Deslandes Alison, Avery Jodie, Chen Hsiang-Ting, Leonardi Mathew, Condous George, Hull M Louise
Robinson Research Institute University of Adelaide Adelaide South Australia Australia.
School of Computer and Mathematical Sciences University of Adelaide Adelaide South Australia Australia.
Australas J Ultrasound Med. 2023 Nov 20;27(1):5-11. doi: 10.1002/ajum.12368. eCollection 2024 Feb.
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.
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.
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.
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.
本研究旨在调查人工智能（AI）工具在与妇科超声相关的超声技能教学中的当前应用情况。
进行了一项范围综述。2022年12月，使用预定义的关键词在八个数据库（MEDLINE、EMBASE、EMCARE、CINAHL、Scopus、Web of Science、IEEE Xplore和ACM数字图书馆）中进行了检索。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相关，所有类型的出版物均符合纳入标准。会议摘要和无法充分翻译成英文的非英语论文被排除。
最初的数据库检索返回了481篇文章。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。这两篇文章均未包含原始研究（一篇系统综述和一篇综述文章）。纳入的文章均未明确提供为妇科成像超声技能教学开发的特定工具的详细信息，但强调了产科领域内的类似应用，这些应用可能会得到扩展。
人工智能有可能协助超声医师和其他超声操作人员的培训，包括在妇科超声领域。然而，这项范围综述显示，迄今为止，尚未发表关于专门用于妇科超声的此类工具的使用或开发的原始研究。
