundice; One dimensional convolutional neural network

RFM9X3LJ49 (Bilirubin)

Date Created: 20250404 Date Completed: 20250515 Latest Revision: 20250516

20250519

PMC11971457

10.1038/s41598-025-96100-9

40185821