Key result
Artificial intelligence models demonstrated high diagnostic accuracy for venous thromboembolism, with a pooled sensitivity of 0.87, specificity of 0.96, and an area under the SROC curve of 0.98 in the test sets.
Why the study?
Venous thromboembolism is a fatal disease, and recent studies suggest artificial intelligence could improve diagnosis and prediction accuracy, prompting a meta-analysis to evaluate its performance.
Does artificial intelligence accurately predict and diagnose venous thromboembolism?
Meta-Analysis (n=51,383)
Does artificial intelligence accurately predict and diagnose venous thromboembolism?
Effect estimate: AUC 0.98 (95% CI 0.97-0.99)
Artificial intelligence models show high accuracy for predicting and diagnosing venous thromboembolism, suggesting potential clinical utility.
Venous thromboembolism (VTE) is a fatal disease and has become a burden on the global health system. Recent studies have suggested that artificial intelligence (AI) could be used to make a diagnosis and predict venous thrombosis more accurately. Thus, we performed a meta-analysis to better evaluate the performance of AI in the prediction and diagnosis of venous thrombosis. PubMed, Web of Science, and EMBASE were used to identify relevant studies. Of the 741 studies, 12 met the inclusion criteria and were included in the meta-analysis. Among them, 5 studies included a training set and test set, and 7 studies included only a training set. In the training set, the pooled sensitivity was 0.87 (95% CI 0.79-0.92), the pooled specificity was 0.95 (95% CI 0.89-0.97), and the area under the summary receiver operating characteristic (SROC) curve was 0.97 (95% CI 0.95-0.98). In the test set, the pooled sensitivity was 0.87 (95% CI 0.74-0.93), the pooled specificity was 0.96 (95% CI 0.79-0.99), and the area under the SROC curve was 0.98 (95% CI 0.97-0.99). The combined results remained significant in the subgroup analyzes, which included venous thrombosis type, AI type, model type (diagnosis/prediction), and whether the period was perioperative. In conclusion, AI may aid in the diagnosis and prediction of venous thrombosis, demonstrating high sensitivity, specificity and area under the SROC curve values. Thus, AI has important clinical value.
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Wang et al. (2021) conducted a meta-analysis in Venous thromboembolism (VTE) (n=51,383). Artificial Intelligence (AI) models was evaluated on Area under the summary receiver operating characteristic (SROC) curve in the test set (AUC 0.98, 95% CI 0.97-0.99). Artificial intelligence models demonstrated high diagnostic accuracy for venous thromboembolism, with a pooled sensitivity of 0.87, specificity of 0.96, and an area under the SROC curve of 0.98 in the test sets.
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