Version 1
: Received: 2 April 2024 / Approved: 2 April 2024 / Online: 2 April 2024 (10:56:24 CEST)
How to cite:
Rallis, D.; Baltogianni, M.; Kapetaniou, K.; Giapros, V. Current Applications of Artificial Intelligence in the Neonatal Intensive Care Unit. Preprints2024, 2024040177. https://doi.org/10.20944/preprints202404.0177.v1
Rallis, D.; Baltogianni, M.; Kapetaniou, K.; Giapros, V. Current Applications of Artificial Intelligence in the Neonatal Intensive Care Unit. Preprints 2024, 2024040177. https://doi.org/10.20944/preprints202404.0177.v1
Rallis, D.; Baltogianni, M.; Kapetaniou, K.; Giapros, V. Current Applications of Artificial Intelligence in the Neonatal Intensive Care Unit. Preprints2024, 2024040177. https://doi.org/10.20944/preprints202404.0177.v1
APA Style
Rallis, D., Baltogianni, M., Kapetaniou, K., & Giapros, V. (2024). Current Applications of Artificial Intelligence in the Neonatal Intensive Care Unit. Preprints. https://doi.org/10.20944/preprints202404.0177.v1
Chicago/Turabian Style
Rallis, D., Konstantina Kapetaniou and Vasileios Giapros. 2024 "Current Applications of Artificial Intelligence in the Neonatal Intensive Care Unit" Preprints. https://doi.org/10.20944/preprints202404.0177.v1
Abstract
Artificial intelligence (AI) refers to computer algorithms that replicate the cognitive function of humans. Machine learning is widely applicable using structured and unstructured data, while deep learning is derived from the neural networks of the human brain that process and interpret information. During the last decades, AI has been introduced in several aspects of healthcare. In this review, we aim to present the current application of AI in the neonatal intensive care unit. AI-based models have been applied to neurocritical care, including automated seizure detection algorithms and electroencephalogram-based hypoxic-ischemic encephalopathy severity grading systems. Moreover, AI models evaluating magnetic resonance imaging contributed to the progress of the evaluation of the neonatal developing brain and the understanding of how prenatal events affect both structural and functional network topologies. Furthermore, AI algorithms have been applied to predict the development of bronchopulmonary dysplasia and assess the extubation readiness of preterm neonates. Automated models have been also used for the detection of retinopathy of prematurity and the need for treatment. Among others, AI algorithms have been utilized for the detection of sepsis, the need for patent ductus arteriosus treatmnet, the evaluation of jaundice, and the detection of gastrointestinal morbidities. Finally, AI prediction models have been constructed for the evaluation of the neurodevelopmental outcome and the overall mortality of neonates. Although the application of AI in neonatology is encouraging, further research in AI models is warranted in the future including retraining clinical trials, validating the outcomes, and addressing serious ethics issues.
Keywords
artificial intelligence; machine learning; deep learning; neonate
Subject
Biology and Life Sciences, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
