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Convolutional Neural Networks for Diabetic Retinopathy Grading from iPhone Fundus Images

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Hybrid Artificial Intelligent Systems (HAIS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14001))

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Abstract

Diabetic eye diseases is a major issue in Europe and the prevalence of visual impairment and blindness caused by Diabetic Retinopathy (DR) has significantly increased in the last decades. Efficient screening and early diagnose of DR by family physicians would help to reduce costs in health systems and shorten waiting lists, thus decreasing patients’ emotional stress. In this sense, the use of portable image devices (e.g., a mobile phone with a specific fundus image capturing device attach to it) combined with AI-based systems arise as a powerful tool to address this problem. This paper develops 2 well-known pre-trained Convolutional Neural Networks and fine-tune them on a local Spanish cohort and 3 more publicly available fundus image dataset for DR grading. The models trained were evaluated on fundus images captured using an iPhone mobile within the local Spanish cohort. The results of the analysis showed how in one of the settings tested, one of the models was able to surpass human-level performance achieving an AUC of 0.679 in comparison to an AUC of 0.667 achieved by ophthalmologists when diagnosing the grade of DR on the same iPhone fundus images, although further work and improvements need to take place in order to consider it for a realistic deployment in the daily clinical practice.

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Notes

  1. 1.

    Available at: https://apps.apple.com/es/app/ret-in-cam/id1509765945.

  2. 2.

    Available at: https://www.kaggle.com/competitions/diabetic-retinopathy-detection/overview.

  3. 3.

    Available at: https://github.com/deepdrdoc/DeepDRiD.

  4. 4.

    Available at: https://zenodo.org/record/4891308#.ZEaOEHZByUn.

  5. 5.

    Available at: https://docs.opencv.org/4.x/dd/d1a/group__imgproc__feature.html#ga47849c3be0d0406ad3ca45db65a25d2d.

  6. 6.

    Available at: https://scikit-image.org/docs/stable/api/skimage.restoration.html#skimage.restoration.inpaint_biharmonic.

References

  1. Akudjedu, T.N., Torre, S., Khine, R., Katsifarakis, D., Newman, D., Malamateniou, C.: Knowledge, perceptions, and expectations of artificial intelligence in radiography practice: a global radiography workforce survey. J. Med. Imaging Radiat. Sci. 54(1), 104–116 (2023)

    Article  Google Scholar 

  2. Anwar, S.M., Majid, M., Qayyum, A., Awais, M., Alnowami, M., Khan, M.K.: Medical image analysis using convolutional neural networks: a review. J. Med. Syst. 42, 1–13 (2018)

    Article  Google Scholar 

  3. Boucher, M.C., et al.: Teleophthalmology screening for diabetic retinopathy through mobile imaging units within Canada. Can. J. Ophthalmol. 43(6), 658–668 (2008)

    Article  Google Scholar 

  4. Chollet, F., et al.: Keras (2015). https://keras.io

  5. Constable, I., Yogesan, K., Eikelboom, R., Barry, C., Cuypers, M.: Fred hollows lecture: digital screening for eye disease. Clin. Exp. Ophthalmol. 28(3), 129–132 (2000)

    Article  Google Scholar 

  6. Esteva, A., et al.: Deep learning-enabled medical computer vision. NPJ Digit. Med. 4(1), 1–9 (2021)

    Google Scholar 

  7. Flaxman, S.R., et al.: Global causes of blindness and distance vision impairment 1990–2020: a systematic review and meta-analysis. Lancet Glob. Health 5(12), e1221–e1234 (2017)

    Article  Google Scholar 

  8. Gonçalves, C.B., Souza, J.R., Fernandes, H.: CNN architecture optimization using bio-inspired algorithms for breast cancer detection in infrared images. Comput. Biol. Med. 142, 105205 (2022)

    Google Scholar 

  9. Group, D.R.: Frequency of evidence-based screening for retinopathy in type 1 diabetes. N. Engl. J. Med. 376(16), 1507–1516 (2017)

    Google Scholar 

  10. Gu, J., et al.: Recent advances in convolutional neural networks. Pattern Recognit. 77, 354–377 (2018)

    Article  Google Scholar 

  11. Gulshan, V., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316, 2402–2410 (2016)

    Google Scholar 

  12. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  13. He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. CoRR abs/1603.05027 (2016)

    Google Scholar 

  14. International Diabetes Federation: IDF diabetes atlas 2019 (2019). https://www.diabetesatlas.org/en/. Accessed 2 Mar 2020

  15. Karatzia, L., Aung, N., Aksentijevic, D.: Artificial intelligence in cardiology: hope for the future and power for the present. Front. Cardiovasc. Med. 9 (2022)

    Google Scholar 

  16. Krishna, S.T., Kalluri, H.K.: Deep learning and transfer learning approaches for image classification. Int. J. Recent Technol. Eng. (IJRTE) 7(5S4), 427–432 (2019)

