# Applications of Artificial Intelligence in Neonatology
## 2 Applications of Artificial Intelligence
### AI and the Respiratory System
The US National Institute of Child Health and Human Development has recently endorsed a prognostic tool, the BPD Outcome Estimator [63], which is widely employed to guide steroid administration and family counseling. In any case, the estimator is only applicable to neonates of White, Black, or Hispanic descent. [PERSON] et al. recently designed an ML algorithm for respiratory outcome prediction for extremely premature infants of Asian heritage, aiming to fill this gap in knowledge [64]. As a demonstration of the model, a Web app was developed and is currently consultable online. In anycase, the algorithm was tested on a small dataset derived from 29 patients and needs extensive and prospective validation before being employed for clinical purposes, majorly limiting the results of the study. [PERSON] et al. proposed an AI method capable of predicting the severity of BPD, defined as mild, moderate, or severe, by studying the chest X-ray recorded on the 28 th day of oxygen administration using deep learning and image segmentation [65]. Their model was developed with a relatively large cohort (121 preterm infants with at least 28 days of oxygen need) and has the advantages of being non-invasive, rapid, and independent of neonatologists' experience. The proposed algorithm is based on the segmentation of the lungs in chest X-ray images and showed good predictive performance. Other groups focused on different data that could help predict BPD. For instance, in a recent work by [PERSON] et al., the combination of genetic and clinical factors was studied: exome sequencing was performed for more than 200 preterm infants and integrated with clinical features [66]. This dataset was then employed to create an ML model capable of identifying early infants with a high risk of developing BPD with good accuracy. [PERSON] and colleagues retrospectively analyzed the perinatal factors and the respiratory support needed in the first days of life in a population of preterm babies with the aim of obtaining an ML algorithm able to predict BPD-free survival. They trained the model and tested it, finding good performance in terms of the area under the curve. They also designed simulation scenarios to validate and use the model to solve clinical questions about BPD [67]. Apnea of prematurity (AOP) is another common issue detected in premature infants admitted to NICUs. It is correlated with the immaturity of the respiratory control system in these fragile patients and can be triggered by several conditions, including infections, gastroesophageal reflux, and seizures [68]. AOP is diagnosed in more than 50% of preterm babies and is almost universal in infants with extremely low birth weight [69]. It is associated with brain damage and poor neurodevelopmental outcomes and thus needs prompt recognition and treatment. AOP can be categorized as central (lack of diaphragmatic contraction due to the cessation of respiratory drive), obstructive (due to airway obstruction), or mixed apnea (characterized by both central and obstructive features). As a standard of care, bedside monitors are set to generate an alarm in cases of a temporary drop in the  
Figure 2: Representative lung ultrasound images used for scoring and calculated histograms (source: [62]). amplitude of the thoracic impedance, as it reflects a drop in thoracic motion and, indirectly, in respiratory effort [70]. This strategy permits the identification of central apneas with suboptimal accuracy, as reported in clinical studies, showing a high rate of false positive episodes [71]. [PERSON] and colleagues developed and validated an enhanced AOP detection model based on ML to automatically recognize true apnea by analyzing data from the electrocardiographic monitoring of infants [72]. The authors found that AOP often occurred in clusters and that breathing patterns were more disturbed in patients with more frequent central AOP and concluded that AI leads to improved apnea detection compared to traditional methods with fewer false alarms.