目的:总结机器学习算法在MRI上区分骶髂关节炎特征方面的性能证据,并将其与人类医师的准确性进行比较。
方法:MEDLINE,EMBASE,CIHNAL,WebofScience,IEEE,搜索了美国风湿病学会和欧洲风湿病学协会联盟摘要档案,以获取2008年至2023年6月4日之间发表的研究。两位作者独立筛选并提取了变量,并使用表格和森林地块显示结果。
结果:选自2381项研究。超过一半的研究使用了深度学习模型,使用评估的脊柱关节炎国际协会骶髂关节炎的标准作为基本事实,并手动提取感兴趣的区域。所有研究都报告了曲线下的面积作为性能指标,范围从0.76到0.99。敏感性和特异性是第二常报告的指标,敏感性为0.56-1.00,特异性为0.67-1.00;这些结果与放射科医师在同一队列中的敏感性为0.67-1.00,特异性为0.78-1.00。超过一半的研究表明,由于样本量小或过拟合问题,在质量评估的分析领域存在较高的偏倚风险。
结论:由于研究之间的高度异质性和小样本量,机器学习算法在MRI上辨别骶髂关节炎特征的性能不同。过拟合,以及个别研究的漏报问题。需要进一步精心设计和透明的研究。
Summarise the evidence of the performance of the machine learning algorithm in discriminating
sacroiliitis features on MRI and compare it with the accuracy of human physicians.
MEDLINE, EMBASE, CIHNAL, Web of Science, IEEE, American College of Rheumatology and European Alliance of Associations for Rheumatology abstract archives were searched for studies published between 2008 and 4 June 2023. Two authors independently screened and extracted the variables, and the results are presented using tables and forest plots.
Ten studies were selected from 2381. Over half of the studies used deep learning models, using Assessment of Spondyloarthritis International Society
sacroiliitis criteria as the ground truth, and manually extracted the regions of interest. All studies reported the area under the curve as a performance index, ranging from 0.76 to 0.99. Sensitivity and specificity were the second-most commonly reported indices, with sensitivity ranging from 0.56 to 1.00 and specificity ranging from 0.67 to 1.00; these results are comparable to a radiologist\'s sensitivity of 0.67-1.00 and specificity of 0.78-1.00 in the same cohort. More than half of the studies showed a high risk of bias in the analysis domain of quality appraisal owing to the small sample size or overfitting issues.
The performance of machine learning algorithms in discriminating
sacroiliitis features on MRI varied owing to the high heterogeneity between studies and the small sample sizes, overfitting, and under-reporting issues of individual studies. Further well-designed and transparent studies are required.