# Applications of Artificial Intelligence in Neonatology
## 2 Applications of Artificial Intelligence  
### AI and Neuromitoring  
The revolution in AI--and, particularly, the subcategory of ML--has influenced research on the neuromonitoring of critically sick neonates over the last couple of decades. With the advancement in processing power and data storage, AI now enables computer systems to scrutinize and analyze enormous volumes of information, deciphering illness patterns. Clinical neuromonitoring can generate big data, particularly in terms of electroencephalogram (EEG) data [5]. An EEG is a non-invasive recording of cerebral cortex electrical activity that permits real-time assessment of cortical background function, which has prognostic utility for neonates. As EEGs also permit the differentiation of epileptic seizures from nonepileptic events and can detect nonconvexity seizures, they are also the reference tool for diagnosing seizures [6]. Continuous EEG (cEEG) recordings maximize the diagnostic utility of this tool, as they are more likely to record electrographic events, but also its prognostic significance, permitting the assessment of the evolution in background activity over time [7,8]. In any case, interpreting the plethora of information generated requires expertise and is resource-consuming, aspects that limit the diffusion of cEEG monitoring. A number of ML algorithms based on EEG background activity have been proposed with various aims. Much research has been undertaken in the area of automated EEG interpretation regarding the early severity grading of perinatal hypoxic-ischemic encephalopathy (HIE) in order to provide diagnostic decision support [9,10,11]. Recently, advanced techniques for signal processing and ML, such as convolutional neural network structures able to self-extract convolutional features from raw EEGs [12], have demonstrated strong performance metrics for classifying HIE severity grades [13]. [PERSON] et al. also investigated the potential role of AI in predictive modeling for electrographic seizures in neonates with HIE [14], aiming to identify infants at the highest risk of subsequent seizures early. ML models were developed separately and in combination for qualitative EEG (assessed by a neurophysiologist) and quantitative EEG (automatically generated by weighting the quantity of discontinuous activity) features, as well as for clinical features. Interestingly, the automated quantitative EEG analysis performed as well as the analysis executed by an expert neurophysiologist, augmenting the predictive value of these models by adding clinical information. These reports emphasize the potential of using ML to process the background EEG activity of neonates with HIE. AI has also been extensively investigated in sleep studies of term and preterm infants, using EEG data to automatically classify neonatal sleep stages [15,16]. Sleep studies are particularly relevant in the management of sick infants due to their prognostic value, as the expression of alternating vigilance states (i.e., sleep-wake cycling) is a positive prognostic factor and a global marker of neurological well-being [17,18]. Detection of sleep states could also enhance neuroprotective care, as less interruption during deep sleep can be a therapeutic approach. In any case, the research on AI-driven tools evaluating neonatal cortical background activity is still in the preclinical stage. The detection of paroxysmal events is another key purpose of EEG monitoring. The neonatal population is at the highest risk of seizures compared with any other time in life, and seizures are the most frequent neurological emergency in this age group [19]. The incidence of seizures is estimated at approximately 8% in neonates and has a parabolical relation to gestational age, with seizures being more frequent in infants born at less than 30 and more than 36 weeks of gestation [19].