Visual assessment versus computer-assisted gray scale analysis in the ultrasound evaluation of neonatal respiratory status

BACKGROUND AND AIM: Lung ultrasound has been used to describe common respiratory diseases both by visual and computer-assisted gray scale analysis. In the present paper, we compare both methods in assessing neonatal respiratory status keeping two oxygenation indexes as standards. PATIENTS AND METHODS: Neonates admitted to the NICU for respiratory distress were enrolled. Two neonatologists not attending the patients performed a lung scan, built a single frame database and rated the images with a standardized score. The same dataset was processed using the gray scale analysis implemented with textural features and machine learning analysis. Both the oxygenation ratio (PaO2/FiO2) and the alveolar arterial oxygen gradient (A-a) were kept as reference standards. RESULTS: Seventy-five neonates with different respiratory status were enrolled in the study and a dataset of 600 ultrasound frames was built. Visual assessment of respiratory status correlated significantly with PaO2/FiO2 (r = -0.55; p<0.0001) and the A-a (r = 0.59; p<0.0001) with a strong interobserver agreement (K = 0.91). A significant correlation was also found between both oxygenation indexes and the gray scale analysis of lung ultrasound scans using regions of interest corresponding to 50K (r = -0.42; p<0.002 for PaO2/FiO2; r = 0.46 p<0.001 for A-a) and 100K (r = -0.35 p<0.01 for PaO2/FiO2; r = 0.58 p<0.0001 for A-a) pixels regions of interest. CONCLUSIONS: A semi quantitative estimate of the degree of neonatal respiratory distress was demonstrated both by a validated scoring system and by computer assisted analysis of the ultrasound scan. This data may help to implement point of care ultrasound diagnostics in the NICU.