Scientists Unveil Smartphone App That Can Detect Jaundice In Newborns

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Scientists have revealed on March 2 that a smartphone app that keeps a curb on newborn babies for jaundice may be an effective way to save lives worldwide.

Written By Sounak Mitra | Mumbai | Updated On:
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Scientists have revealed on March 2 that a smartphone app that keeps a curb on newborn babies for jaundice may be an effective and economic way of identifying which cases need treatment. The study reported that jaundice causes skin and white of the eyes to turn yellow resulting in 114,000 newborn deaths and 178,000 cases of disability a year. The study says that one-third of the deaths occur in South Asia and sub-Saharan Africa. As per the reports, the study was conducted by researchers at the University College London (UCL) and published in the journal PLOS ONE which plays a crucial role to health care professionals in poorer regions.

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Robust assessment

Senior author Terence Leung, from UCL's medical physics and biomedical engineering department said that midwives and nurses in many regions across the world rely on sight alone to identify jaundice which is unreliable especially for newborns with darker skin. He added that the smartphone-based method assures a more strategic and strong assessment ensuring that the serious cases do not go unnoticed. The researchers believe that the app-based method could help prevent deaths of newborn babies due to jaundice. 

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60 per cent successful 

Jaundice is caused by high levels of a yellow compound called bilirubin that is generated during the normal breakdown of red blood cells. Scientists revealed that most of the jaundice cases are not harmful, but in some rare cases a neurotoxic form of bilirubin can enter the brain, which may lead to death or disabilities like hearing loss, cerebral palsy and cognitive impairment. 
As per reports, in the recent study, pictures of the 37 newborn babies were captured on smartphone who had been referred for blood tests.

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The images were then processed to remove distortions from background light and the yellowness of the eyes were measured to predict bilirubin levels. The prediction were then compared to the blood test results with an algorithm correctly identifying all cases that would require further treatment. It was found that the app was  60 per cent successful at identifying cases that did not require any treatment. 

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