Singh Gagandeep, Singh Annie, Kainth Tejasvi, Suman Sudhir, Sakla Nicole, Partyka Luke, Phatak Tej, Prasanna Prateek
Department of Radiology, Columbia University Irving Medical Center, NY, USA.
Atal Bihari Vajpayee Institute of Medical Sciences, New Delhi, India.
Eur J Radiol Open. 2025 May 9;14:100657. doi: 10.1016/j.ejro.2025.100657. eCollection 2025 Jun.
Pulmonary embolism (PE) is the third most fatal cardiovascular disease in the United States. Currently, Computed Tomography Pulmonary Angiography (CTPA) serves as diagnostic gold standard for detecting PE. However, its efficacy is limited by factors such as contrast bolus timing, physician-dependent diagnostic accuracy, and time taken for scan interpretation. To address these limitations, we propose an AI-based PE triaging model (AID-PE) designed to predict the presence and key characteristics of PE on CTPA. This model aims to enhance diagnostic accuracy, efficiency, and the speed of PE identification.
We trained AID-PE on the RSNA-STR PE CT (RSPECT) Dataset, N = 7279 and subsequently tested it on an in-house dataset (n = 106). We evaluated efficiency in a separate dataset (D, n = 200) by comparing the time from scan to report in standard PE detection workflow versus AID-PE.
A comparative analysis showed that AID-PE had an AUC/accuracy of 0.95/0.88. In contrast, a Convolutional Neural Network (CNN) classifier and a CNN-Long Short-Term Memory (LSTM) network without an attention module had an AUC/accuracy of 0.5/0.74 and 0.88/0.65, respectively. Our model achieved AUCs of 0.82 and 0.95 for detecting PE on the validation dataset and the independent test set, respectively. On D, AID-PE took an average of 1.32 s to screen for PE across 148 CTPA studies, compared to an average of 40 min in contemporary workflow.
AID-PE outperformed a baseline CNN classifier and a single-stage CNN-LSTM network without an attention module. Additionally, its efficiency is comparable to the current radiological workflow.
肺栓塞（PE）是美国第三大致命性心血管疾病。目前，计算机断层扫描肺动脉造影（CTPA）是检测PE的诊断金标准。然而，其有效性受到诸如对比剂团注时间、依赖医生的诊断准确性以及扫描解读所需时间等因素的限制。为解决这些局限性，我们提出了一种基于人工智能的PE分诊模型（AID-PE），旨在预测CTPA上PE的存在及关键特征。该模型旨在提高PE诊断的准确性、效率和识别速度。
我们在RSNA-STR PE CT（RSPECT）数据集（N = 7279）上训练AID-PE，随后在内部数据集（n = 106）上对其进行测试。我们通过比较标准PE检测工作流程与AID-PE从扫描到报告的时间，在另一个单独的数据集（D，n = 200）中评估效率。
对比分析表明，AID-PE的AUC/准确率为0.95/0.88。相比之下，一个卷积神经网络（CNN）分类器和一个没有注意力模块的CNN-长短期记忆（LSTM）网络的AUC/准确率分别为0.5/0.74和0.88/0.65。我们的模型在验证数据集和独立测试集上检测PE的AUC分别为0.82和0.95。在数据集D上，AID-PE在148项CTPA研究中平均花费1.32秒筛查PE，而当代工作流程平均需要40分钟。
AID-PE优于基线CNN分类器和没有注意力模块的单阶段CNN-LSTM网络。此外，其效率与当前的放射学工作流程相当。
