磁共振成像训练的先驱性精确性:MRI解释能力量表的引入

IF 3 4区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Halil Yilmaz, Dilber Polat
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引用次数: 0

摘要

尽管磁共振成像(MRI)在临床诊断和医学教育中发挥着核心作用,但明显缺乏专门设计的标准化、有效的工具来评估MRI解释能力。现有的评估方法往往评估一般的诊断推理,但未能解决MRI解释的独特认知需求,如空间定向、断层解剖的识别以及正常和病理结构的区分。为了应对这些挑战,本研究旨在开发MRI解释能力量表(MRI- ics),这是一种专门针对准确MRI解释所需技能的工具。采用顺序探索性混合方法。与经验丰富的MRI口译学生(通过滚雪球抽样选择)进行半结构化访谈,了解项目开发情况。采用探索性因子分析(EFA)建立建构效度,并采用Kaiser-Meyer-Olkin测度(KMO)和Bartlett's球形度检验(BTS)。信度采用Cronbach’s alpha (α)评价。MRI- ics确定了三个因素:(1)识别MRI图像结构的能力(8个项目,解释方差27.46%,Cronbach's α = 0.89);(2)专业发展必要性(7项,解释方差20.25%,Cronbach’s α = 0.80);(3)在诊断过程中的利用率(6项,解释方差14.01%,Cronbach’s α = 0.84)。总解释方差为61.72%,总Cronbach’s α为0.89。MRI- ics提供了一个可靠的、经过验证的框架,以加强全球MRI解释培训,填补了医学教育评估的关键空白。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pioneering Precision in Magnetic Resonance Imaging Training: The Introduction of the MRI Interpretation Competency Scale

Despite the central role of magnetic resonance imaging (MRI) in clinical diagnosis and medical education, there is a notable absence of standardized, validated tools specifically designed to assess MRI interpretation competencies. Existing assessment methods often evaluate general diagnostic reasoning but fail to address the unique cognitive demands of MRI interpretation, such as spatial orientation, recognition of sectional anatomy, and differentiation of normal and pathological structures. In response to these challenges, this study aimed to develop the MRI Interpretation Competency Scale (MRI-ICS), a tool specifically targeting the skills required for accurate MRI interpretation. A sequential exploratory mixed methods approach was employed. Semi-structured interviews with experienced MRI interpreter students (selected via snowball sampling) informed item development. Exploratory factor analysis (EFA) was conducted to establish construct validity, supported by the Kaiser–Meyer–Olkin measure (KMO) and Bartlett's Test of Sphericity (BTS). Reliability was assessed using Cronbach's alpha (α). The MRI-ICS identified three factors: (1) ability to discern structures in MRI images (eight items, explained variance 27.46%, Cronbach's α = 0.89); (2) necessity for professional development (seven items, explained variance 20.25%, Cronbach's α = 0.80); and (3) utilization in the diagnostic process (six items, explained variance 14.01%, Cronbach's α = 0.84). The total explained variance was 61.72%, with an overall Cronbach's α of 0.89. The MRI-ICS offers a reliable, validated framework to enhance MRI interpretation training globally, filling a critical gap in medical education assessment.

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来源期刊
International Journal of Imaging Systems and Technology
International Journal of Imaging Systems and Technology 工程技术-成像科学与照相技术
CiteScore
6.90
自引率
6.10%
发文量
138
审稿时长
3 months
期刊介绍: The International Journal of Imaging Systems and Technology (IMA) is a forum for the exchange of ideas and results relevant to imaging systems, including imaging physics and informatics. The journal covers all imaging modalities in humans and animals. IMA accepts technically sound and scientifically rigorous research in the interdisciplinary field of imaging, including relevant algorithmic research and hardware and software development, and their applications relevant to medical research. The journal provides a platform to publish original research in structural and functional imaging. The journal is also open to imaging studies of the human body and on animals that describe novel diagnostic imaging and analyses methods. Technical, theoretical, and clinical research in both normal and clinical populations is encouraged. Submissions describing methods, software, databases, replication studies as well as negative results are also considered. The scope of the journal includes, but is not limited to, the following in the context of biomedical research: Imaging and neuro-imaging modalities: structural MRI, functional MRI, PET, SPECT, CT, ultrasound, EEG, MEG, NIRS etc.; Neuromodulation and brain stimulation techniques such as TMS and tDCS; Software and hardware for imaging, especially related to human and animal health; Image segmentation in normal and clinical populations; Pattern analysis and classification using machine learning techniques; Computational modeling and analysis; Brain connectivity and connectomics; Systems-level characterization of brain function; Neural networks and neurorobotics; Computer vision, based on human/animal physiology; Brain-computer interface (BCI) technology; Big data, databasing and data mining.
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