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BiliBin: An Intelligent Mobile Phone-based Platform to Monitor Newborn Jaundice
Eisa Zarepour , Mohammad Reza Mohammadi , Morteza Zakeri-Nasrabadi , Sara Aein , Razieh Sangsari , Leila Taheri , Mojtaba Akbari , Ali Zabihallahpour
Abstract:   (71 Views)
Using mobile phones for medical applications are proliferating due to high-quality embedded sensors. Jaundice, a yellow discoloration of the skin caused by excess bilirubin, is a prevalent physiological problem in newborns. While moderate amounts of bilirubin are safe in healthy newborns, extreme levels are fatal and cause devastating and irreversible brain damage. Accurate tests to measure jaundice require a blood draw or dedicated clinical devices facing difficulty where clinical technology is unavailable. This paper presents a smartphone-based screening tool to detect neonatal hyperbilirubinemia caused by the high bilirubin production rate. A machine learning regression model is trained on a pretty large dataset of images, including 446 samples, taken from newborns' sternum skin in four medical centers in Iran. The learned model is then used to estimate the level of bilirubin. Experimental results show a mean absolute error of 1.807 mg/dl and a correlation of 0.701 between predicted bilirubin by the proposed method and the TSB values as ground truth.
Keywords: Health Sensing, Image Processing, Internet of Things, Machine Learning, Neonatal Jaundice.
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Type of Study: Research Paper | Subject: Biomedical Systems
Received: 2024/07/21 | Revised: 2024/08/07 | Accepted: 2024/07/31
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Creative Commons License
© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.