Skip to main content
Top

2022 | OriginalPaper | Chapter

AI for the Detection of the Diabetic Retinopathy

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literature
1.
go back to reference Vocaturo, E., Veltri, P.: On the use of networks in biomedicine. In: FNC/MobiSPC (2017), pp. 498–503 Vocaturo, E., Veltri, P.: On the use of networks in biomedicine. In: FNC/MobiSPC (2017), pp. 498–503
2.
go back to reference Caroprese, L., Cascini, P.L., Cinaglia, P., Dattola, F., Franco, P., Iaquinta, P., Iusi, M., Tradigo, G., Veltri, P., Zumpano, E.: Software tools for medical imaging. In: ADBIS (Short Papers and Workshops), pp. 297–304 (2018) Caroprese, L., Cascini, P.L., Cinaglia, P., Dattola, F., Franco, P., Iaquinta, P., Iusi, M., Tradigo, G., Veltri, P., Zumpano, E.: Software tools for medical imaging. In: ADBIS (Short Papers and Workshops), pp. 297–304 (2018)
3.
go back to reference Gullo, F., Ponti, G., Tagarelli, A., Tradigo, G., Veltri, P.: A time series approach for clustering mass spectrometry data. J. Comput. Sci. 3(5), 344–355 (2012)CrossRef Gullo, F., Ponti, G., Tagarelli, A., Tradigo, G., Veltri, P.: A time series approach for clustering mass spectrometry data. J. Comput. Sci. 3(5), 344–355 (2012)CrossRef
4.
go back to reference Gardner, D., Akil, H., Ascoli, G.A., Bowden, D.M., Bug, W., Donohue, D.E., Goldberg, D.H., Grafstein, B., Grethe, J.S., Gupta, A., Halavi, M., Kennedy, D.N., Marenco, L., Martone, M.E., Miller, P.L., Muller, H.M., Robert, A., Shepherd, G.M., Sternberg, P.W., Van Essen, D.C.: The neuroscience information framework: a data and knowledge environment for neuroscience. Neuroinformatics 6, 149–160 (2008)CrossRef Gardner, D., Akil, H., Ascoli, G.A., Bowden, D.M., Bug, W., Donohue, D.E., Goldberg, D.H., Grafstein, B., Grethe, J.S., Gupta, A., Halavi, M., Kennedy, D.N., Marenco, L., Martone, M.E., Miller, P.L., Muller, H.M., Robert, A., Shepherd, G.M., Sternberg, P.W., Van Essen, D.C.: The neuroscience information framework: a data and knowledge environment for neuroscience. Neuroinformatics 6, 149–160 (2008)CrossRef
5.
go back to reference Muller, P., Schurmann, M., Guck, J.: ODTbrain: a Python library for full-view, dense diffraction tomography. BMC Bioinform. 16, 1–9 (2015)CrossRef Muller, P., Schurmann, M., Guck, J.: ODTbrain: a Python library for full-view, dense diffraction tomography. BMC Bioinform. 16, 1–9 (2015)CrossRef
6.
go back to reference Uhlmann, V., Singh, S., Carpenter, A.E.: CP-CHARM: segmentation-free image classification made accessible. BMC Bioinform. 17, 1–2 (2016)CrossRef Uhlmann, V., Singh, S., Carpenter, A.E.: CP-CHARM: segmentation-free image classification made accessible. BMC Bioinform. 17, 1–2 (2016)CrossRef
10.
11.
18.
go back to reference Li, Z., Keel, S., Liu, C., He, Y., Meng, W., Scheetz, J., Lee, P.Y., Shaw, J., Ting, D., Wong, T.Y., Taylor, H., Chang, R., He, M.: An automated grading system for detection of vision-threatening referable diabetic retinopathy on the basis of color fundus photographs. Diabetes Care 41(12), 2509–2516 (2018)CrossRef Li, Z., Keel, S., Liu, C., He, Y., Meng, W., Scheetz, J., Lee, P.Y., Shaw, J., Ting, D., Wong, T.Y., Taylor, H., Chang, R., He, M.: An automated grading system for detection of vision-threatening referable diabetic retinopathy on the basis of color fundus photographs. Diabetes Care 41(12), 2509–2516 (2018)CrossRef
19.
go back to reference Early Treatment Diabetic Retinopathy Study Research Group, Grading diabetic retinopathy from stereoscopic color fundus photographs An extension of the modified Airlie House classification: ETDRS report number 10. Ophthalmology 98, 786–806 (1991) Early Treatment Diabetic Retinopathy Study Research Group, Grading diabetic retinopathy from stereoscopic color fundus photographs An extension of the modified Airlie House classification: ETDRS report number 10. Ophthalmology 98, 786–806 (1991)
20.
