Improved detection of small pulmonary embolism on unenhanced computed tomography using an artificial intelligence-based algorithm – a single centre retrospective study

Hagen F, Vorberg L, Thamm F, Ditt H, Maier A, Brendel JM, Ghibes P, Bongers MN, Krumm P, Nikolaou K, Horger M (2024)

Publication Type: Journal article

Publication year: 2024

Journal

International Journal of Cardiovascular Imaging Springer Verlag (Germany)

DOI: 10.1007/s10554-024-03222-8

Abstract

To preliminarily verify the feasibility of a deep-learning (DL) artificial intelligence (AI) model to localize pulmonary embolism (PE) on unenhanced chest-CT by comparison with pulmonary artery (PA) CT angiography (CTA). In a monocentric study, we retrospectively reviewed 99 oncological patients (median age in years: 64 (range: 28–92 years); percentage of female: 39.4%) who received unenhanced and contrast-enhanced chest CT examinations in one session between January 2020 and October 2022 and who were diagnosed incidentally with PE. Findings in the unenhanced images were correlated with the contrast-enhanced images, which were considered the gold standard for central, segmental and subsegmental PE. The new algorithm was trained and tested based on the 99 unenhanced chest-CT image data sets. Based on them, candidate boxes, which were output by the model, were post-processed by evaluating whether the predicted box intersects with the patient’s lung segmentation at any position. The AI-based algorithm proved to have an overall sensitivity of 54.5% for central, of 81.9% for segmental and 80.0% for subsegmental PE if taking n = 20 candidate boxes into account. Depending on the localization of the pulmonary embolism, the detection rate for only one box was: 18.1% central, 34.7% segmental and 0.0% subsegmental. The median volume of the clots differed significantly between the three subgroups and was 846.5 mm3 (IQR:591.1-964.8) in central, 201.3 mm3 (IQR:98.3-390.9) in segmental and 110.6 mm3 (IQR:94.3–128.0) in subsegmental PA (p < 0.05). The new algorithm proved to have high sensitivity in detecting PE in particular in segmental/subsegmental localization and may guide to decide whether a second contrast enhanced CT is necessary.

Authors with CRIS profile

Linda Vorberg Lehrstuhl für Informatik 5 (Mustererkennung) Florian Thamm Lehrstuhl für Informatik 5 (Mustererkennung) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

Involved external institutions

Eberhard Karls Universität Tübingen DE Germany (DE)

How to cite

APA:

Hagen, F., Vorberg, L., Thamm, F., Ditt, H., Maier, A., Brendel, J.M.,... Horger, M. (2024). Improved detection of small pulmonary embolism on unenhanced computed tomography using an artificial intelligence-based algorithm – a single centre retrospective study. International Journal of Cardiovascular Imaging. https://doi.org/10.1007/s10554-024-03222-8

MLA:

Hagen, Florian, et al. "Improved detection of small pulmonary embolism on unenhanced computed tomography using an artificial intelligence-based algorithm – a single centre retrospective study." International Journal of Cardiovascular Imaging (2024).

BibTeX: Download