Accuracy of artificial intelligence algorithms in predicting acute respiratory distress syndrome: a systematic review and meta-analysis
Abstract Background 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 guida...
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| Language: | English |
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BMC
2025-01-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | https://doi.org/10.1186/s12911-025-02869-0 |
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| author | Yaxin Xiong Yuan Gao Yucheng Qi Yingfei Zhi Jia Xu Kuo Wang Qiuyue Yang Changsong Wang Mingyan Zhao Xianglin Meng |
| author_facet | Yaxin Xiong Yuan Gao Yucheng Qi Yingfei Zhi Jia Xu Kuo Wang Qiuyue Yang Changsong Wang Mingyan Zhao Xianglin Meng |
| author_sort | Yaxin Xiong |
| collection | DOAJ |
| description | Abstract Background 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. Methods 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. Results 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). Conclusion 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. |
| format | Article |
| id | doaj-art-853a1f795f4f4f338ac9facedca6691a |
| institution | Kabale University |
| issn | 1472-6947 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj-art-853a1f795f4f4f338ac9facedca6691a2025-02-02T12:27:48ZengBMCBMC Medical Informatics and Decision Making1472-69472025-01-0125112010.1186/s12911-025-02869-0Accuracy of artificial intelligence algorithms in predicting acute respiratory distress syndrome: a systematic review and meta-analysisYaxin Xiong0Yuan Gao1Yucheng Qi2Yingfei Zhi3Jia Xu4Kuo Wang5Qiuyue Yang6Changsong Wang7Mingyan Zhao8Xianglin Meng9Department of Critical Care Medicine, First Affiliated Hospital of Harbin Medical UniversityDepartment of Critical Care Medicine, First Affiliated Hospital of Harbin Medical UniversityDepartment of Critical Care Medicine, First Affiliated Hospital of Harbin Medical UniversityDepartment of Critical Care Medicine, First Affiliated Hospital of Harbin Medical UniversityDepartment of Critical Care Medicine, First Affiliated Hospital of Harbin Medical UniversityDepartment of Critical Care Medicine, First Affiliated Hospital of Harbin Medical UniversityDepartment of Critical Care Medicine, First Affiliated Hospital of Harbin Medical UniversityDepartment of Critical Care Medicine, First Affiliated Hospital of Harbin Medical UniversityDepartment of Critical Care Medicine, First Affiliated Hospital of Harbin Medical UniversityDepartment of Critical Care Medicine, First Affiliated Hospital of Harbin Medical UniversityAbstract Background 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. Methods 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. Results 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). Conclusion 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.https://doi.org/10.1186/s12911-025-02869-0Artificial intelligenceAcute respiratory distress symptomsPredictionMeta-analysis |
| spellingShingle | Yaxin Xiong Yuan Gao Yucheng Qi Yingfei Zhi Jia Xu Kuo Wang Qiuyue Yang Changsong Wang Mingyan Zhao Xianglin Meng Accuracy of artificial intelligence algorithms in predicting acute respiratory distress syndrome: a systematic review and meta-analysis BMC Medical Informatics and Decision Making Artificial intelligence Acute respiratory distress symptoms Prediction Meta-analysis |
| title | Accuracy of artificial intelligence algorithms in predicting acute respiratory distress syndrome: a systematic review and meta-analysis |
| title_full | Accuracy of artificial intelligence algorithms in predicting acute respiratory distress syndrome: a systematic review and meta-analysis |
| title_fullStr | Accuracy of artificial intelligence algorithms in predicting acute respiratory distress syndrome: a systematic review and meta-analysis |
| title_full_unstemmed | Accuracy of artificial intelligence algorithms in predicting acute respiratory distress syndrome: a systematic review and meta-analysis |
| title_short | Accuracy of artificial intelligence algorithms in predicting acute respiratory distress syndrome: a systematic review and meta-analysis |
| title_sort | accuracy of artificial intelligence algorithms in predicting acute respiratory distress syndrome a systematic review and meta analysis |
| topic | Artificial intelligence Acute respiratory distress symptoms Prediction Meta-analysis |
| url | https://doi.org/10.1186/s12911-025-02869-0 |
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