# Applications of Artificial Intelligence in Neonatology
## 2 Applications of Artificial Intelligence
### AI and Neuromitoring
These results can be understood as the upper limit for the performance of any machine detecting seizures. In recent years, the first important steps in the transition of SDAs to the cot-side have been made. As part of the monitoring framework used to conduct an antiseizure medication trial, a 2019 study introduced an SDA in the clinical setting [42]. The real-time review systematically included the algorithm, sending direct email alerts to study neurologists if they chose to activate this function. Although all the study neurologists found the SDA useful, a high false detection rate was reported, and only two out of nine used the instant messaging alert function, others finding false-positive alerts to be too disruptive. Eventually, the first randomized clinical trial evaluating the impact of ML on the recognition of neonatal seizures in real time in an NICU was published in 2020 [43]. After repeated training and offline analysis [11; 39], the SDA developed by a group from Cork was tested in a multicenter clinical investigation by assessing the accuracy of the recognition of electrographic seizures with and without the algorithm as a cot-side support tool for the clinical team. Although the predefined target of enhancing the detection of individual infants with seizures was not met, the algorithm significantly improved the recognition of seizure hours. These results were more pronounced on weekends as compared to weekdays (45.3% in the non-algorithm group vs. 66.0% in the algorithm group). The authors speculated that the latter finding may have more reflected the situation in NICUs in which no EEG expertise is readily available, suggesting that the advantage offered by the SDA could be greater if tested with teams less experienced in bedside EEG interpretation. Although further research is warranted to demonstrate improvements in diagnostic performance, ML techniques can be safely applied in clinical care, and it  
Figure 1: An EEG viewer incorporating an SDA. In the upper panel the output of the SDA is displayed as a graph of seizure probability. The trace changes to red and an annotation is made when a seizure is identified. The interface also displays the cEEG and aEEG (source: [39]). appears likely that they will become an integral part of the neurological evaluation of vulnerable neonates. Recently, AI has also been employed in the field of neonatal neuroimaging and developmental follow-up. The internal capsule is a subcortical white matter structure composed of afferent and efferent fiber tracts connecting the cerebral hemispheres to other subcortical structures, the brainstem, and the spinal cord. The posterior limb of the internal capsule (PLIC) is one of the structures in which myelination commences in the neonatal brain. Importantly, the correct and timely maturation of the PLIC, particularly in terms of myelination, is crucial to neurological outcomes in term and preterm neonates. Abnormal or asymmetrical signals detected through magnetic resonance imaging (MRI) in the PLIC have been associated with perinatal complications, development of hemiplegia, and poor neurodevelopmental outcomes [44]. These findings are especially relevant in neonatal care, as the rate of neurodevelopmental abnormalities among infants with perinatal problems remains high despite advances in clinical practice [45]. In particular, the prevalence of cerebral palsy, a severe condition carrying important life-long burdens and associated with an abnormal PLIC MRI signal, has been increasing over recent decades, being reported as well above 2.0 per 1000 live births [46], with a significant increase with decreasing gestational age [47]. A recent study proposed an ML pipeline for the automated segmentation and quantification of the PLIC in premature infants undergoing MRI [48]. The authors showed good accuracy for the AI method compared to expert analysis, and successfully applied their ML pipeline to data from a large dataset, proving generalizability.