Coronary artery disease evaluation during transcatheter aortic valve replacement work-up using photon-counting CT and artificial intelligence.
Diagn Interv Imaging
; 105(7-8): 273-280, 2024.
Article
em En
| MEDLINE
| ID: mdl-38368176
ABSTRACT
PURPOSE:
The purpose of this study was to evaluate the capabilities of photon-counting (PC) CT combined with artificial intelligence-derived coronary computed tomography angiography (PC-CCTA) stenosis quantification and fractional flow reserve prediction (FFRai) for the assessment of coronary artery disease (CAD) in transcatheter aortic valve replacement (TAVR) work-up. MATERIALS ANDMETHODS:
Consecutive patients with severe symptomatic aortic valve stenosis referred for pre-TAVR work-up between October 2021 and June 2023 were included in this retrospective tertiary single-center study. All patients underwent both PC-CCTA and ICA within three months for reference standard diagnosis. PC-CCTA stenosis quantification (at 50% level) and FFRai (at 0.8 level) were predicted using two deep learning models (CorEx, Spimed-AI). Diagnostic performance for global CAD evaluation (at least one significant stenosis ≥ 50% or FFRai ≤ 0.8) was assessed.RESULTS:
A total of 260 patients (138 men, 122 women) with a mean age of 78.7 ± 8.1 (standard deviation) years (age range 51-93 years) were evaluated. Significant CAD on ICA was present in 126/260 patients (48.5%). Per-patient sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were 96.0% (95% confidence interval [CI] 91.0-98.7), 68.7% (95% CI 60.1-76.4), 74.3 % (95% CI 69.1-78.8), 94.8% (95% CI 88.5-97.8), and 81.9% (95% CI 76.7-86.4) for PC-CCTA, and 96.8% (95% CI 92.1-99.1), 87.3% (95% CI 80.5-92.4), 87.8% (95% CI 82.2-91.8), 96.7% (95% CI 91.7-98.7), and 91.9% (95% CI 87.9-94.9) for FFRai. Area under the curve of FFRai was 0.92 (95% CI 0.88-0.95) compared to 0.82 for PC-CCTA (95% CI 0.77-0.87) (P < 0.001). FFRai-guidance could have prevented the need for ICA in 121 out of 260 patients (46.5%) vs. 97 out of 260 (37.3%) using PC-CCTA alone (P < 0.001).CONCLUSION:
Deep learning-based photon-counting FFRai evaluation improves the accuracy of PC-CCTA ≥ 50% stenosis detection, reduces the need for ICA, and may be incorporated into the clinical TAVR work-up for the assessment of CAD.Palavras-chave
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Base de dados:
MEDLINE
Assunto principal:
Estenose da Valva Aórtica
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Doença da Artéria Coronariana
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Inteligência Artificial
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Substituição da Valva Aórtica Transcateter
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Angiografia por Tomografia Computadorizada
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article