go back to reference Abrmoff, M.D., Folk, J.C., Han, D.P., Walker, J.D., Williams, D.F., Russell, S.R., Massin, P., Cochener, B., Gain, P., Tang, L., Lamard, M.: Automated analysis of retinal images for detection of referable diabetic retinopathy. JAMA Ophthalmol. 131(3), 351–357 (2013)CrossRef Abrmoff, M.D., Folk, J.C., Han, D.P., Walker, J.D., Williams, D.F., Russell, S.R., Massin, P., Cochener, B., Gain, P., Tang, L., Lamard, M.: Automated analysis of retinal images for detection of referable diabetic retinopathy. JAMA Ophthalmol. 131(3), 351–357 (2013)CrossRef
21.
go back to reference Abrmoff, M.D., Lou, Y., Erginay, A., Clarida, W., Amelon, R., Folk, J.C., Niemeijer, M.: Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest. Ophthalmol. Vis. Sci. 57(13), 5200–5206 (2016)CrossRef Abrmoff, M.D., Lou, Y., Erginay, A., Clarida, W., Amelon, R., Folk, J.C., Niemeijer, M.: Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest. Ophthalmol. Vis. Sci. 57(13), 5200–5206 (2016)CrossRef
23.
go back to reference Gulshan, V., Peng, L., Coram, M., Stumpe, M.C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., Kim, R., Raman, R., Nelson, P.C., Mega, J.L., Webster, D.R.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22), 2402–2410 (2016). https://doi.org/10.1001/jama.2016.17216 CrossRef Gulshan, V., Peng, L., Coram, M., Stumpe, M.C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., Kim, R., Raman, R., Nelson, P.C., Mega, J.L., Webster, D.R.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22), 2402–2410 (2016). https://​doi.​org/​10.​1001/​jama.​2016.​17216 CrossRef
24.
go back to reference Tufail A, Kapetanakis VV, Salas-Vega S, Egan C, Rudisill C, Owen CG, et al. An observational study to assess if automated diabetic retinopathy image assessment software can replace one or more steps of manual imaging grading and to determine their cost-effectiveness. Health Technol. Assess. (Rockv) 20, 172 (2016). xxviii Tufail A, Kapetanakis VV, Salas-Vega S, Egan C, Rudisill C, Owen CG, et al. An observational study to assess if automated diabetic retinopathy image assessment software can replace one or more steps of manual imaging grading and to determine their cost-effectiveness. Health Technol. Assess. (Rockv) 20, 172 (2016). xxviii
25.
go back to reference Solanki, K., Ramachandra, C., Bhat, S., Bhaskaranand, M., Nittala, M.G., Sadda, S.R.: Automated, high-throughput, image analysis for diabetic retinopathy screening. Invest. Ophthalmol. Vis. Sci. 56, 1429 (2015) Solanki, K., Ramachandra, C., Bhat, S., Bhaskaranand, M., Nittala, M.G., Sadda, S.R.: Automated, high-throughput, image analysis for diabetic retinopathy screening. Invest. Ophthalmol. Vis. Sci. 56, 1429 (2015)
26.
go back to reference Ribeiro, L., Oliveira, C.M., Neves, C., Ramos, J.D., Ferreira, H., Cunha-Vaz, J.: Screening for diabetic retinopathy in the central region of Portugal. Added value of automated disease/no disease grading. Ophthalmologica 233, 96–103 (2015) Ribeiro, L., Oliveira, C.M., Neves, C., Ramos, J.D., Ferreira, H., Cunha-Vaz, J.: Screening for diabetic retinopathy in the central region of Portugal. Added value of automated disease/no disease grading. Ophthalmologica 233, 96–103 (2015)
27.
go back to reference Ribeiro, M.L., Nunes, S.G., Cunha-Vaz, J.G.: Microaneurysm turnover at the macula predicts risk of development of clinically significant macular edema in persons with mild nonproliferative diabetic retinopathy. Diabetes Care 36, 1254–1259 (2012)CrossRef Ribeiro, M.L., Nunes, S.G., Cunha-Vaz, J.G.: Microaneurysm turnover at the macula predicts risk of development of clinically significant macular edema in persons with mild nonproliferative diabetic retinopathy. Diabetes Care 36, 1254–1259 (2012)CrossRef
28.
go back to reference Bawankar, P., Shanbhag, N., Dhawan, B., Palsule, A., Kumar, D., Chandel, S., et al.: Sensitivity and specificity of automated analysis of single-field non-mydriatic fundus photographs by Bosch DR Algorithm Comparison with mydriatic fundus photography (ETDRS) for screening in undiagnosed diabetic retinopathy. PLoS One 12, e0189854 (2017)CrossRef Bawankar, P., Shanbhag, N., Dhawan, B., Palsule, A., Kumar, D., Chandel, S., et al.: Sensitivity and specificity of automated analysis of single-field non-mydriatic fundus photographs by Bosch DR Algorithm Comparison with mydriatic fundus photography (ETDRS) for screening in undiagnosed diabetic retinopathy. PLoS One 12, e0189854 (2017)CrossRef
29.
