PDF(Pubmed)
Abstract：
UNASSIGNED: When diagnosing Coronavirus disease 2019(COVID-19), radiologists cannot make an accurate judgments because the image characteristics of COVID-19 and other pneumonia are similar. As machine learning advances, artificial intelligence(AI) models show promise in diagnosing COVID-19 and other pneumonias. We performed a systematic review and meta-analysis to assess the diagnostic accuracy and methodological quality of the models.
UNASSIGNED: We searched PubMed, Cochrane Library, Web of Science, and Embase, preprints from medRxiv and bioRxiv to locate studies published before December 2021, with no language restrictions. And a quality assessment (QUADAS-2), Radiomics Quality Score (RQS) tools and CLAIM checklist were used to assess the quality of each study. We used random-effects models to calculate pooled sensitivity and specificity, I2 values to assess heterogeneity, and Deeks\' test to assess publication bias.
UNASSIGNED: We screened 32 studies from the 2001 retrieved articles for inclusion in the meta-analysis. We included 6737 participants in the test or validation group. The meta-analysis revealed that AI models based on chest imaging distinguishes COVID-19 from other pneumonias: pooled area under the curve (AUC) 0.96 (95 % CI, 0.94-0.98), sensitivity 0.92 (95 % CI, 0.88-0.94), pooled specificity 0.91 (95 % CI, 0.87-0.93). The average RQS score of 13 studies using radiomics was 7.8, accounting for 22 % of the total score. The 19 studies using deep learning methods had an average CLAIM score of 20, slightly less than half (48.24 %) the ideal score of 42.00.
UNASSIGNED: The AI model for chest imaging could well diagnose COVID-19 and other pneumonias. However, it has not been implemented as a clinical decision-making tool. Future researchers should pay more attention to the quality of research methodology and further improve the generalizability of the developed predictive models.
UNASSIGNED: We searched PubMed, Cochrane Library, Web of Science, and Embase, preprints from medRxiv and bioRxiv to locate studies published before December 2021, with no language restrictions. And a quality assessment (QUADAS-2), Radiomics Quality Score (RQS) tools and CLAIM checklist were used to assess the quality of each study. We used random-effects models to calculate pooled sensitivity and specificity, I2 values to assess heterogeneity, and Deeks\' test to assess publication bias.
UNASSIGNED: We screened 32 studies from the 2001 retrieved articles for inclusion in the meta-analysis. We included 6737 participants in the test or validation group. The meta-analysis revealed that AI models based on chest imaging distinguishes COVID-19 from other pneumonias: pooled area under the curve (AUC) 0.96 (95 % CI, 0.94-0.98), sensitivity 0.92 (95 % CI, 0.88-0.94), pooled specificity 0.91 (95 % CI, 0.87-0.93). The average RQS score of 13 studies using radiomics was 7.8, accounting for 22 % of the total score. The 19 studies using deep learning methods had an average CLAIM score of 20, slightly less than half (48.24 %) the ideal score of 42.00.
UNASSIGNED: The AI model for chest imaging could well diagnose COVID-19 and other pneumonias. However, it has not been implemented as a clinical decision-making tool. Future researchers should pay more attention to the quality of research methodology and further improve the generalizability of the developed predictive models.
摘要：
■在诊断2019年冠状病毒病（COVID-19）时，由于COVID-19和其他肺炎的图像特征相似，放射科医生无法做出准确的判断。随着机器学习的进步，人工智能(AI)模型在诊断COVID-19和其他肺炎方面显示出希望。我们进行了系统评价和荟萃分析，以评估模型的诊断准确性和方法学质量。
■我们搜索了PubMed，科克伦图书馆,WebofScience,和Embase,medRxiv和bioRxiv的预印本，以定位2021年12月之前发表的研究，没有语言限制。和质量评估(QUADAS-2)，使用影像组学质量评分（RQS）工具和CLAIM检查表来评估每个研究的质量。我们使用随机效应模型来计算合并的敏感性和特异性，评估异质性的I2值，和Deeks'测试以评估发表偏差。
■我们从2001年检索的文章中筛选了32项研究，以纳入荟萃分析。我们将6737名参与者纳入测试或验证组。荟萃分析显示，基于胸部影像学的AI模型将COVID-19与其他肺炎区分开来：曲线下的合并面积（AUC）0.96（95％CI，0.94-0.98），灵敏度0.92（95%CI，0.88-0.94），合并特异性0.91（95%CI，0.87-0.93）。使用影像组学的13项研究的平均RQS评分为7.8，占总分的22％。使用深度学习方法的19项研究的CLAIM平均得分为20分，略低于理想得分为42.00分的一半(48.24%)。
■胸部成像的AI模型可以很好地诊断COVID-19和其他肺炎。然而,它尚未作为临床决策工具实施.未来的研究人员应该更加关注研究方法的质量，并进一步提高所开发预测模型的泛化性。
■我们搜索了PubMed，科克伦图书馆,WebofScience,和Embase,medRxiv和bioRxiv的预印本，以定位2021年12月之前发表的研究，没有语言限制。和质量评估(QUADAS-2)，使用影像组学质量评分（RQS）工具和CLAIM检查表来评估每个研究的质量。我们使用随机效应模型来计算合并的敏感性和特异性，评估异质性的I2值，和Deeks'测试以评估发表偏差。
■我们从2001年检索的文章中筛选了32项研究，以纳入荟萃分析。我们将6737名参与者纳入测试或验证组。荟萃分析显示，基于胸部影像学的AI模型将COVID-19与其他肺炎区分开来：曲线下的合并面积（AUC）0.96（95％CI，0.94-0.98），灵敏度0.92（95%CI，0.88-0.94），合并特异性0.91（95%CI，0.87-0.93）。使用影像组学的13项研究的平均RQS评分为7.8，占总分的22％。使用深度学习方法的19项研究的CLAIM平均得分为20分，略低于理想得分为42.00分的一半(48.24%)。
■胸部成像的AI模型可以很好地诊断COVID-19和其他肺炎。然而,它尚未作为临床决策工具实施.未来的研究人员应该更加关注研究方法的质量，并进一步提高所开发预测模型的泛化性。
