Enfermedades cardiovascularesscreening de la población general

  1. X. Múgica 2
  2. S. Gómez Sánchez 3
  3. R. Cilla 2
  4. S. Banderas García 3
  5. R. Muñoz 4
  6. X. Gràcia Aloy 4
  7. MI. Martínez Segura 1
  8. A. Sánchez-Fortún Sánchez 3
  9. MÀ. Pouplana Sardà 3
  10. P. Campos Figueroa 3
  11. R. Bouchikh El Jarroudi 3
  12. A. Garcés 3
  13. A. Sabala Llopart 3
  14. F. Ortuño 4
  15. I. Besada 2
  16. JA. de Frutos 2
  17. O. Estrada Cuxart 5
  18. E. Isusquiza 2
  19. S. Ruiz Bilbao 3
  1. 1 Institut d'Investigació en Ciències de la Salut Germans Trias i Pujol
    info

    Institut d'Investigació en Ciències de la Salut Germans Trias i Pujol

    Barcelona, España

    ROR https://ror.org/03bzdww12

  2. 2 ULMA Medical Technologies. Departamento de Desarrollo. Oñati, Gipuzkoa.
  3. 3 Servicio de Oftalmología. Hospital Universitari Germans Trias i Pujol (HUGTiP). Badalona. Barcelona.
  4. 4 Dirección de Sistemas de Información. HUGTiP . Badalona. Barcelona.
  5. 5 Direcció d’Estratègia Assistencial i d’Innovació. HUGTiP. Badalona. Barcelona
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Zeitschrift:
Annals d'oftalmologia: òrgan de les Societats d'Oftalmologia de Catalunya, Valencia i Balears
  1. Romero Aroca, Pere
  2. Zapata Victori, Miguel Ángel
  3. Zarranz Ventura, Javier

ISSN: 1133-7737

Datum der Publikation: 2023

Titel der Ausgabe: INTELIGENCIA ARTIFICIAL Y OFTALMOLOGÍA: ESTADO ACTUAL EN CATALUÑA

Ausgabe: 31

Nummer: 4

Art: Artikel

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Andere Publikationen in: Annals d'oftalmologia: òrgan de les Societats d'Oftalmologia de Catalunya, Valencia i Balears

Zusammenfassung

Macular pathologies affect the fovea, the most important area of the retina for visual function. New retinal imaging techniques, such as optical coherence tomography (OCT) and retinography, allow direct, non-invasive, high-resolution visualization of the retinal layers. The aim of the artificial intelligence and macular optical coherence tomography (OCT) project AI4Ret (PI-20-113) is the creation of an artificial intelligence (AI) software based on deep learning (DL) to identify biomarkers for cardiovascular risk prediction from macular OCT images. AI4Ret has curated a dataset of macula-centered OCT images from the overall population attended by the ophthalmology department of the Hospital Universitario Germans Trias i Pujol (HUGTiP). Then, a deep learning model has been trained to identify specific biomarkers of arterial hypertension (AHT), type 2 diabetes mellitus (DM2), and dyslipidemia in OCT images of patients without macular pathology. These factors could be a predictor for an additional risk from suffering a cardiovascular event. Currently, the model achieves a sensitivity and specificity of 84% and 69% for AHT, 86% and 67% for DM2, and 74% and 78% for dyslipidemia. Thus, patients without macular pathology present retinal alterations that the AI model detects. Their presence could mean a higher risk of metabolic alterations and a higher probability of suffering a cardiovascular event.

Fuente de los datos: Dialnet