# Applications of Artificial Intelligence in Neonatology
## 2 Applications of Artificial Intelligence
### AI and Neuromitoring
Early recognition of seizures is crucial as they are oftentimes a manifestation of an underlying pathological condition, such as HIE, meningitis, or stroke [20; 21], and because recent evidence suggests that early diagnosis and treatment of seizures improve the response to medication [22]. Furthermore, it is widely established that impaired long-term neurodevelopmental outcomes are associated with an increased seizure burden [23; 24; 25]. The recurrence of seizures itself appears to bring additional neurodevelopmental repercussions, independently of the underlying etiology [26]. On the other hand, treatment for nonepileptic events exposes neonates to unneeded harmful medications [27]. The diagnosis of seizures is particularly difficult in the neonatal population as the events are usually electrographic only, their manifestation can be masked by medication, and, even when present, they can be challenging to differentiate from normal neonatal movements [28]. EEG monitoring is thus crucial to identify neonatal seizures, and cEEG monitoring is the recommended standard of care for this aim [29]. According to current guidelines, 24 h cEEG monitoring should be performed for high-risk neonates to screen for seizures, and 24 h of seizure freedom are recommended if seizing activity is detected. Among infants with HIE undergoing therapeutic hypothermia, up to 72 h of EEG recording should be considered, as rewarming is the most likely phase for delayed seizures [29]. In any case, cEEG monitoring is not available in many NICUs due to resource restrictions. The interpretation of data generated with cEEGs is difficult and time-consuming and requires the availability of experienced neurophysiologists on a 24/7 basis. These aspects limit the availability of this crucial neuromonitoring tool, as only half of NICUs report the use of cEEGs, indicating an implementation issue [30]. Amplitude-integrated EEG (aEEG) monitoring has been adopted as a surrogate for cEEG monitoring, gaining universal approval and use in NICUs worldwide. An aEEG is a simplified bedside tool that displays one--or, more commonly, two--channels of EEG data quantitatively converted via smoothing, rectification, and filtering. Then, the cortical electrical activity is time-compressed and presented in a semi-logarithmic chart [31]. The role of aEEG monitoring in the assessment of background cortical activity is well-established [32] and, compared with cEEG monitoring, faster and more straightforward to apply and review. In any case, its sensitivity, specificity, and interobserver agreement are critically suboptimal in detecting seizures [33; 34], and experts suggest a complementary role, rather than replacing cEEG monitoring, for it [7; 35]. Hence, given the key role but the limited accessibility of cEEGs, significant research effort has been invested over recent decades into the development of automatic seizure detection algorithms (SDAs) [36]. After the pioneering work of [PERSON] et al. [37], who proposed a computerized detection system for neonatal seizures back in 1992, several different approaches have been reported, improved, and validated. While the initial SDAs originated from modifications of algorithms designed for adults [37; 38], showing performances far from acceptable for clinical purposes, the progressive complexity of the algorithmic solutions and the techniques for signal processing led to reliable SDAs capable of analyzing channels of raw EEG data in real time and continuously, as shown in Figure 1[39; 40]. An important theoretical limitation of these systems is that the definition of ground truth is not absolute in the detection of seizures. An SDA can only perform as well as the neurophysiologist who performed the annotation of the training data. The insightful report by [PERSON] et al. employed the gold standard for EEG monitoring--multichannel cEEG monitoring--to determine the interobserver agreement regarding neonatal seizure identification among three international experts, which was reported to be 83% [41].