Assessing the Accuracy of an Artificial Intelligence-Based Segmentation Algorithm for the Thoracic Aorta in Computed Tomography Applications

Assessing the Accuracy of an Artificial Intelligence-Based Segmentation Algorithm for the Thoracic Aorta in Computed Tomography Applications

The aim was to evaluate the accuracy of a prototypical artificial intelligence-based algorithm for automated segmentation and diameter measurement of the thoracic aorta (TA) using CT. One hundred twenty-two patients who underwent dual-source CT were retrospectively included. Ninety-three of these pa...

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Bibliographic Details
Published in:Diagnostics (Basel) Vol. 12; no. 8; p. 1790
Main Authors: Artzner, Christoph, Bongers, Malte N, Kärgel, Rainer, Faby, Sebastian, Hefferman, Gerald, Herrmann, Judith, Nopper, Svenja L, Perl, Regine M, Walter, Sven S
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 23-07-2022
MDPI
Subjects:
Abdomen
Accuracy
Algorithms
Aorta, Thoracic
Aortic aneurysms
Artificial intelligence
Automation
Coronary vessels
CT imaging
Deep learning
dimensional measurement accuracy
Dissection
Electrocardiography
Health aspects
Image segmentation
Males
Medical examination
Medical imaging
Patients
Software
spiral computed tomography
Technology application
thoracic aorta
Tomography
Veins & arteries
Germany
spiral computed tomography
thoracic aorta
dimensional measurement accuracy
software
artificial intelligence
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Summary:The aim was to evaluate the accuracy of a prototypical artificial intelligence-based algorithm for automated segmentation and diameter measurement of the thoracic aorta (TA) using CT. One hundred twenty-two patients who underwent dual-source CT were retrospectively included. Ninety-three of these patients had been administered intravenous iodinated contrast. Images were evaluated using the prototypical algorithm, which segments the TA and determines the corresponding diameters at predefined anatomical locations based on the American Heart Association guidelines. The reference standard was established by two radiologists individually in a blinded, randomized fashion. Equivalency was tested and inter-reader agreement was assessed using intra-class correlation (ICC). In total, 99.2% of the parameters measured by the prototype were assessable. In nine patients, the prototype failed to determine one diameter along the vessel. Measurements along the TA did not differ between the algorithm and readers (p > 0.05), establishing equivalence. Inter-reader agreement between the algorithm and readers (ICC ≥ 0.961; 95% CI: 0.940−0.974), and between the readers was excellent (ICC ≥ 0.879; 95% CI: 0.818−0.92). The evaluated prototypical AI-based algorithm accurately measured TA diameters at each region of interest independent of the use of either contrast utilization or pathology. This indicates that the prototypical algorithm has substantial potential as a valuable tool in the rapid clinical evaluation of aortic pathology.
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ISSN:2075-4418
2075-4418
DOI:10.3390/diagnostics12081790