Neonatal Jaundice Detection System

Abstract

Neonatal jaundice is a common condition that occurs in newborn infants in the first week of life. Today, techniques used for detection are required blood samples and other clinical testing with special equipment. The aim of this study is creating a non-invasive system to control and to detect the jaundice periodically and helping doctors for early diagnosis. In this work, first, a patient group which is consisted from jaundiced babies and a control group which is consisted from healthy babies are prepared, then between 24 and 48 h after birth, 40 jaundiced and 40 healthy newborns are chosen. Second, advanced image processing techniques are used on the images which are taken with a standard smartphone and the color calibration card. Segmentation, pixel similarity and white balancing methods are used as image processing techniques and RGB values and pixels' important information are obtained exactly. Third, during feature extraction stage, with using colormap transformations and feature calculation, comparisons are done in RGB plane between color change values and the 8-color calibration card which is specially designed. Finally, in the bilirubin level estimation stage, kNN and SVR machine learning regressions are used on the dataset which are obtained from feature extraction. At the end of the process, when the control group is based on for comparisons, jaundice is succesfully detected for 40 jaundiced infants and the success rate is 85 %. Obtained bilirubin estimation results are consisted with bilirubin results which are obtained from the standard blood test and the compliance rate is 85 %.

The smart phone study: assessing the reliability and accuracy of neonatal jaundice measurement using smart phone application

“…19 The bilirubin estimation by machine learning regressions was consistent with serum bilirubin tests with a compliance rate of 85%.…”

Acylation derivatization based LC-MS analysis of 25-hydroxyvitamin D from finger-prick blood

“…We observed that 78% (115/147) of newborns developed clinical neonatal jaundice (serum TBIL level >85 mol/l) (15). Overall, 73% (125/147) of newborns had vitamin D deficiency, which was 47% in nonjaundiced neonates and 81% in jaundiced neonates (P < 0.001).…”

“…Sample collection was performed at the Department of Neonatology and the Department of Clinical Laboratory of Renmin Hospital of Wuhan University (Wuhan, China). Neonatal jaundice or neonatal hyperbilirubinemia was defined as a TBIL level in infant blood that exceeded 5 mg/dl (85 mol/l) (15), and vitamin D deficiency was defined as a concentration of 25(OH)D less than 20 ng/ml (16). Healthy and jaundiced neonates with postnatal ages of 2-28 days and gestational ages of 37-40 weeks were recruited before any intervention (phototherapy or vitamin D supplementation).…”

Smartphone screening for neonatal jaundice via ambient-subtracted sclera chromaticity

“…Research interest in smartphone-based jaundice screening is growing, and recent work is promising [15][16][17][18][19][20][21][22]. The most extensively validated system is the BiliCam app that estimates total serum bilirubin (TSB) based on the skin colour of the sternum [15].…”