EARLY PREDICTION OF NEONATAL EPILEPSY BY USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

Abstract

Neonatal epilepsy syndromes are serious medical conditions that are frequently associated with structural, genetic or neuro-metabolic disorders. With timely diagnosis and treatment to mitigate the risks of neurodevelopmental impairment or other long-term complications. Due to the rapid MOVEMENTS in Artificial Intelligence (AI) and Machine Learning (ML), new opportunities arise for improving early diagnosis and more patient-specific functioning of neonatal epilepsy.

Ai is the broad umbrella term for technology that simulates human cognition thought processes, reasoning ability and decision making in which applications are being integrated into epilepsy care. Its transformative role is through real-time seizure detection, enhanced diagnostic accuracy, and tailored treatment strategies. Machine learning and deep learning models can transform the interpretation of Early electroencephalography (EEG), automating up to 80 percent of this type of analysis, which compares two intricate brain wave patterns, whilst minimizing human error. These systems can monitor continuous EEG data, detect signals with high risk of seizure generation, and even make a prediction when the seizure will occur before visually identifiable clinical signs are present.

ML techniques also achieved excellent performance when analyzing large, multicenter datasets to evaluate preliminary clinical signs and EEG characteristics. Given this, the use of such early prediction methods is valuable in cases like that of hypoxic-ischemic encephalopathy (HIE) where seizures can occur and prevention or intervention may be required.

Clinicians can shift from reactive to proactive care models by harnessing these technologies. This transition holds significant potential in alleviating the burden of neonatal epilepsy.

Neonatal care can improve when the very early prediction and treatment of epilepsy through AI and ML, which greatly enhances outcomes for patients.