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Co-registered optical coherence tomography and X-ray angiography for the prediction of fractional flow reserve.
Hatfaludi, Cosmin-Andrei; Tache, Irina-Andra; Ciusdel, Costin-Florian; Puiu, Andrei; Stoian, Diana; Calmac, Lucian; Popa-Fotea, Nicoleta-Monica; Bataila, Vlad; Scafa-Udriste, Alexandru; Itu, Lucian Mihai.
Afiliación
  • Hatfaludi CA; Advanta, Siemens SRL, 15 Noiembrie Bvd, Brasov, 500097, Romania. cosmin.hatfaludi@unitbv.ro.
  • Tache IA; Automation and Information Technology, Transilvania University of Brasov, Mihai Viteazu nr. 5, Brasov, 5000174, Romania. cosmin.hatfaludi@unitbv.ro.
  • Ciusdel CF; Advanta, Siemens SRL, 15 Noiembrie Bvd, Brasov, 500097, Romania.
  • Puiu A; Department of Automatic Control and Systems Engineering, University Politehnica of Bucharest, Bucharest, 014461, Romania.
  • Stoian D; Advanta, Siemens SRL, 15 Noiembrie Bvd, Brasov, 500097, Romania.
  • Calmac L; Automation and Information Technology, Transilvania University of Brasov, Mihai Viteazu nr. 5, Brasov, 5000174, Romania.
  • Popa-Fotea NM; Advanta, Siemens SRL, 15 Noiembrie Bvd, Brasov, 500097, Romania.
  • Bataila V; Automation and Information Technology, Transilvania University of Brasov, Mihai Viteazu nr. 5, Brasov, 5000174, Romania.
  • Scafa-Udriste A; Advanta, Siemens SRL, 15 Noiembrie Bvd, Brasov, 500097, Romania.
  • Itu LM; Automation and Information Technology, Transilvania University of Brasov, Mihai Viteazu nr. 5, Brasov, 5000174, Romania.
Int J Cardiovasc Imaging ; 40(5): 1029-1039, 2024 May.
Article en En | MEDLINE | ID: mdl-38376719
ABSTRACT
Cardiovascular disease (CVD) stands as the leading global cause of mortality, and coronary artery disease (CAD) has the highest prevalence, contributing to 42% of these fatalities. Recognizing the constraints inherent in the anatomical assessment of CAD, Fractional Flow Reserve (FFR) has emerged as a pivotal functional diagnostic metric. Herein, we assess the potential of employing an ensemble approach with deep neural networks (DNN) to predict invasively measured Fractional Flow Reserve (FFR) using raw anatomical data extracted from both optical coherence tomography (OCT) and X-ray coronary angiography (XA). In this study, we used a challenging dataset, with 46% of the lesions falling within the FFR range of 0.75 to 0.85. Despite this complexity, our model achieved an accuracy of 84.3%, demonstrating a sensitivity of 87.5% and a specificity of 81.4%. Our results demonstrate that incorporating both OCT and XA signals, co-registered, as inputs for the DNN model leads to an important increase in overall accuracy.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Enfermedad de la Arteria Coronaria / Valor Predictivo de las Pruebas / Angiografía Coronaria / Vasos Coronarios / Tomografía de Coherencia Óptica / Reserva del Flujo Fraccional Miocárdico Límite: Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Int J Cardiovasc Imaging Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2024 Tipo del documento: Article País de afiliación: Rumanía Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Enfermedad de la Arteria Coronaria / Valor Predictivo de las Pruebas / Angiografía Coronaria / Vasos Coronarios / Tomografía de Coherencia Óptica / Reserva del Flujo Fraccional Miocárdico Límite: Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Int J Cardiovasc Imaging Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2024 Tipo del documento: Article País de afiliación: Rumanía Pais de publicación: Estados Unidos