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AI for the Detection of the Diabetic Retinopathy

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Integrating Artificial Intelligence and IoT for Advanced Health Informatics

Part of the book series: Internet of Things ((ITTCC))

Abstract

Diabetes has become one of the major causes of deaths in the world, and diabetic eye complications causing blindness and low vision have greatly increased. The International Diabetes Federation (IDF) (International Diabetes Federation, https://www.diabetesatlas.org/en/sections/worldwide-toll-of-diabetes.html) reports that about 1 in 11 adults (463 million people) worldwide has diabetes, and 1.6 million deaths are directly attributed to diabetes each year. It also estimates that, by 2035, there will be 600 million people with diabetes, and by 2045 the number will be 700 million.

Diabetic retinopathy (DR) is a complication of diabetes that affects eyes: it originates from the damage of the blood vessels of the light-sensitive tissue of the retina and is among the primary cause of blindness.

Considering the number of patients affected by diabetes worldwide, it is straightforward that an affective screening of potential number of patients affected by DR is of paramount importance. While the primary method for evaluating diabetic retinopathy involves direct and indirect ophthalmoscopy, artificial intelligence (AI) has been on the rise in the eye care sector. AI uses sophisticated algorithms to analyze a vast amount of clinical data in order to provide effective diagnostic insights with the final aim of accomplishing tasks with minimal involvement of human beings. AI is undoubtedly a major frontier in the general healthcare domain. AI tools provide low-cost and effective solutions in supporting early and accurate diagnosis, facilitating the work of specialists, allowing the release of low-cost solutions for effective (self)-diagnosis, and allowing to select specific treatments. Diabetic retinopathy can be revealed by analyzing fundus photograph datasets of patients and therefore is a disease to which AI tools can provide effective support. This chapter describes the state of the art of AI-based DR screening technologies, some of which are already commercially available.

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Author information

Authors and Affiliations

  1. DIMES, University of Calabria, Rende, Italy

    Eugenio Vocaturo

  2. CNR-NANOTEC National Research Council, Rende, Italy

    Eugenio Vocaturo

  3. DIMES, University of Calabria, Rende, Italy

    Ester Zumpano

Authors
  1. Eugenio Vocaturo
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  2. Ester Zumpano
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Correspondence to Ester Zumpano .

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Editors and Affiliations

  1. ICAR-CNR, University of Calabria, Rende, Italy

    Carmela Comito

  2. ICAR-CNR, University of Calabria, Rende, Italy

    Agostino Forestiero

  3. DIMES, University of Calabria, Rende, Italy

    Ester Zumpano

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Vocaturo, E., Zumpano, E. (2022). AI for the Detection of the Diabetic Retinopathy. In: Comito, C., Forestiero, A., Zumpano, E. (eds) Integrating Artificial Intelligence and IoT for Advanced Health Informatics. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-030-91181-2_8

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  • DOI: https://doi.org/10.1007/978-3-030-91181-2_8

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