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Automated detection of pulmonary embolism in CT pulmonary angiograms using an AI-powered algorithm



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.


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.


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.


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.

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Advanced Modeled Iterative Reconstruction


Computer-assisted detection


CT pulmonary angiogram


Deep convolutional neural network


Digital imaging and communications in medicine


False negative


False positive


File transfer protocol


Iterative reconstruction


Negative predictive value


Picture Archiving and Communication System


Pulmonary embolism


Postgraduate year


Positive predictive value


Sinogram Affirmed Iterative Reconstruction


Sample weighted categorical cross-entropy


True positive


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Correspondence to Thomas Weikert.

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The scientific guarantor of this publication is Gregor Sommer, MD, PhD (University Hospital Basel, Switzerland).

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The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

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Written informed consent was waived by the Institutional Review Board (project number 2019-01050). We added the document of the IRB decision to this submission.

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Weikert, T., Winkel, D.J., Bremerich, J. et al. Automated detection of pulmonary embolism in CT pulmonary angiograms using an AI-powered algorithm. Eur Radiol 30, 6545–6553 (2020).

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  • Pulmonary embolism
  • Computed tomography angiography
  • Artificial intelligence
  • Computer-assisted image processing