Artificial Intelligence in the Diagnosis and Management of Appendicitis in Pediatric Departments: A Systematic Review.

IF 1.5 3区医学 Q2 PEDIATRICS

European Journal of Pediatric Surgery Pub Date : 2024-10-01 Epub Date: 2024-01-30 DOI:10.1055/a-2257-5122

Robin Rey, Renato Gualtieri, Giorgio La Scala, Klara Posfay Barbe

{"title":"Artificial Intelligence in the Diagnosis and Management of Appendicitis in Pediatric Departments: A Systematic Review.","authors":"Robin Rey, Renato Gualtieri, Giorgio La Scala, Klara Posfay Barbe","doi":"10.1055/a-2257-5122","DOIUrl":null,"url":null,"abstract":"Introduction: Artificial intelligence (AI) is a growing field in medical research that could potentially help in the challenging diagnosis of acute appendicitis (AA) in children. However, usefulness of AI in clinical settings remains unclear. Our aim was to assess the accuracy of AIs in the diagnosis of AA in the pediatric population through a systematic literature review.Methods: PubMed, Embase, and Web of Science were searched using the following keywords: \"pediatric,\" \"artificial intelligence,\" \"standard practices,\" and \"appendicitis,\" up to September 2023. The risk of bias was assessed using PROBAST.Results: A total of 302 articles were identified and nine articles were included in the final review. Two studies had prospective validation, seven were retrospective, and no randomized control trials were found. All studies developed their own algorithms and had an accuracy greater than 90% or area under the curve >0.9. All studies were rated as a \"high risk\" concerning their overall risk of bias.Conclusion: We analyzed the current status of AI in the diagnosis of appendicitis in children. The application of AI shows promising potential, but the need for more rigor in study design, reporting, and transparency is urgent to facilitate its clinical implementation.","PeriodicalId":56316,"journal":{"name":"European Journal of Pediatric Surgery","volume":" ","pages":"385-391"},"PeriodicalIF":1.5000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Pediatric Surgery","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1055/a-2257-5122","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/30 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"PEDIATRICS","Score":null,"Total":0}

引用次数: 0

Abstract

Introduction: Artificial intelligence (AI) is a growing field in medical research that could potentially help in the challenging diagnosis of acute appendicitis (AA) in children. However, usefulness of AI in clinical settings remains unclear. Our aim was to assess the accuracy of AIs in the diagnosis of AA in the pediatric population through a systematic literature review.

Methods: PubMed, Embase, and Web of Science were searched using the following keywords: "pediatric," "artificial intelligence," "standard practices," and "appendicitis," up to September 2023. The risk of bias was assessed using PROBAST.

Results: A total of 302 articles were identified and nine articles were included in the final review. Two studies had prospective validation, seven were retrospective, and no randomized control trials were found. All studies developed their own algorithms and had an accuracy greater than 90% or area under the curve >0.9. All studies were rated as a "high risk" concerning their overall risk of bias.

Conclusion: We analyzed the current status of AI in the diagnosis of appendicitis in children. The application of AI shows promising potential, but the need for more rigor in study design, reporting, and transparency is urgent to facilitate its clinical implementation.

查看原文本刊更多论文

人工智能在儿科阑尾炎诊断和管理中的应用：系统综述。

导言人工智能是医学研究中一个不断发展的领域，有可能帮助诊断儿童急性阑尾炎（AA）。然而，人工智能在临床环境中的实用性仍不明确。我们的目的是通过系统性文献综述来评估人工智能在诊断儿童急性阑尾炎方面的准确性。方法使用以下关键词搜索 PubMed、Embase 和 Web of Science："儿科"、"人工智能"、"标准实践 "和 "阑尾炎"，搜索时间截至 2023 年 9 月。使用 PROBAST 对偏倚风险进行了评估。结果共发现了 302 篇文章，最终有 9 篇文章被纳入最终综述。其中两项研究为前瞻性验证，七项为回顾性研究，未发现随机对照试验。所有研究都开发了自己的算法，准确率超过 90% 或 AUC > 0.9。所有研究的总体偏倚风险均被评为 "高风险"。结论我们分析了人工智能在儿童阑尾炎诊断中的应用现状。人工智能的应用前景广阔，但迫切需要在研究设计、报告和透明度方面更加严格，以促进其临床应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

European Journal of Pediatric Surgery 医学-外科

CiteScore

3.90

自引率

5.60%

发文量

审稿时长

6-12 weeks

期刊介绍： This broad-based international journal updates you on vital developments in pediatric surgery through original articles, abstracts of the literature, and meeting announcements. You will find state-of-the-art information on: abdominal and thoracic surgery neurosurgery urology gynecology oncology orthopaedics traumatology anesthesiology child pathology embryology morphology Written by surgeons, physicians, anesthesiologists, radiologists, and others involved in the surgical care of neonates, infants, and children, the EJPS is an indispensable resource for all specialists.