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

• Published in: PloS one Vol. 13; no. 10; p. e0202397
• Main Authors: Raimondi, Francesco, Migliaro, Fiorella, Verdoliva, Luisa, Gragnaniello, Diego, Poggi, Giovanni, Kosova, Roberta, Sansone, Carlo, Vallone, Gianfranco, Capasso, Letizia
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 18.10.2018; Public Library of Science (PLoS)
• Subjects: Alveoli; Biology and Life Sciences; Computer and Information Sciences; Diagnosis; Edema; Electrical engineering; Engineering and Technology; Evaluation; Gray scale; Information technology; Intensive care; Learning algorithms; Lungs; Machine learning; Medicine and Health Sciences; Neonates; Newborn babies; Oxygen; Oxygenation; Patients; Pediatrics; Pneumonia; Psychological aspects; Remote sensing; Research and Analysis Methods; Respiratory diseases; Respiratory tract diseases; Surfactants; Ultrasonic imaging; Ultrasound; Ventilators; Italy
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0202397

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. 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. 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. 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.