通过自适应图像识别训练系统(AdaptUS)技术支持超声训练在产前诊断中的有效性。

IF 2.4 4区 医学 Q2 OBSTETRICS & GYNECOLOGY
Geburtshilfe Und Frauenheilkunde Pub Date : 2025-03-05 eCollection Date: 2025-03-01 DOI:10.1055/a-2510-7185
Talia Sachs, Stefan Michel, Katarina Koziol, Alex Kunz, Agnes Wittek, Ricarda Neubauer, Hannah Klinkhammer, Johannes Weimer, Brigitte Strizek, Florian Recker
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引用次数: 0

摘要

背景:产前诊断,特别是超声检查,对监测胎儿发育和发现潜在的并发症至关重要。传统的超声培训往往缺乏对图像识别和解释的足够关注,这对准确诊断至关重要。本研究评估AdaptUS模块的有效性,AdaptUS模块是一个技术支持的自适应学习平台,旨在提高产前医学超声诊断技能。方法:对76名德国大学附属医院医学生进行前瞻性横断面研究,分为干预组(n = 37)和对照组(n = 39)。干预组使用AdaptUS模块,该模块根据个人表现调整其内容。更准确地说,它是一个超声图像的学习程序,虽然不能直接适应用户的技能水平,但可以认为是自适应的,因为错误回答的图像会被重新呈现以重新解释。然而,图像目前是随机显示的,还没有根据用户的能力进行调整,确保挑战是一致的,但不是根据技能水平量身定制的。值得注意的是,这不是一个超声图像软件,而是一个图像解释软件,旨在帮助用户通过反复接触医学图像来提高他们的诊断技能。相比之下,对照组没有接受这种训练。在学期开始和结束时,两组学生都接受了超声波诊断技能的评估,采用了一系列16个问题,这些问题包括正确解读图像,而不是标准的多项选择题。对组内和组间检测前后结果进行统计学分析比较。结果:干预组的平均测试成绩显著提高,从70.9%提高到86.0% (p)。讨论:AdaptUS培训模块通过提供个性化的学习体验,解决了传统培训方法的不足,显著提高了超声诊断技能,特别是在产前医学方面。AdaptUS的成功凸显了将适应性学习技术纳入医学教育以弥合理论知识与实际应用之间差距的重要性。未来的研究应探索此类培训对临床实践的长期影响,并考虑采用虚拟现实等先进技术来进一步提高教育效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Effectiveness of Technology-Supported Ultrasound Training in Prenatal Diagnosis through an Adaptive Image Recognition Training System (AdaptUS).

Background: Prenatal diagnostics, particularly ultrasound examinations, are vital for monitoring fetal development and detecting potential complications. Traditional ultrasound training often lacks adequate focus on image recognition and interpretation, which are crucial for accurate diagnostics. This study evaluates the effectiveness of the AdaptUS module, a technology-supported, adaptive learning platform designed to enhance ultrasound diagnostic skills in prenatal medicine.

Methods: A prospective cross-sectional study was conducted with 76 medical students from the German University Hospital, divided into an intervention group (n = 37) and a control group (n = 39). The intervention group engaged with the AdaptUS module, which adjusts its content based on individual performance. More precisely, it is a learning program for ultrasound images that, while not directly adaptive to the user's skill level, can be considered adaptive in the sense that incorrectly answered images are presented again for re-interpretation. However, the images are currently shown at random and are not yet adjusted to the user's abilities, ensuring that the challenge is consistent but not tailored to skill level. It is important to note that this is not an ultrasound image software, but rather an image interpretation software designed to help users improve their diagnostic skills through repeated exposure to medical images. In contrast, the control group did not receive this training. Both groups were assessed on their ultrasound diagnostic skills at the beginning and end of the semester using a series of 16 questions, which involved interpreting images correctly rather than a standard multiple-choice format. Statistical analysis was performed to compare the pre- and post-test results within and between the groups.

Results: The intervention group showed a significant improvement in their mean test scores, increasing from 70.9% to 86.0% (p < 0.001), while the control group's scores decreased slightly from 62.0% to 59.0%, though this change was not statistically significant. The difference in score improvements between the intervention and control groups was statistically significant (p < 0.001). The feedback from students in the intervention group was overwhelmingly positive, highlighting the system's flexibility in addressing individual learning needs and suggesting its potential for broader integration into medical curricula.

Discussion: The AdaptUS training module significantly enhances ultrasound diagnostic skills, particularly in prenatal medicine, by providing a personalized learning experience that addresses the gaps in traditional training methods. The success of AdaptUS underscores the importance of integrating adaptive learning technologies into medical education to bridge the gap between theoretical knowledge and practical application. Future research should explore the long-term impact of such training on clinical practice and consider incorporating advanced technologies like virtual reality to further enhance educational outcomes.

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来源期刊
Geburtshilfe Und Frauenheilkunde
Geburtshilfe Und Frauenheilkunde 医学-妇产科学
CiteScore
2.50
自引率
22.20%
发文量
828
审稿时长
6-12 weeks
期刊介绍: Geburtshilfe und Frauenheilkunde (GebFra) addresses the whole field of obstetrics and gynecology and is concerned with research as much as with clinical practice. In its scientific section, it publishes original articles, reviews and case reports in all fields of the discipline, namely gynecological oncology, including oncology of the breast obstetrics and perinatal medicine, reproductive medicine, and urogynecology. GebFra invites the submission of original articles and review articles. In addition, the journal publishes guidelines, statements and recommendations in cooperation with the DGGG, SGGG, OEGGG and the Arbeitsgemeinschaft der Wissenschaftlichen Medizinischen Fachgesellschaften (AWMF, Association of Scientific Medical Societies, www.awmf.org). Apart from the scientific section, Geburtshilfe und Frauenheilkunde has a news and views section that also includes discussions, book reviews and professional information. Letters to the editors are welcome. If a letter discusses an article that has been published in our journal, the corresponding author of the article will be informed and invited to comment on the letter. The comment will be published along with the letter.
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