Internal Medicine B, Assuta Medical Center, Samson Assuta Ashdod University Hospital, Ashdod, Israel.
Ben-Gurion University of the Negev, Be'er Sheva, Israel.
Sci Rep. 2021 Aug 4;11(1):15814. doi: 10.1038/s41598-021-95249-3.
Computed tomographic pulmonary angiography (CTPA) is the gold standard for pulmonary embolism (PE) diagnosis. However, this diagnosis is susceptible to misdiagnosis. In this study, we aimed to perform a systematic review of current literature applying deep learning for the diagnosis of PE on CTPA. MEDLINE/PUBMED were searched for studies that reported on the accuracy of deep learning algorithms for PE on CTPA. The risk of bias was evaluated using the QUADAS-2 tool. Pooled sensitivity and specificity were calculated. Summary receiver operating characteristic curves were plotted. Seven studies met our inclusion criteria. A total of 36,847 CTPA studies were analyzed. All studies were retrospective. Five studies provided enough data to calculate summary estimates. The pooled sensitivity and specificity for PE detection were 0.88 (95% CI 0.803-0.927) and 0.86 (95% CI 0.756-0.924), respectively. Most studies had a high risk of bias. Our study suggests that deep learning models can detect PE on CTPA with satisfactory sensitivity and an acceptable number of false positive cases. Yet, these are only preliminary retrospective works, indicating the need for future research to determine the clinical impact of automated PE detection on patient care. Deep learning models are gradually being implemented in hospital systems, and it is important to understand the strengths and limitations of these algorithms.
计算机断层扫描肺动脉造影（CTPA）是肺栓塞（PE）诊断的金标准。然而，这种诊断容易出现误诊。在这项研究中，我们旨在对目前应用深度学习技术诊断 CTPA 中 PE 的文献进行系统回顾。我们在 MEDLINE/PUBMED 上搜索了报告 CTPA 上深度学习算法对 PE 准确性的研究。使用 QUADAS-2 工具评估偏倚风险。计算汇总敏感性和特异性。绘制汇总受试者工作特征曲线。符合纳入标准的研究有 7 项。共分析了 36847 项 CTPA 研究。所有研究均为回顾性。有 5 项研究提供了足够的数据来计算汇总估计值。PE 检测的汇总敏感性和特异性分别为 0.88（95%CI 0.803-0.927）和 0.86（95%CI 0.756-0.924）。大多数研究存在高偏倚风险。我们的研究表明，深度学习模型可以用令人满意的敏感性和可接受的假阳性病例来检测 CTPA 中的 PE。然而，这些只是初步的回顾性研究，表明需要进一步研究来确定自动 PE 检测对患者护理的临床影响。深度学习模型正在逐渐在医院系统中实施，了解这些算法的优势和局限性非常重要。
