Artificial intelligence in predicting chronic kidney disease prognosis. A systematic review and meta-analysis
Background Chronic kidney disease (CKD) is a common condition that can lead to serious health complications. Artificial Intelligence (AI) has shown the potential to improve the prediction of CKD progression, offering increased accuracy over traditional methods. Therefore, this systematic review and...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Renal Failure |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/0886022X.2024.2435483 |
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| author | Qinyu Pan Mengli Tong |
| author_facet | Qinyu Pan Mengli Tong |
| author_sort | Qinyu Pan |
| collection | DOAJ |
| description | Background Chronic kidney disease (CKD) is a common condition that can lead to serious health complications. Artificial Intelligence (AI) has shown the potential to improve the prediction of CKD progression, offering increased accuracy over traditional methods. Therefore, this systematic review and meta-analysis examine the diagnostic performance of various AI models in predicting CKD.Method Search was performed in different databases for studies reporting the diagnostic accuracy of AI-based prediction models for the progression of CKD. Meanwhile, pre-defined eligibility criteria were used for the selection of studies. Pooled sensitivity, specificity, and area under curve (AUC) were calculated utilizing Meta-disc 1.4. Quality assessment was performed using the prediction model risk of bias assessment tool (PROBAST).Results A total of 33 studies were included. The pooled sensitivity of prediction tools was 0.43 (95% CI, 0.41–0.44, I2 = 99.3%, p < 0.01). A significant difference (p < 0.01) was also observed in the pooled specificity 0.92 (95% CI, 0.91–0.92, I2 = 99.5%). Positive likelihood ratio (PLP) and negative likelihood ratio (NLR) were 5.12 (95% CI: 3.60–7.27, I2 = 91.3%, p < 0.01) and 0.28 (95% CI: 0.21–0.37, I2 = 99.3%, p < 0.01), respectively and AUC was 0.89, suggesting a diagnostic accuracy of AI-based prediction models for the progression of CKD.Conclusions This study demonstrates the promising potential of AI models in predicting CKD progression. However, further efforts are needed to optimize model performance, particularly in balancing sensitivity and specificity to ensure generalizability across diverse populations. Limitations of this study include the potential for overfitting in certain AI models due to imbalanced datasets. The high heterogeneity and the lack of standardized predictors limit the generalizability of findings across different populations. |
| format | Article |
| id | doaj-art-ccebbd5851cd43d590943b9da275fdf1 |
| institution | DOAJ |
| issn | 0886-022X 1525-6049 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Renal Failure |
| spelling | doaj-art-ccebbd5851cd43d590943b9da275fdf12025-08-20T03:21:31ZengTaylor & Francis GroupRenal Failure0886-022X1525-60492024-12-0146210.1080/0886022X.2024.2435483Artificial intelligence in predicting chronic kidney disease prognosis. A systematic review and meta-analysisQinyu Pan0Mengli Tong1Hangzhou TCM Hospital, Affiliated to Zhejiang Chinese Medical University, Hangzhou, ChinaHangzhou TCM Hospital, Affiliated to Zhejiang Chinese Medical University, Hangzhou, ChinaBackground Chronic kidney disease (CKD) is a common condition that can lead to serious health complications. Artificial Intelligence (AI) has shown the potential to improve the prediction of CKD progression, offering increased accuracy over traditional methods. Therefore, this systematic review and meta-analysis examine the diagnostic performance of various AI models in predicting CKD.Method Search was performed in different databases for studies reporting the diagnostic accuracy of AI-based prediction models for the progression of CKD. Meanwhile, pre-defined eligibility criteria were used for the selection of studies. Pooled sensitivity, specificity, and area under curve (AUC) were calculated utilizing Meta-disc 1.4. Quality assessment was performed using the prediction model risk of bias assessment tool (PROBAST).Results A total of 33 studies were included. The pooled sensitivity of prediction tools was 0.43 (95% CI, 0.41–0.44, I2 = 99.3%, p < 0.01). A significant difference (p < 0.01) was also observed in the pooled specificity 0.92 (95% CI, 0.91–0.92, I2 = 99.5%). Positive likelihood ratio (PLP) and negative likelihood ratio (NLR) were 5.12 (95% CI: 3.60–7.27, I2 = 91.3%, p < 0.01) and 0.28 (95% CI: 0.21–0.37, I2 = 99.3%, p < 0.01), respectively and AUC was 0.89, suggesting a diagnostic accuracy of AI-based prediction models for the progression of CKD.Conclusions This study demonstrates the promising potential of AI models in predicting CKD progression. However, further efforts are needed to optimize model performance, particularly in balancing sensitivity and specificity to ensure generalizability across diverse populations. Limitations of this study include the potential for overfitting in certain AI models due to imbalanced datasets. The high heterogeneity and the lack of standardized predictors limit the generalizability of findings across different populations.https://www.tandfonline.com/doi/10.1080/0886022X.2024.2435483NephrologyAI-based prediction modelsCKD progressionartificial neural network |
| spellingShingle | Qinyu Pan Mengli Tong Artificial intelligence in predicting chronic kidney disease prognosis. A systematic review and meta-analysis Renal Failure Nephrology AI-based prediction models CKD progression artificial neural network |
| title | Artificial intelligence in predicting chronic kidney disease prognosis. A systematic review and meta-analysis |
| title_full | Artificial intelligence in predicting chronic kidney disease prognosis. A systematic review and meta-analysis |
| title_fullStr | Artificial intelligence in predicting chronic kidney disease prognosis. A systematic review and meta-analysis |
| title_full_unstemmed | Artificial intelligence in predicting chronic kidney disease prognosis. A systematic review and meta-analysis |
| title_short | Artificial intelligence in predicting chronic kidney disease prognosis. A systematic review and meta-analysis |
| title_sort | artificial intelligence in predicting chronic kidney disease prognosis a systematic review and meta analysis |
| topic | Nephrology AI-based prediction models CKD progression artificial neural network |
| url | https://www.tandfonline.com/doi/10.1080/0886022X.2024.2435483 |
| work_keys_str_mv | AT qinyupan artificialintelligenceinpredictingchronickidneydiseaseprognosisasystematicreviewandmetaanalysis AT menglitong artificialintelligenceinpredictingchronickidneydiseaseprognosisasystematicreviewandmetaanalysis |