LungAIR: An automated technique to predict hospitalization due to LRTI using fused information

This paper presents a quantitative imaging method and software technology to predict the risk and assess the severity of respiratory diseases in premature babies by fusing information from multiple sources: Non-invasive low-radiation chest X-ray (CXR) imaging and clinical parameters. Prematurity is the largest single cause of death in children under five in the world. Lower respiratory tract infections (LRTI) are the top cause of hospitalization and mortality in prematurity. However, there is no objective clinical marker to predict and prevent severe LRTI in the 15 million babies born prematurely every year worldwide. Traditionally, imaging biomarkers of lung disease from computed tomography have been successfully used in adults, but they entail heightened risks for children due to cumulative radiation and the need for sedation. The proposed technology is the first approach that uses low-radiation CXR imaging to predict hospitalization due to LRTI in prematurity. The method uses deep learning to quantify heterogeneous patterns (air trapping and irregular opacities) in the chest, which are combined with clinical parameters to predict the risk of LRTI. Our preliminary results obtained using a data obtained from ten premature subjects with LRTI showed high correlation between our imaging biomarkers and the rehospitalization of these subjects R ² =0.98).