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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.
Keywords acute appendicitis - children - artificial intelligence - diagnostic accuracy Ethical ApprovalNo ethical approval was required as the review concerns data from previously published studies.
The manuscript does not contain any individual's data in any form.
The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.
None declared.
All authors contributed to the study conception and design. R.R. and R.G. carried out the literature search, extracted and analyzed the data, and wrote the first draft of the manuscript; K.P.B. and G.L.S. reviewed the manuscript for important intellectual content; all authors approved the final version of the manuscript.
Received: 23 October 2023
Accepted: 25 January 2024
Accepted Manuscript online:
30 January 2024
Article published online:
29 February 2024
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