Artificial intelligence as a teaching tool for gynaecological ultrasound: A systematic search and scoping review

Q3 Medicine Australasian Journal of Ultrasound in Medicine Pub Date : 2023-11-20 DOI:10.1002/ajum.12368

Alison Deslandes, Jodie Avery, Hsiang-Ting Chen, Mathew Leonardi, George Condous, M. Louise Hull

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Abstract

Purpose

The aim of this study was to investigate the current application of artificial intelligence (AI) tools in the teaching of ultrasound skills as they pertain to gynaecological ultrasound.

Methods

A scoping review was performed. Eight databases (MEDLINE, EMBASE, EMCARE, CINAHL, Scopus, Web of Science, IEEE Xplore and ACM digital library) were searched in December 2022 using predefined keywords. All types of publications were eligible for inclusion so long as they reported the use of an AI tool, included reference to or discussion of teaching or the improvement of ultrasound skills and pertained to gynaecological ultrasound. Conference abstracts and non-English language papers which could not be adequately translated into English were excluded.

Results

The initial database search returned 481 articles. After screening against our inclusion and exclusion criteria, two were deemed to meet the inclusion criteria. Neither of the articles included reported original research (one systematic review and one review article). Neither of the included articles explicitly provided details of specific tools developed for the teaching of ultrasound skills for gynaecological imaging but highlighted similar applications within the field of obstetrics which could potentially be expanded.

Conclusion

Artificial intelligence can potentially assist in the training of sonographers and other ultrasound operators, including in the field of gynaecological ultrasound. This scoping review revealed however that to date, no original research has been published reporting the use or development of such a tool specifically for gynaecological ultrasound.

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人工智能作为妇科超声教学工具：系统检索和范围审查

本研究旨在调查目前人工智能（AI）工具在妇科超声技能教学中的应用情况。2022 年 12 月，我们使用预定义的关键词检索了八个数据库（MEDLINE、EMBASE、EMCARE、CINAHL、Scopus、Web of Science、IEEE Xplore 和 ACM 数字图书馆）。所有类型的出版物只要报告了人工智能工具的使用情况，提及或讨论了教学或超声技能的提高，且与妇科超声有关，均可纳入检索范围。会议摘要和无法充分翻译成英语的非英语论文被排除在外。根据我们的纳入和排除标准进行筛选后，有两篇文章被认为符合纳入标准。这两篇文章均未报告原创性研究（一篇系统综述和一篇评论文章）。收录的文章均未明确提供为教授妇科成像超声技能而开发的特定工具的详细信息，但强调了产科领域的类似应用，这些应用有可能得到扩展。然而，此次范围界定审查显示，迄今为止，尚未有任何原创性研究报告专门针对妇科超声使用或开发此类工具。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Australasian Journal of Ultrasound in Medicine Medicine-Radiology, Nuclear Medicine and Imaging

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1.90

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0.00%

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