Division of Pediatric Gastroenterology, Hepatology and Nutrition, Cincinnati Children's Hospital Medictal Center, University of Cincinnati, OH, USA.
Division of Gastroenterology, Hepatology, and Nutrition, and the SickKids Research and Learning Institutes, The Hospital for Sick Children, Toronto, ON, Canada; Department of Paediatrics and The Wilson Centre, University of Toronto, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
Gastrointest Endosc Clin N Am. 2023 Apr;33(2):291-308. doi: 10.1016/j.giec.2022.12.001.
The application of artificial intelligence (AI) has great promise for improving pediatric endoscopy. The majority of preclinical studies have been undertaken in adults, with the greatest progress being made in the context of colorectal cancer screening and surveillance. This development has only been possible with advances in deep learning, like the convolutional neural network model, which has enabled real-time detection of pathology. Comparatively, the majority of deep learning systems developed in inflammatory bowel disease have focused on predicting disease severity and were developed using still images rather than videos. The application of AI to pediatric endoscopy is in its infancy, thus providing an opportunity to develop clinically meaningful and fair systems that do not perpetuate societal biases. In this review, we provide an overview of AI, summarize the advances of AI in endoscopy, and describe its potential application to pediatric endoscopic practice and education.
人工智能(AI)的应用在改善儿科内窥镜检查方面具有巨大的前景。大多数临床前研究都是在成年人中进行的,在结直肠癌筛查和监测方面取得了最大的进展。这一发展只有在深度学习的进步的情况下才成为可能,例如卷积神经网络模型,它实现了实时检测病理学。相比之下,大多数在炎症性肠病中开发的深度学习系统都集中在预测疾病严重程度上,并且是使用静态图像而不是视频开发的。AI 在儿科内窥镜检查中的应用还处于起步阶段,因此有机会开发具有临床意义和公平性的系统,而不会延续社会偏见。在这篇综述中,我们提供了 AI 的概述,总结了 AI 在内窥镜检查方面的进展,并描述了其在儿科内窥镜检查实践和教育中的潜在应用。
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