Automatic Machine Learning Classification of Late Preterm and Term Neonatal Lung Disorder in X-ray Images

Neonatal respiratory distress syndrome (NRDS) is a condition often seen in premature babies, where lungs are not fully developed. It is the most common respiratory disorder in premature newborns, and its prevalence is directly proportional to the premature birth rate. At present, physical examination, a blood test to measure blood oxygen saturation, and X-ray images are used for diagnosis. Early diagnosis of the condition is of high importance due to available management methods. Therefore, the development of methods to carry out NRDS diagnosis accurately and efficiently can significantly contribute to improving chances of treatment.

In this project, we set out to devise accurate classification methods that can be used to automatically, or semi-automatically, identify cases of NRDS and thus enabling early intervention. This project also aims to develop methods that would assist clinicians with distinguishing between NRDS and other conditions and would enable more suitable and timely treatments, therefore reducing the need for unnecessary medication or procedures.