{"title":"使用磁共振成像数据识别运动障碍症受影响脑区的机器学习方法：系统综述与诊断荟萃分析》（A Systematic Review and Diagnostic Meta-analysis）。","authors":"Sadegh Ghaderi PhD, Mahdi Mohammadi PhD, Fatemeh Sayehmiri PhD, Sana Mohammadi MD, Arian Tavasol MD, Masoud Rezaei PhD, Azadeh Ghalyanchi-Langeroudi PhD","doi":"10.1002/jmri.29364","DOIUrl":null,"url":null,"abstract":"<div>\n \n <section>\n \n <h3> Background</h3>\n \n <p>Movement disorders such as Parkinson's disease are associated with structural and functional changes in specific brain regions. Advanced magnetic resonance imaging (MRI) techniques combined with machine learning (ML) are promising tools for identifying imaging biomarkers and patterns associated with these disorders.</p>\n </section>\n \n <section>\n \n <h3> Purpose/Hypothesis</h3>\n \n <p>We aimed to systematically identify the brain regions most commonly affected in movement disorders using ML approaches applied to structural and functional MRI data. We searched the PubMed and Scopus databases using relevant keywords up to June 2023 for studies that used ML approaches to detect brain regions associated with movement disorders using MRI data.</p>\n </section>\n \n <section>\n \n <h3> Study Type</h3>\n \n <p>A systematic review and diagnostic meta-analysis.</p>\n </section>\n \n <section>\n \n <h3> Population/Subjects</h3>\n \n <p>Sixty-seven studies with 6,285 patients were included.</p>\n </section>\n \n <section>\n \n <h3> Field Strength/Sequence</h3>\n \n <p>Studies utilizing 1.5T or 3T MR scanners and the acquisition of diffusion tensor imaging (DTI), structural MRI (sMRI), functional MRI (fMRI), or a combination of these were included.</p>\n </section>\n \n <section>\n \n <h3> Assessment</h3>\n \n <p>The authors independently assessed the study quality using the CLAIM and QUADAS-2 criteria and extracted data on diagnostic accuracy measures.</p>\n </section>\n \n <section>\n \n <h3> Statistical Tests</h3>\n \n <p>Sensitivity, specificity, accuracy, and area under the curve were pooled using random-effects models. Q statistics and the <i>I</i><sup>2</sup> index were used to evaluate heterogeneity, and Begg's funnel plot was used to identify publication bias.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>sMRI showed the highest sensitivity (93%) and mixed modalities had the highest specificity (90%) for detecting regional abnormalities. sMRI had a 94% sensitivity for identifying subcortical changes. The support vector machine (93%) and logistic regression (91%) models exhibited high diagnostic accuracies.</p>\n </section>\n \n <section>\n \n <h3> Data Conclusion</h3>\n \n <p>The combination of advanced MR neuroimaging techniques and ML is a promising approach for identifying brain biomarkers and affected regions in movement disorders with subcortical structures frequently implicated. Structural MRI, in particular, showed strong performance.</p>\n </section>\n \n <section>\n \n <h3> Level of Evidence</h3>\n \n <p>1</p>\n </section>\n \n <section>\n \n <h3> Technical Efficacy</h3>\n \n <p>Stage 2</p>\n </section>\n </div>","PeriodicalId":3,"journal":{"name":"ACS Applied Electronic Materials","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine Learning Approaches to Identify Affected Brain Regions in Movement Disorders Using MRI Data: A Systematic Review and Diagnostic Meta-analysis\",\"authors\":\"Sadegh Ghaderi PhD, Mahdi Mohammadi PhD, Fatemeh Sayehmiri PhD, Sana Mohammadi MD, Arian Tavasol MD, Masoud Rezaei PhD, Azadeh Ghalyanchi-Langeroudi PhD\",\"doi\":\"10.1002/jmri.29364\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>Movement disorders such as Parkinson's disease are associated with structural and functional changes in specific brain regions. 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引用次数: 0
摘要
背景：帕金森病等运动障碍与特定脑区的结构和功能变化有关。先进的磁共振成像（MRI）技术与机器学习（ML）相结合，是识别与这些疾病相关的成像生物标志物和模式的有前途的工具：我们旨在利用应用于结构性和功能性 MRI 数据的 ML 方法，系统性地确定运动障碍中最常受影响的脑区。截至 2023 年 6 月，我们使用相关关键词在 PubMed 和 Scopus 数据库中检索了使用 ML 方法检测磁共振成像数据与运动障碍相关的脑区的研究：研究类型：系统综述和诊断荟萃分析：共纳入 67 项研究，涉及 6285 名患者：场强/序列：纳入了使用1.5T或3T磁共振扫描仪并采集了弥散张量成像（DTI）、结构磁共振成像（sMRI）、功能磁共振成像（fMRI）或这些成像的组合的研究：作者使用 CLAIM 和 QUADAS-2 标准独立评估研究质量，并提取诊断准确性测量数据：使用随机效应模型对敏感性、特异性、准确性和曲线下面积进行汇总。结果：在检测区域异常方面，sMRI 的灵敏度最高（93%），混合模式的特异度最高（90%）。支持向量机（93%）和逻辑回归（91%）模型的诊断准确率很高：数据结论：将先进的磁共振神经成像技术与 ML 相结合是一种很有前景的方法，可用于识别运动障碍的大脑生物标记物和受影响区域，皮层下结构经常受到牵连。尤其是结构磁共振成像，显示出强大的性能：1 技术效率：第 2 阶段。
Machine Learning Approaches to Identify Affected Brain Regions in Movement Disorders Using MRI Data: A Systematic Review and Diagnostic Meta-analysis
Background
Movement disorders such as Parkinson's disease are associated with structural and functional changes in specific brain regions. Advanced magnetic resonance imaging (MRI) techniques combined with machine learning (ML) are promising tools for identifying imaging biomarkers and patterns associated with these disorders.
Purpose/Hypothesis
We aimed to systematically identify the brain regions most commonly affected in movement disorders using ML approaches applied to structural and functional MRI data. We searched the PubMed and Scopus databases using relevant keywords up to June 2023 for studies that used ML approaches to detect brain regions associated with movement disorders using MRI data.
Study Type
A systematic review and diagnostic meta-analysis.
Population/Subjects
Sixty-seven studies with 6,285 patients were included.
Field Strength/Sequence
Studies utilizing 1.5T or 3T MR scanners and the acquisition of diffusion tensor imaging (DTI), structural MRI (sMRI), functional MRI (fMRI), or a combination of these were included.
Assessment
The authors independently assessed the study quality using the CLAIM and QUADAS-2 criteria and extracted data on diagnostic accuracy measures.
Statistical Tests
Sensitivity, specificity, accuracy, and area under the curve were pooled using random-effects models. Q statistics and the I2 index were used to evaluate heterogeneity, and Begg's funnel plot was used to identify publication bias.
Results
sMRI showed the highest sensitivity (93%) and mixed modalities had the highest specificity (90%) for detecting regional abnormalities. sMRI had a 94% sensitivity for identifying subcortical changes. The support vector machine (93%) and logistic regression (91%) models exhibited high diagnostic accuracies.
Data Conclusion
The combination of advanced MR neuroimaging techniques and ML is a promising approach for identifying brain biomarkers and affected regions in movement disorders with subcortical structures frequently implicated. Structural MRI, in particular, showed strong performance.
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