OBJECTIVES:To evaluate the performance of an AI-powered algorithm for the automatic detection of pulmonary embolism (PE) on chest computed tomography pulmonary angiograms (CTPAs) on a large dataset.

METHODS:We retrospectively identified all CTPAs conducted at our institution in 2017 (n = 1499). Exams with clinical questions other than PE were excluded from the analysis (n = 34). The remaining exams were classified into positive (n = 232) and negative (n = 1233) for PE based on the final written reports, which defined the reference standard. The fully anonymized 1-mm series in soft tissue reconstruction served as input for the PE detection prototype algorithm that was based on a deep convolutional neural network comprising a Resnet architecture. It was trained and validated on 28,000 CTPAs acquired at other institutions. The result series were reviewed using a web-based feedback platform. Measures of diagnostic performance were calculated on a per patient and a per finding level.

RESULTS:The algorithm correctly identified 215 of 232 exams positive for pulmonary embolism (sensitivity 92.7%; 95% confidence interval [CI] 88.3-95.5%) and 1178 of 1233 exams negative for pulmonary embolism (specificity 95.5%; 95% CI 94.2-96.6%). On a per finding level, 1174 of 1352 findings marked as embolus by the algorithm were true emboli. Most of the false positive findings were due to contrast agent-related flow artifacts, pulmonary veins, and lymph nodes.

CONCLUSION:The AI prototype algorithm we tested has a high degree of diagnostic accuracy for the detection of PE on CTPAs. Sensitivity and specificity are balanced, which is a prerequisite for its clinical usefulness.

KEY POINTS:• An AI-based prototype algorithm showed a high degree of diagnostic accuracy for the detection of pulmonary embolism on CTPAs. • It can therefore help clinicians to automatically prioritize exams with a high suspection of pulmonary embolism and serve as secondary reading tool. • By complementing traditional ways of worklist prioritization in radiology departments, this can speed up the diagnostic and therapeutic workup of patients with pulmonary embolism and help to avoid false negative calls.

利用人工智能算法自动检测CT肺动脉造影中的肺动脉栓塞

目的：在大数据集上评价一种基于人工智能的肺栓塞(PE)自动检测算法在胸部CT肺动脉造影(CTPA)上的性能。

方法：我们回顾性地确定了2017年在我所进行的所有CTPA(n=1499)。
有临床问题而不是肺栓塞的检讨被打消在剖析之外(n=34)。
根据终极书面报告将剩余的肺栓塞检讨分为阳性(n=232)和阴性(n=1233)，确定参考标准。
软组织重修中完备匿名的1-mm序列作为PE检测原型算法的输入，该PE检测原型算法基于包括RESNET架构的深度卷积神经网络。
它在其他机构得到的28,000个CTPA上进行了培训和验证。
利用基于网络的反馈平台对结果系列进行审查。
诊断性能的丈量是在每个患者和每个创造的水平上打算的。

结果：该算法精确识别出232例肺栓塞阳性患者中的215例(敏感性为92.7%，95%可信区间为88.3~95.5%)和1233例阴性患者中的1178例(特异性为95.5%，95%可信区间为94.2-96.6%)。
在每个创造的水平上，该算法标记为栓子的1352个创造中有1174个是真正的栓子。
大多数假阳性的创造是由于造影剂干系的血流伪影、肺静脉和淋巴结。

结论：我们测试的人工智能原型算法对CTPA上PE的检测具有较高的诊断准确率。
敏感度和特异度是平衡的，这是其临床有效性的条件。

要点：·基于人工智能的原型算法对CTPA上肺栓塞的检测具有较高的诊断准确率。
·因此，它可以帮助临床年夜夫自动对高度疑惑肺栓塞的检讨进行优先排序，并作为赞助阅读工具。
·通过补充放射科事情列表优先顺序的传统方法，这可以加快肺栓塞患者的诊断和治疗事情，并有助于避免假阴性呼叫。