Segmentation of X‐ray coronary angiography with an artificial intelligence deep learning model: Impact in operator visual assessment of coronary stenosis severity
Miguel Nobre Menezes, Beatriz Silva, João Lourenço Silva, Tiago Rodrigues, João Silva Marques, Cláudio Guerreiro, João Pedro Guedes, Manuel Oliveira‐Santos, Arlindo L. Oliveira, Fausto J. Pinto- Cardiology and Cardiovascular Medicine
- Radiology, Nuclear Medicine and imaging
- General Medicine
Abstract
Background
Visual assessment of the percentage diameter stenosis (%DSVE) of lesions is essential in coronary angiography (CAG) interpretation. We have previously developed an artificial intelligence (AI) model capable of accurate CAG segmentation. We aim to compare operators’ %DSVE in angiography versus AI‐segmented images.
Methods
Quantitative coronary analysis (QCA) %DS (%DSQCA) was previously performed in our published validation dataset. Operators were asked to estimate %DSVE of lesions in angiography versus AI‐segmented images in separate sessions and differences were assessed using angiography %DSQCA as reference.
Results
A total of 123 lesions were included. %DSVE was significantly higher in both the angiography (77% ± 20% vs. 56% ± 13%, p < 0.001) and segmentation groups (59% ± 20% vs. 56% ± 13%, p < 0.001), with a much smaller absolute %DS difference in the latter. For lesions with %DSQCA of 50%–70% (60% ± 5%), an even higher discrepancy was found (angiography: 83% ± 13% vs. 60% ± 5%, p < 0.001; segmentation: 63% ± 15% vs. 60% ± 5%, p < 0.001). Similar, less pronounced, findings were observed for %DSQCA < 50% lesions, but not %DSQCA > 70% lesions. Agreement between %DSQCA/%DSVE across %DSQCA strata (<50%, 50%–70%, >70%) was approximately twice in the segmentation group (60.4% vs. 30.1%; p < 0.001). %DSVE inter‐operator differences were smaller with segmentation.
Conclusion
%DSVE was much less discrepant with segmentation versus angiography. Overestimation of %DSQCA < 70% lesions with angiography was especially common. Segmentation may reduce %DSVE overestimation and thus unwarranted revascularization.