Prediction and Diagnosis of Venous Thromboembolism Using Artificial Intelligence Approaches: A Systematic Review and Meta-Analysis.
Prediction and Diagnosis of Venous Thromboembolism Using Artificial Intelligence Approaches: A Systematic Review and Meta-Analysis.
复制标题
DOI：
10.1177/10760296211021162
复制
发表时间：
2021-01
期刊：
影响因子：
--
通讯作者：
Zhou Z
中科院分区：
文献类型：
--
作者：
Wang Q;Yuan L;Ding X;Zhou Z
静脉血栓栓塞（VTE）是一种致命疾病，已成为全球卫生系统的负担。最近的研究表明，人工智能（AI）可用于更准确地诊断和预测静脉血栓形成。因此，我们进行了荟萃分析，以更好地评价AI在预测和诊断静脉血栓形成方面的性能。PubMed、Web of Science和EMBASE用于识别相关研究。在741项研究中，12项符合纳入标准，并被纳入荟萃分析。其中，5项研究包括训练集和测试集，7项研究仅包括训练集。在训练集中，合并灵敏度为0.87（95% CI 0.79-0.92），合并特异性为0.95（95% CI 0.89-0.97），汇总受试者工作特征（SROC）曲线下面积为0.97（95% CI 0.95-0.98）。在测试集中，合并灵敏度为0.87（95% CI 0.74-0.93），合并特异性为0.96（95% CI 0.79-0.99），SROC曲线下面积为0.98（95% CI 0.97-0.99）。在亚组分析中，包括静脉血栓形成类型、AI类型、模型类型（诊断/预测）以及是否为围手术期，组合结果仍具有显著性。总之，AI可以帮助诊断和预测静脉血栓形成，表现出高灵敏度，特异性和SROC曲线下面积值。因此，AI具有重要的临床应用价值。
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.
登录
查看更多内容
影响因子：
64.5
作者：
Pan C;Schoppe O;Parra-Damas A;Cai R;Todorov MI;Gondi G;von Neubeck B;Böğürcü-Seidel N;Seidel S;Sleiman K;Veltkamp C;Förstera B;Mai H;Rong Z;Trompak O;Ghasemigharagoz A;Reimer MA;Cuesta AM;Coronel J;Jeremias I;Saur D;Acker-Palmer A;Acker T;Garvalov BK;Menze B;Zeidler R;Ertürk A
通讯作者：
Ertürk A
影响因子：
168.9
作者：
Attia, Zachi, I;Noseworthy, Peter A.;Friedman, Paul A.
通讯作者：
Friedman, Paul A.
影响因子：
1.5
作者：
Liu, Kun;Chen, Jun;Li, Xiaoqiang
通讯作者：
Li, Xiaoqiang
DOI：
10.1136/amiajnl-2014-002768
发表时间：
2015-01-01
影响因子：
6.4
作者：
Rochefort, Christian M.;Verma, Aman D.;Buckeridge, David L.
通讯作者：
Buckeridge, David L.
影响因子：
3
作者：
Tian Z;Sun S;Eguale T;Rochefort CM
通讯作者：
Rochefort CM