Zhang Feng, Guo Liangqing, Liu Lin, Han Xiaochun
Department of Ultrasound, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan 250014, Shandong, China.
Department of Endocrinology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan 250014, Shandong, China.
Parkinsons Dis. 2025 May 22;2025:2079341. doi: 10.1155/padi/2079341. eCollection 2025.
To evaluate the diagnostic accuracy of machine learning-assisted magnetic resonance imaging (MRI) in detecting cognitive impairment among Parkinson's disease (PD) patients through a systematic review and meta-analysis. We systematically searched for studies that applied machine learning algorithms to MRI data for diagnosing PD with mild cognitive impairment (PD-MCI). Data were extracted and synthesized to calculate pooled sensitivity, specificity, positive likelihood ratio (PLR) and negative diagnostic likelihood ratio (NLR), and diagnostic odds ratios (DOR). A bivariate random-effects model and summary receiver operating characteristic (SROC) curves were employed for statistical analysis. The quality of studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) instrument. The publication bias was investigated through Deeks' funnel plot. All statistical analyses were conducted using Stata 14.0. The pooled sensitivity and specificity for diagnosing PD-MCI using machine learning-assisted MRI were 0.82 (95% CI: 0.75-0.87) and 0.81 (95% CI: 0.73-0.87), respectively. The PLR was 4.28 (95% CI: 2.93-6.27), and the NLR was 0.23 (95% CI: 0.16-0.32), indicating a high diagnostic accuracy. The area under the curve (AUC) for the SROC was 0.85 (95% CI: 0.82-0.88). Quality assessment using the QUADAS-2 tool showed a predominantly low risk of bias among the studies, and the Deeks' funnel plot suggested no significant publication bias (=0.30). In summary, the MRI combined with machine learning for diagnosing PD-MCI achieved high accuracy with the pooled sensitivity of 82% and specificity of 81%.
通过系统评价和荟萃分析，评估机器学习辅助磁共振成像（MRI）在检测帕金森病（PD）患者认知障碍中的诊断准确性。我们系统检索了将机器学习算法应用于MRI数据以诊断轻度认知障碍帕金森病（PD-MCI）的研究。提取并综合数据以计算合并敏感度、特异度、阳性似然比（PLR）和阴性诊断似然比（NLR）以及诊断比值比（DOR）。采用双变量随机效应模型和汇总受试者工作特征（SROC）曲线进行统计分析。使用诊断准确性研究质量评估（QUADAS-2）工具评估研究质量。通过Deeks漏斗图研究发表偏倚。所有统计分析均使用Stata 14.0进行。使用机器学习辅助MRI诊断PD-MCI的合并敏感度和特异度分别为0.82（95%CI：0.75-0.87）和0.81（95%CI：0.73-0.87）。PLR为4.28（95%CI：2.93-6.27），NLR为0.23（95%CI：0.16-0.32），表明诊断准确性较高。SROC曲线下面积（AUC）为0.85（95%CI：0.82-0.88）。使用QUADAS-2工具进行的质量评估显示，研究中的偏倚风险主要较低，Deeks漏斗图表明无显著发表偏倚（=0.3）。总之，MRI结合机器学习诊断PD-MCI具有较高的准确性，合并敏感度为82%，特异度为8
