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A New Deep Learning Algorithm with Activation Mapping for Diabetic Retinopathy: Backtesting after 10 Years of Tele-Ophthalmology

  • Dades identificatives

    Identificador: imarina:9281706
    Autors:
    Pareja-Rios, AliciaCeruso, SabatoRomero-Aroca, PedroBonaque-Gonzalez, Sergio
    Resum:
    We report the development of a deep learning algorithm (AI) to detect signs of diabetic retinopathy (DR) from fundus images. For this, we use a ResNet-50 neural network with a double resolution, the addition of Squeeze-Excitation blocks, pre-trained in ImageNet, and trained for 50 epochs using the Adam optimizer. The AI-based algorithm not only classifies an image as pathological or not but also detects and highlights those signs that allow DR to be identified. For development, we have used a database of about half a million images classified in a real clinical environment by family doctors (FDs), ophthalmologists, or both. The AI was able to detect more than 95% of cases worse than mild DR and had 70% fewer misclassifications of healthy cases than FDs. In addition, the AI was able to detect DR signs in 1258 patients before they were detected by FDs, representing 7.9% of the total number of DR patients detected by the FDs. These results suggest that AI is at least comparable to the evaluation of FDs. We suggest that it may be useful to use signaling tools such as an aid to diagnosis rather than an AI as a stand-alone tool.
  • Altres:

    Autor segons l'article: Pareja-Rios, Alicia; Ceruso, Sabato; Romero-Aroca, Pedro; Bonaque-Gonzalez, Sergio;
    Departament: Medicina i Cirurgia
    Autor/s de la URV: Romero Aroca, Pedro
    Paraules clau: Tele-ophthalmology Diabetic retinopathy Deep learning Artificial-intelligence Artificial intelligence
    Resum: We report the development of a deep learning algorithm (AI) to detect signs of diabetic retinopathy (DR) from fundus images. For this, we use a ResNet-50 neural network with a double resolution, the addition of Squeeze-Excitation blocks, pre-trained in ImageNet, and trained for 50 epochs using the Adam optimizer. The AI-based algorithm not only classifies an image as pathological or not but also detects and highlights those signs that allow DR to be identified. For development, we have used a database of about half a million images classified in a real clinical environment by family doctors (FDs), ophthalmologists, or both. The AI was able to detect more than 95% of cases worse than mild DR and had 70% fewer misclassifications of healthy cases than FDs. In addition, the AI was able to detect DR signs in 1258 patients before they were detected by FDs, representing 7.9% of the total number of DR patients detected by the FDs. These results suggest that AI is at least comparable to the evaluation of FDs. We suggest that it may be useful to use signaling tools such as an aid to diagnosis rather than an AI as a stand-alone tool.
    Àrees temàtiques: Medicine, general & internal Medicine (miscellaneous) Medicine (all)
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    Adreça de correu electrònic de l'autor: pedro.romero@urv.cat
    Identificador de l'autor: 0000-0002-7061-8987
    Data d'alta del registre: 2024-09-07
    Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
    Enllaç font original: https://www.mdpi.com/2077-0383/11/17/4945
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referència a l'article segons font original: Journal Of Clinical Medicine. 11 (17):
    Referència de l'ítem segons les normes APA: Pareja-Rios, Alicia; Ceruso, Sabato; Romero-Aroca, Pedro; Bonaque-Gonzalez, Sergio; (2022). A New Deep Learning Algorithm with Activation Mapping for Diabetic Retinopathy: Backtesting after 10 Years of Tele-Ophthalmology. Journal Of Clinical Medicine, 11(17), -. DOI: 10.3390/jcm11174945
    DOI de l'article: 10.3390/jcm11174945
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2022
    Tipus de publicació: Journal Publications
  • Paraules clau:

    Medicine (Miscellaneous),Medicine, General & Internal
    Tele-ophthalmology
    Diabetic retinopathy
    Deep learning
    Artificial-intelligence
    Artificial intelligence
    Medicine, general & internal
    Medicine (miscellaneous)
    Medicine (all)
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