# Applications of Artificial Intelligence in Neonatology
## 2 Applications of Artificial Intelligence
### AI and the Respiratory System  
Respiratory distress syndrome (RDS) is the most common respiratory consequence of prematurity. It is caused by primary pulmonary surfactant deficiency and affects more than 80% of infants born before 29 weeks of gestational age [52; 53]. Bronchopulmonary dysplasia (BPD) is a chronic respiratory condition experienced by neonates born at the early stages of pulmonary development [54] that leads to poor long-term respiratory, cardiovascular, and neurodevelopmental outcomes. Its prevalence is around 45% in infants born <28 weeks of gestational age and it is associated with longer hospitalization and a higher risk of comorbidities, such as poor somatic growth and neurodevelopmental impairment [55; 56]. BPD also carries substantial social and economic costs [57; 58]. The risk of developing BPD is higher in subgroups of infants who experience worse RDS and a worse early respiratory course [59]. The diagnosis of RDS is clinical and is not based on a single specific diagnostic test. As previously mentioned, RDS is caused by lung immaturity and the derived deficit of surfactant in the alveoli, leading to high surface tension and the collapse of respiratory units. The cornerstone of the management of patients with RDS is administrating exogenous surfactant, aiming to enhance pulmonary compliance and minimize exposure to mechanical ventilation and the derived ventilator-induced lung injury [60]. In any case, prophylactic therapy with surfactant is not a cost-effective strategy [53], and unnecessary or inappropriate administration can lead to potentially dangerous side effects (i.e., airway lesions, pneumothorax, pulmonary hemorrhage). Some authors have investigated the role of biomarkers of RDS, such as the lecithin/sphingomyelin ratio (L/S ratio) from gastric aspirates or amniotic fluid samples. The L/S ratio can give information about endogenous pulmonary surfactant production, which is decreased in preterm infants with RDS. Unfortunately, the calculation of the L/S ratio is still confined to research and not available at the bedside due to technical complexities. To overcome these limitations and provide a bedside point-of-care test method useful for clinicians, [PERSON] et al. tested a machine-learning algorithm also useful for the study of other biological samples that are interpretable in the mid-infrared spectrum [61]. Their findings are particularly insightful as, after clinical validation, the employment of ML-guided devices able to quantify biomarkers of RDS in real time might potentially guide interventions in preterm infants with respiratory symptoms. In any case, considerable concerns about signal-to-noise and signal-to-background limitations remain, and the applicability of this new technology at the bedside does not appear forthcoming. [PERSON] et al. focused on lung ultrasound, a technique able to quantify pulmonary aeration, and its ability to correlate with respiratory status in sick neonates, applying AI-assisted analysis [62]. They built a dataset of scans (600 ultrasound frames) to perform a texture and ML analysis with the aim of correlating the ultrasound and the mean grayscale intensity with the oxygenation status. To do so, they enrolled a cohort of 75 neonates with various degrees of respiratory distress of diverse origin. Despite a significant linear correlation between the grayscale ML analysis and blood gases indexes being found, the relatively small sample size, the inhomogeneous etiology of the respiratory distress, and the variable postnatal age warrant further research on this topic with larger datasets derived from more uniform cohorts. The representative lung ultrasound findings and the calculated intensity histograms are shown in Figure 2.  
A particular topic of research interest in pediatric respiratory care is risk stratification for BPD, aiming to identify infants that could benefit from preventive interventions, such as corticosteroids, or treatment for a patent ductus arterious using a standardized risk estimation tool.