Bio-Remote Sensing in Predicting Infection in Neonates With Thermal Imaging and Machine Learning

Abstract

Premature birth complications have different causes and vary in different parts of the world with sepsis as one of the leading causes of these complications. The body releases anti-inflammatory substances when an infection is detected and this, in turn, could damage healthy organs, especially when they are not fully developed. Preterm babies are susceptible to diseases due to their underdeveloped organs and immune systems. Hence, it is extremely important to treat sepsis as soon as the baby is diagnosed. Neonatal sepsis is a dangerous nonspecific disease in babies, and it is a clinically very difficult and challenging task to diagnose. Late or incorrect treatment of infants' sepsis can lead to death which is one of the most causes of mortality rate in neonates. In the traditional treatment of sepsis, the needed time and accuracy for diagnosis are still very concerning, considering the number of involved risks in late diagnosis or mistreatment of sepsis cases. Thus, the need for having a fast and reliable algorithm with high accuracy to predict sepsis before clinical recognition would help the doctors to treat the neonates in time and to reduce the mortality rate related to sepsis. This paper presents a fast, accurate, and reliable thermographic Bio-Remote Sensing approach to predicting sepsis in neonates and discusses the significance of combining the Thermal Imaging technique with Machine Learning (ML). At the same time, it provides a more practical and desirable solution for physicians by minimising the traditional diagnosis time and maximizing the accuracy of the prediction needed to detect sepsis in neonates.