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人工智能与儿童喘息:我们目前的进展如何?

Artificial intelligence and wheezing in children: where are we now?

作者信息

Venditto Laura, Morano Sonia, Piazza Michele, Zaffanello Marco, Tenero Laura, Piacentini Giorgio, Ferrante Giuliana

机构信息

Cystic Fibrosis Center of Verona, Azienda Ospedaliera Universitaria Integrata, Verona, Italy.

Pediatric Division, Department of Surgery, Dentistry, Pediatrics and Gynaecology, University of Verona, Verona, Italy.

出版信息

Front Med (Lausanne). 2024 Aug 27;11:1460050. doi: 10.3389/fmed.2024.1460050. eCollection 2024.

Abstract

Wheezing is a common condition in childhood, and its prevalence has increased in the last decade. Up to one-third of preschoolers develop recurrent wheezing, significantly impacting their quality of life and healthcare resources. Artificial Intelligence (AI) technologies have recently been applied in paediatric allergology and pulmonology, contributing to disease recognition, risk stratification, and decision support. Additionally, the COVID-19 pandemic has shaped healthcare systems, resulting in an increased workload and the necessity to reduce access to hospital facilities. In this view, AI and Machine Learning (ML) approaches can help address current issues in managing preschool wheezing, from its recognition with AI-augmented stethoscopes and monitoring with smartphone applications, aiming to improve parent-led/self-management and reducing economic and social costs. Moreover, in the last decade, ML algorithms have been applied in wheezing phenotyping, also contributing to identifying specific genes, and have been proven to even predict asthma in preschoolers. This minireview aims to update our knowledge on recent advancements of AI applications in childhood wheezing, summarizing and discussing the current evidence in recognition, diagnosis, phenotyping, and asthma prediction, with an overview of home monitoring and tele-management.

摘要

喘息是儿童期的常见病症,在过去十年中其患病率有所上升。多达三分之一的学龄前儿童会出现反复喘息,这对他们的生活质量和医疗资源产生了重大影响。人工智能(AI)技术最近已应用于儿科过敏学和肺病学领域,有助于疾病识别、风险分层和决策支持。此外,新冠疫情对医疗系统产生了影响,导致工作量增加,同时有必要减少医院设施的使用。从这个角度来看,人工智能和机器学习(ML)方法有助于解决当前学龄前喘息管理中的问题,从使用人工智能增强听诊器进行识别到通过智能手机应用程序进行监测,旨在改善家长主导/自我管理并降低经济和社会成本。此外,在过去十年中,机器学习算法已应用于喘息表型分析,也有助于识别特定基因,并且已被证明甚至可以预测学龄前儿童的哮喘。本综述旨在更新我们对人工智能在儿童喘息方面最新进展的认识,总结和讨论目前在识别、诊断、表型分析和哮喘预测方面的证据,并概述家庭监测和远程管理。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c005/11385867/448e73e63b76/fmed-11-1460050-g001.jpg

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