Xiong Yaxin, Gao Yuan, Qi Yucheng, Zhi Yingfei, Xu Jia, Wang Kuo, Yang Qiuyue, Wang Changsong, Zhao Mingyan, Meng Xianglin
Department of Critical Care Medicine, First Affiliated Hospital of Harbin Medical University, Heilongjiang, China.
Heilongjiang Provincial Key Laboratory of Critical Care Medicine, Heilongjiang, China.
BMC Med Inform Decis Mak. 2025 Jan 28;25(1):44. doi: 10.1186/s12911-025-02869-0.
Acute respiratory distress syndrome (ARDS) is a serious threat to human life. Hence, early and accurate diagnosis and treatment are crucial for patient survival. This meta-analysis evaluates the accuracy of artificial intelligence in the early diagnosis of ARDS and provides guidance for future research and applications.
A search on PubMed, Embase, Cochrane, Web of Science, CNKI, Wanfang, Chinese Biomedical Literature (CBM), and VIP databases was systematically conducted, from their establishment to November 2023, to obtain eligible studies for the analysis and evaluation of the predictive effect of AI on ARDS. The retrieved literature was screened according to inclusion and exclusion criteria, the quality of the included studies was assessed using QUADAS-2, and the included studies were statistically analyzed.
Among the 2, 996 studies, 33 were included in this meta-analysis, which showed that the pooled sensitivity of AI in predicting ARDS was 0.81 (0.76-0.85), the pooled specificity was 0.88 (0.84-0.91), and the area under the receiver operating characteristic curve (AUC) was 0.91 (0.88-0.93). The analyzed studies included 28 models, with a pooled sensitivity of 0.79 (0.76-0.82), a pooled specificity of 0.85 (0.83-0.88), and an AUC of 0.89 (0.86-0.91). In the subgroup analysis, the pooled AUC of the AI models ANN, CNN, LR, RF, SVM, and XGB were 0.86 (0.83-0.89), 0.91 (0.88-0.93), 0.86 (0.83-0.89), and 0.89 (0.86-0.91), 0.90 (0.87-0.92), 0.93 (0.90-0.95), respectively. In an additional subgroup analysis, we evaluated the predictive performance of the AI models trained using different predictors. This meta-analysis was registered in PROSPERO (CRD42023491546).
AI has good sensitivity and specificity for predicting ARDS, indicating a high clinical application value. Algorithmic models such as CNN, SVM, and XGB have improved prediction performance. The subgroup analysis revealed that the model trained using images combined with other predictors had the best predictive performance.
急性呼吸窘迫综合征（ARDS）对人类生命构成严重威胁。因此，早期准确的诊断和治疗对患者生存至关重要。本荟萃分析评估人工智能在ARDS早期诊断中的准确性，并为未来的研究和应用提供指导。
系统检索PubMed、Embase、Cochrane、Web of Science、中国知网（CNKI）、万方、中国生物医学文献数据库（CBM）和维普数据库，检索时间从建库至2023年11月，以获取 eligible 研究，用于分析和评估人工智能对ARDS的预测效果。根据纳入和排除标准对检索到的文献进行筛选，使用QUADAS-2评估纳入研究的质量，并对纳入研究进行统计分析。
在2996项研究中，33项被纳入本荟萃分析，结果显示人工智能预测ARDS的合并灵敏度为0.81（0.76 - 0.85），合并特异度为0.88（0.84 - 0.91），受试者工作特征曲线下面积（AUC）为0.91（0.88 - 0.93）。分析的研究包括28个模型，合并灵敏度为0.79（0.76 - 0.82），合并特异度为0.85（0.83 - 0.88），AUC为0.89（0.86 - 0.91）。在亚组分析中，人工智能模型人工神经网络（ANN）、卷积神经网络（CNN）、逻辑回归（LR）、随机森林（RF）、支持向量机（SVM）和极端梯度提升（XGB）的合并AUC分别为0.86（0.83 - 0.89）、0.91（0.88 - 0.93）、0.86（0.83 - 0.89）、0.89（0.86 - 0.91）、0.90（0.87 - 0.92）、0.93（0.90 - 0.95）。在另一项亚组分析中，我们评估了使用不同预测因子训练的人工智能模型的预测性能。本荟萃分析已在国际前瞻性系统评价注册库（PROSPERO）注册（CRD42023491546）。
人工智能对ARDS具有良好的灵敏度和特异度，表明具有较高的临床应用价值。CNN、SVM和XGB等算法模型具有改进的预测性能。亚组分析显示，使用图像与其他预测因子相结合训练的模型具有最佳的预测性能。
