Artificial Intelligence Aided Ultrasound Detection of DDH: A Scoping Review Protocol

Rusul Yonis, Daniel Perry, James S Bowness
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Abstract

Background: Ultrasound imaging plays a pivotal role in the diagnosis and monitoring of developmental dysplasia of the hip (DDH). However, this step requires a formal referral to the radiology department for an ultrasound by an expert radiologist or sonographer. This process can delay diagnosis and treatment initiation due to long wait times caused by the high demand on NHS services. In recent years, there has been a growing interest in leveraging artificial intelligence (AI) in ultrasound imaging. AI has potential to assist in image acquisition and interpretation, to inform clinical decision-making. Further benefits may include improved accuracy, efficiency, and consistency in diagnosis, ultimately leading to better patient outcomes. This scoping review aims to review the evidence for AI to support ultrasound detection of DDH, including reviewing the methodologies employed, the accuracy and utility of algorithms, challenges and opportunities for clinical translation, and requirements for future research. Methods: We will conduct a comprehensive search of the literature using multiple databases, including ACM Digital Library, EMBASE, OVID MEDLINE, PUBMED, COCHRANE Library, CINAHL, and IEEE Explore. These databases cover a wide range of academic disciplines, including computer science, and medical sciences, ensuring thorough coverage of relevant studies related to artificial intelligence (AI) in ultrasound for developmental dysplasia of the hip (DDH). In addition, we will explore the International Committee of Medical Journal Editors (ICMJE) approved clinical trial registries and the World Health Organization (WHO) clinical trials registry to identify ongoing or completed studies in this field. This will capture relevant research that may not yet be published in peer-reviewed journals. To supplement the research databases, we will search the websites of international societies in relevant fields, such as the British Society of Children's Orthopaedic Surgery (BSCOS) and Paediatric Orthopaedic Society of North America (POSNA). As AI has a strong commercial interest, we will review product information and publicly available evidence from EXO Imaging (https://www.exo.inc), a commercial company with a known interest in this field and an established AI aided US device. Discussion: This scoping review represents the first comprehensive attempt to gather the available evidence on the application of AI in ultrasound imaging for the diagnosis of DDH. By systematically reviewing and synthesizing a diverse range of studies, we aim to provide an overview of the current state of the art in this emerging field, identify gaps in the literature, and inform future research.
人工智能辅助超声检测 DDH:范围审查协议
背景:超声波成像在髋关节发育不良(DDH)的诊断和监测中起着关键作用。然而,这一步骤需要正式转诊到放射科,由放射科专家或超声波技师进行超声波检查。近年来,人们对利用人工智能(AI)进行超声成像越来越感兴趣。人工智能有可能协助图像采集和解读,为临床决策提供信息。方法:我们将使用多个数据库对文献进行全面检索,包括 ACM Digital Library、EMBASE、OVID MEDLINE、PUBMED、COCHRANE Library、CINAHL 和 IEEE Explore。这些数据库涵盖了包括计算机科学和医学科学在内的广泛学术学科,确保了人工智能(AI)在髋关节发育不良(DDH)超声波检查中的相关研究的全面覆盖。此外,我们还将探索国际医学期刊编辑委员会(ICMJE)批准的临床试验注册表和世界卫生组织(WHO)临床试验注册表,以确定该领域正在进行或已完成的研究。为了补充研究数据库,我们将搜索相关领域的国际学会网站,如英国儿童矫形外科学会 (BSCOS) 和北美儿童矫形外科学会 (POSNA)。由于人工智能具有强烈的商业利益,我们将审查 EXO Imaging (https://www.exo.inc) 的产品信息和公开可用的证据,EXO Imaging 是一家商业公司,在这一领域具有众所周知的利益,并且是一种成熟的人工智能辅助美国设备。讨论:本范围综述是首次全面尝试收集人工智能在超声成像中应用于诊断 DDH 的现有证据。通过系统回顾和综合各种研究,我们旨在概述这一新兴领域的技术现状,找出文献中的空白,并为未来的研究提供参考。
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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