Segmentation of X-ray coronary angiography with an artificial intelligence deep learning model: Impact in operator visual assessment of coronary stenosis severity.

Autor:	Nobre Menezes M; Structural and Coronary Heart Disease Unit, Cardiovascular Center of the University of Lisbon, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal.; Departamento de Coração e Vasos, Serviço de Cardiologia, CHULN Hospital de Santa Maria, Lisboa, Portugal., Silva B; Structural and Coronary Heart Disease Unit, Cardiovascular Center of the University of Lisbon, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal.; Departamento de Coração e Vasos, Serviço de Cardiologia, CHULN Hospital de Santa Maria, Lisboa, Portugal., Silva JL; INESC-ID/Instituto Superior Técnico, Lisbon, Portugal., Rodrigues T; Structural and Coronary Heart Disease Unit, Cardiovascular Center of the University of Lisbon, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal.; Departamento de Coração e Vasos, Serviço de Cardiologia, CHULN Hospital de Santa Maria, Lisboa, Portugal., Marques JS; Structural and Coronary Heart Disease Unit, Cardiovascular Center of the University of Lisbon, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal.; Departamento de Coração e Vasos, Serviço de Cardiologia, CHULN Hospital de Santa Maria, Lisboa, Portugal., Guerreiro C; Department of Cardiology, Centro Hospitalar de Vila Nova de Gaia, Vila Nova de Gaia, Portugal., Guedes JP; Unidade de Hemodinâmica e Cardiologia de Intervenção, Serviço de Cardiologia, Centro Hospitalar Universitário do Algarve, Hospital de Faro, Faro, Portugal., Oliveira-Santos M; Unidade de Intervenção Cardiovascular, Serviço de Cardiologia do Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal.; Pólo das Ciências da Saúde, Unidade Central, Azinhaga de Santa Comba, Celas, Faculty of Medicine, University of Coimbra, Coimbra, Portugal., Oliveira AL; INESC-ID/Instituto Superior Técnico, Lisbon, Portugal., Pinto FJ; Structural and Coronary Heart Disease Unit, Cardiovascular Center of the University of Lisbon, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal.; Departamento de Coração e Vasos, Serviço de Cardiologia, CHULN Hospital de Santa Maria, Lisboa, Portugal.
Jazyk:	angličtina
Zdroj:	Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions [Catheter Cardiovasc Interv] 2023 Oct; Vol. 102 (4), pp. 631-640. Date of Electronic Publication: 2023 Aug 14.
DOI:	10.1002/ccd.30805
Abstrakt:	Background: Visual assessment of the percentage diameter stenosis (%DS VE ) 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' %DS VE in angiography versus AI-segmented images. Methods: Quantitative coronary analysis (QCA) %DS (%DS QCA ) was previously performed in our published validation dataset. Operators were asked to estimate %DS VE of lesions in angiography versus AI-segmented images in separate sessions and differences were assessed using angiography %DS QCA as reference. Results: A total of 123 lesions were included. %DS VE 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 %DS QCA 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 %DS QCA  < 50% lesions, but not %DS QCA  > 70% lesions. Agreement between %DS QCA /%DS VE across %DS QCA strata (<50%, 50%-70%, >70%) was approximately twice in the segmentation group (60.4% vs. 30.1%; p < 0.001). %DS VE inter-operator differences were smaller with segmentation. Conclusion: %DS VE was much less discrepant with segmentation versus angiography. Overestimation of %DS QCA  < 70% lesions with angiography was especially common. Segmentation may reduce %DS VE overestimation and thus unwarranted revascularization. (© 2023 Wiley Periodicals LLC.)
Databáze:	MEDLINE
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