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Referable Diabetic Retinopathy Prediction Algorithm Applied to a Population of 120,389 Type 2 Diabetics over 11 Years Follow-Up

  • Datos identificativos

    Identificador: imarina:9366227
    Autores:
    Romero-Aroca, PedroVerges, RaquelPascual-Fontanilles, JordiValls, AidaFranch-Nadal, JosepMundet, XavierMoreno, AntonioBasora, JosepGarcia-Curto, EugeniBaget-Bernaldiz, Marc
    Resumen:
    (1) Background: Although DR screening is effective, one of its most significant problems is a lack of attendance. The aim of the present study was to demonstrate the effectiveness of our algorithm in predicting the development of any type of DR and referable DR. (2) Methods: A retrospective study with an 11-year follow-up of a population of 120,389 T2DM patients was undertaken. (3) Results: Applying the results of the algorithm showed an AUC of 0.93 (95% CI, 0.92-0.94) for any DR and 0.90 (95% CI, 0.89-0.91) for referable DR. Therefore, we achieved a promising level of agreement when applying our algorithm. (4) Conclusions: The algorithm is useful for predicting which patients may develop referable forms of DR and also any type of DR. This would allow a personalized screening plan to be drawn up for each patient.
  • Otros:

    Autor según el artículo: Romero-Aroca, Pedro; Verges, Raquel; Pascual-Fontanilles, Jordi; Valls, Aida; Franch-Nadal, Josep; Mundet, Xavier; Moreno, Antonio; Basora, Josep; Garcia-Curto, Eugeni; Baget-Bernaldiz, Marc
    Departamento: Enginyeria Informàtica i Matemàtiques
    Autor/es de la URV: Moreno Ribas, Antonio / Pascual Fontanilles, Jordi / Romero Aroca, Pedro / Valls Mateu, Aïda / Vergés Pujol, Raquel
    Palabras clave: Validation Specificity Sensitivity Screening frequency Risk-assessment Optimization Model Microaneurysm turnover Interval Diabetic retinopathy Artificial intelligence Algorithm
    Resumen: (1) Background: Although DR screening is effective, one of its most significant problems is a lack of attendance. The aim of the present study was to demonstrate the effectiveness of our algorithm in predicting the development of any type of DR and referable DR. (2) Methods: A retrospective study with an 11-year follow-up of a population of 120,389 T2DM patients was undertaken. (3) Results: Applying the results of the algorithm showed an AUC of 0.93 (95% CI, 0.92-0.94) for any DR and 0.90 (95% CI, 0.89-0.91) for referable DR. Therefore, we achieved a promising level of agreement when applying our algorithm. (4) Conclusions: The algorithm is useful for predicting which patients may develop referable forms of DR and also any type of DR. This would allow a personalized screening plan to be drawn up for each patient.
    Áreas temáticas: Medicine, general & internal Internal medicine Clinical biochemistry
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: raquel.vergesp@estudiants.urv.cat jordi.pascual@urv.cat jordi.pascual@urv.cat antonio.moreno@urv.cat pedro.romero@urv.cat aida.valls@urv.cat
    Identificador del autor: 0000-0003-2693-0612 0000-0002-7528-5819 0000-0002-7528-5819 0000-0003-3945-2314 0000-0002-7061-8987 0000-0003-3616-7809
    Fecha de alta del registro: 2024-10-12
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://www.mdpi.com/2075-4418/14/8/833
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referencia al articulo segun fuente origial: Diagnostics. 14 (8): 833-
    Referencia de l'ítem segons les normes APA: Romero-Aroca, Pedro; Verges, Raquel; Pascual-Fontanilles, Jordi; Valls, Aida; Franch-Nadal, Josep; Mundet, Xavier; Moreno, Antonio; Basora, Josep; Gar (2024). Referable Diabetic Retinopathy Prediction Algorithm Applied to a Population of 120,389 Type 2 Diabetics over 11 Years Follow-Up. Diagnostics, 14(8), 833-. DOI: 10.3390/diagnostics14080833
    DOI del artículo: 10.3390/diagnostics14080833
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2024
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Clinical Biochemistry,Medicine, General & Internal
    Validation
    Specificity
    Sensitivity
    Screening frequency
    Risk-assessment
    Optimization
    Model
    Microaneurysm turnover
    Interval
    Diabetic retinopathy
    Artificial intelligence
    Algorithm
    Medicine, general & internal
    Internal medicine
    Clinical biochemistry
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