Artificial intelligence (AI), specifically a branch of AI called deep learning (DL), has proven revolutionary developments in almost all fields, from computer vision to health sciences, and its effects in medicine have changed clinical applications significantly. Although some sub-fields of medicine such as pediatrics have been relatively slow in receiving critical benefits of AI, related research in pediatrics started to be accumulated to a significant level too. Hence, in this paper, we review recently developed machine learning and deep learning based systems for neonatology applications. We systematically evaluate the role of AI in neonatology applications, define the methodologies, including algorithmic developments, and describe the remaining challenges in neonatal diseases. To date, survival analysis, neuroimaging, EEG, pattern analysis of vital parameters, and retinopathy of prematurity diagnosis with AI have been the main focus in neonatology. We have categorically summarized 96 research articles, from 1996 to 2022, and discussed their pros and cons, respectively. We also discuss possible directions for new AI models and the future of neonatology with the rising power of AI, suggesting roadmaps for integration of AI into neonatal intensive care units.
翻译:人工智能(AI),特别是AI称为深层次学习(DL)的一个分支,在几乎所有领域,从计算机视野到健康科学,都证明了革命性的发展,对医学的影响已经大大改变了临床应用。尽管一些次医学领域,如儿科等,在接受AI的关键好处方面相对缓慢,但有关的儿科研究也开始积累到相当的水平。因此,我们在本文件中审查了最近开发的新生儿应用机器学习和深层次学习系统。我们系统地评估了AI在新生儿应用中的作用,界定了方法,包括算法发展,并描述了新生儿疾病方面尚存的挑战。到今天,生存分析、神经成形学、EEEG、生命参数模式分析、与AI的发育前期诊断的复病症等都成为了肿瘤学的主要重点。我们明确总结了1996年至2022年的96篇研究文章,并分别讨论了其利弊。我们还讨论了新的人工智能模型和新生儿学未来可能的方向,同时讨论了AI的不断增强的动力,提出了将AI纳入新生儿密集护理单位的路线图。