{"title":"磁共振成像训练的先驱性精确性:MRI解释能力量表的引入","authors":"Halil Yilmaz, Dilber Polat","doi":"10.1002/ima.70115","DOIUrl":null,"url":null,"abstract":"<p>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 <i>α</i> = 0.89); (2) necessity for professional development (seven items, explained variance 20.25%, Cronbach's <i>α</i> = 0.80); and (3) utilization in the diagnostic process (six items, explained variance 14.01%, Cronbach's <i>α</i> = 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.</p>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 3","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ima.70115","citationCount":"0","resultStr":"{\"title\":\"Pioneering Precision in Magnetic Resonance Imaging Training: The Introduction of the MRI Interpretation Competency Scale\",\"authors\":\"Halil Yilmaz, Dilber Polat\",\"doi\":\"10.1002/ima.70115\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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 <i>α</i> = 0.89); (2) necessity for professional development (seven items, explained variance 20.25%, Cronbach's <i>α</i> = 0.80); and (3) utilization in the diagnostic process (six items, explained variance 14.01%, Cronbach's <i>α</i> = 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.</p>\",\"PeriodicalId\":14027,\"journal\":{\"name\":\"International Journal of Imaging Systems and Technology\",\"volume\":\"35 3\",\"pages\":\"\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2025-05-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ima.70115\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Imaging Systems and Technology\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/ima.70115\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Imaging Systems and Technology","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/ima.70115","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
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.
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.