    Google Scholar 

  17. Kuo, R.Y., et al.: Artificial intelligence in fracture detection: a systematic review and meta-analysis. Radiology 304(1), 50–62 (2022). pMID: 35348381

    Google Scholar 

  18. Li, J.Q., et al.: Prevalence, incidence and future projection of diabetic eye disease in Europe: a systematic review and meta-analysis. Eur. J. Epidemiol. 35, 11–23 (2020)

    Article  Google Scholar 

  19. Liu, H., Teng, L., Fan, L., Sun, Y., Li, H.: A new ultra-wide-field fundus dataset to diabetic retinopathy grading using hybrid preprocessing methods. Comput. Biol. Med. 157, 106750 (2023)

    Article  Google Scholar 

  20. Mujeeb Rahman, K., Nasor, M., Imran, A.: Automatic screening of diabetic retinopathy using fundus images and machine learning algorithms. Diagnostics 12(9), 2262 (2022)

    Article  Google Scholar 

  21. Ogurtsova, K., et al.: IDF diabetes atlas: global estimates for the prevalence of diabetes for 2015 and 2040. Diabetes Res. Clin. Pract. 128, 40–50 (2017)

    Article  Google Scholar 

  22. of Ophthalmology, A.A.: Diabetic retinopathy ppp - updated 2017. https://www.aao.org/preferred-practice-pattern/diabetic-retinopathy-ppp-updated-2017. Accessed 22 Jan 2020

  23. Piccialli, F., Somma, V.D., Giampaolo, F., Cuomo, S., Fortino, G.: A survey on deep learning in medicine: why, how and when? Inf. Fusion 66, 111–137 (2021)

    Google Scholar 

  24. Pranav, R., Emma, C., Oishi, B., J., T.E.: AI in health and medicine. Nat. Med. 28, 31–38 (2022)

    Google Scholar 

  25. Qin, X., Chen, D., Zhan, Y., Yin, D.: Classification of diabetic retinopathy based on improved deep forest model. Biomed. Signal Process. Control 79, 104020 (2023)

    Google Scholar 

  26. Shi, L., Wu, H., Dong, J., Jiang, K., Lu, X., Shi, J.: Telemedicine for detecting diabetic retinopathy: a systematic review and meta-analysis. Br. J. Ophthalmol. 99(6), 823–831 (2015)

    Article  Google Scholar 

  27. Tran, B.X., et al.: Global evolution of research in artificial intelligence in health and medicine: a bibliometric study. J. Clin. Med. 8(3), 360 (2019)

    Google Scholar 

  28. Yiming, Z., Ying, W., Jonathan, L.: Applications of explainable artificial intelligence in diagnosis and surgery. Diagnostics 12, 237 (2022)

    Google Scholar 

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Acknowledgements

The authors acknowledge support given by the supercomputing center in Castilla y León (SCAYLE).

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Authors and Affiliations

  1. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Digitalización, Escuela Politécnica Superior, Universidad de Burgos, Av. Cantabria s/n, 09006, Burgos, Spain

    Samuel Lozano-Juárez, Nuria Velasco-Pérez, Nuño Basurto, Daniel Urda & Álvaro Herrero

  2. Departamento de Ciencias de la Salud, Facultad de Ciencias de la Salud, Universidad de Burgos, Paseo Comendadores, s/n, 09001, Burgos, Spain

    Ian Roberts & Jerónimo Bernal

Authors
  1. Samuel Lozano-Juárez
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  2. Nuria Velasco-Pérez
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  3. Ian Roberts
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  4. Jerónimo Bernal
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  5. Nuño Basurto
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  6. Daniel Urda
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  7. Álvaro Herrero
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Corresponding author

Correspondence to Daniel Urda .

Editor information

Editors and Affiliations

  1. University of Deusto, Bilbao, Spain

    Pablo García Bringas

  2. University of Leon, León, Spain

    Hilde Pérez García

  3. University of La Rioja, Logroño, La Rioja, Spain

    Francisco Javier Martínez de Pisón

  4. Pablo de Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  5. Pablo de Olavide University, Seville, Spain

    Alicia Troncoso Lora

  6. University of Burgos, Burgos, Spain

    Álvaro Herrero

  7. University of A Coruña, Ferrol - Coruña, Spain

    José Luis Calvo Rolle

  8. University of A Coruña, Ferrol - Coruña, Spain

    Héctor Quintián

  9. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Lozano-Juárez, S. et al. (2023). Convolutional Neural Networks for Diabetic Retinopathy Grading from iPhone Fundus Images. In: García Bringas, P., et al. Hybrid Artificial Intelligent Systems. HAIS 2023. Lecture Notes in Computer Science(), vol 14001. Springer, Cham. https://doi.org/10.1007/978-3-031-40725-3_58

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  • DOI: https://doi.org/10.1007/978-3-031-40725-3_58

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-40724-6

  • Online ISBN: 978-3-031-40725-3

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