Articles producció científica> Enginyeria Informàtica i Matemàtiques

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

  • Identification data

    Identifier: imarina:9366227
  • Authors:

    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
    Diagnostics
    10.3390/diagnostics14080833
    Diagnostics. 14 (8): 833-
  • Others:

    Author, as appears in the article.: 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
    Department: Enginyeria Informàtica i Matemàtiques
    URV's Author/s: Moreno Ribas, Antonio / Pascual Fontanilles, Jordi / Valls Mateu, Aïda / Vergés Pujol, Raquel
    Keywords: Validation Specificity Sensitivity Screening frequency Risk-assessment Optimization Model Microaneurysm turnover Interval Diabetic retinopathy Artificial intelligence Algorithm
    Abstract: (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.
    Thematic Areas: Medicine, general & internal Internal medicine Clinical biochemistry
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: jordi.pascual@urv.cat raquel.vergesp@estudiants.urv.cat jordi.pascual@urv.cat antonio.moreno@urv.cat aida.valls@urv.cat
    Author identifier: 0000-0002-7528-5819 0000-0003-2693-0612 0000-0002-7528-5819 0000-0003-3945-2314 0000-0003-3616-7809
    Record's date: 2024-07-20
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://www.mdpi.com/2075-4418/14/8/833
    Licence document URL: http://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Diagnostics. 14 (8): 833-
    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
    Article's DOI: 10.3390/diagnostics14080833
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2024
    Publication Type: Journal Publications
  • Keywords:

    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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