Computer Vision Based Jaundice Monitoring for Smart Low-Cost Phototherapy Light System

• Main Authors: Manabat, Bryan Kristofer A., Delos Reyes, Isaiah Dale L., Matibag, Jela Patricia R., Santiago, Paul Ryan A., Chaves, Steve Maverick, Cao, Reymond P., Cabacungan, Paul M., Oppus, Carlos M, Libatique, Nathaniel Joseph C, Tangonan, Gregory L
• Format: text
• Published: Archīum Ateneo 2023
• Subjects: Computer Vision; Edge Computing; Healthcare; Biomedical; Computer Engineering; Computer Sciences; Electrical and Computer Engineering; Engineering; Physical Sciences and Mathematics
• Online Access: https://archium.ateneo.edu/es-faculty-pubs/122; https://doi.org/10.1109/GHTC56179.2023.10354881
• Institution: Ateneo De Manila University

This paper describes the development and enhancements of the Smart Low-cost Phototherapy Light System (Smart LPLS) at Ateneo Innovation Center (AIC) and partners. The developed Smart LPLS has custom-built LED panels that can output an irradiance of up to 83.89 μW/cm2/n; 2.7 times stronger than the minimum required of 30 μW/cm2/nm for Neonatal Jaundice treatment. The integrated monitoring system uses an ESP32 camera module for capturing and sending images and a Raspberry Pi that enables Near Cloud storage, processing, and rendering capabilities. Using a modified version of Kovac's rules for skin pixel identification and K-means clustering for color quantization proved to be effective in extracting the dominant colors present within a skin region. Extracted colors are checked for yellow pigmentation based on their b∗ axis value on the CIE L*a*b∗ color space, which indicates yellowness (-) or blueness (+). This enables the tracking of increase and decrease in yellow pigmentation over time, with a configurable threshold for sending alerts through email at perceived dangerous levels. The Smart LPLS offers a cost-effective and efficient alternative to commercial phototherapy units, with the additional functionality of an integrated smart monitoring system.