go back to reference Larsen, N., Godt, J., Grunkin, M., Lund-Andersen, H., Larsen, M.: Automated detection of diabetic retinopathy in a fundus photographic screening population. Invest. Ophthalmol. Vis. Sci. 44, 767–771 (2003)CrossRef Larsen, N., Godt, J., Grunkin, M., Lund-Andersen, H., Larsen, M.: Automated detection of diabetic retinopathy in a fundus photographic screening population. Invest. Ophthalmol. Vis. Sci. 44, 767–771 (2003)CrossRef
30.
go back to reference Larsen, M., Godt, J., Larsen, N., Lund-Andersen, H., Sjlie, A.K., Agardh, E., et al.: Automated detection of fundus photographic red lesions in diabetic retinopathy. Invest. Ophthalmol. Vis. Sci. 44, 761–766 (2003)CrossRef Larsen, M., Godt, J., Larsen, N., Lund-Andersen, H., Sjlie, A.K., Agardh, E., et al.: Automated detection of fundus photographic red lesions in diabetic retinopathy. Invest. Ophthalmol. Vis. Sci. 44, 761–766 (2003)CrossRef
32.
go back to reference Gargeya, R., Leng, T.: Automated identification of diabetic retinopathy using deep learning. Ophthalmology 124(7), 962–969 (2017)CrossRef Gargeya, R., Leng, T.: Automated identification of diabetic retinopathy using deep learning. Ophthalmology 124(7), 962–969 (2017)CrossRef
33.
go back to reference Carson Lam, D.Y., Guo, M., Lindsey, T.: Automated detection of diabetic retinopathy using deep learning. AMIA Summits Transl. Sci. Proc. 2018, 147 (2018) Carson Lam, D.Y., Guo, M., Lindsey, T.: Automated detection of diabetic retinopathy using deep learning. AMIA Summits Transl. Sci. Proc. 2018, 147 (2018)
34.
go back to reference Seth, S., Agarwal, B.: A hybrid deep learning model for detecting diabetic retinopathy. J. Stat. Manag. Syst. 21(4), 569–574 (2018) Seth, S., Agarwal, B.: A hybrid deep learning model for detecting diabetic retinopathy. J. Stat. Manag. Syst. 21(4), 569–574 (2018)
35.
go back to reference Li, Y.-H., Yeh, N.-N., Chen, S.-J., Chung, Y.-C.: Computer-assisted diagnosis for diabetic retinopathy based on fundus images using deep convolutional neural network. Mob. Inf. Syst. 2019, 114 (2019) Li, Y.-H., Yeh, N.-N., Chen, S.-J., Chung, Y.-C.: Computer-assisted diagnosis for diabetic retinopathy based on fundus images using deep convolutional neural network. Mob. Inf. Syst. 2019, 114 (2019)
36.
go back to reference Sisodia, D.S., Nair, S., Khobragade, P.: Diabetic retinal fundus images: Preprocessing and feature extraction for early detection of diabetic retinopathy. Biomed. Pharmacol. J. 10(2), 615–626 (2017)CrossRef Sisodia, D.S., Nair, S., Khobragade, P.: Diabetic retinal fundus images: Preprocessing and feature extraction for early detection of diabetic retinopathy. Biomed. Pharmacol. J. 10(2), 615–626 (2017)CrossRef
37.
go back to reference Li, X., Pang, T., Xiong, B., Liu, W., Liang, P., Wang, T.: Convolutional neural networks based transfer learning for diabetic retinopathy fundus image classification. In: Proc. 10th Int. Congr. Image Signal Process., Biomed. Eng. Informat. (CISP-BMEI), October, pp. 1–11 (2017) Li, X., Pang, T., Xiong, B., Liu, W., Liang, P., Wang, T.: Convolutional neural networks based transfer learning for diabetic retinopathy fundus image classification. In: Proc. 10th Int. Congr. Image Signal Process., Biomed. Eng. Informat. (CISP-BMEI), October, pp. 1–11 (2017)
38.
go back to reference Zhou, L., Zhao, Y., Yang, J., Yu, Q., Xu, X.: Deep multiple instance learning for automatic detection of diabetic retinopathy in retinal images. IET Image Process. 12(4), pp. 563–571 (2017)CrossRef Zhou, L., Zhao, Y., Yang, J., Yu, Q., Xu, X.: Deep multiple instance learning for automatic detection of diabetic retinopathy in retinal images. IET Image Process. 12(4), pp. 563–571 (2017)CrossRef
39.
go back to reference Astorino, A., Fuduli, A., Gaudioso, M., Vocaturo, E.: A multiple instance learning algorithm for color images classification. In: Proceedings of the 22nd Int. Database Engineering & Applications Symposium, IDEAS, June 2018, pp. 262–266. ACM, New York (2018) Astorino, A., Fuduli, A., Gaudioso, M., Vocaturo, E.: A multiple instance learning algorithm for color images classification. In: Proceedings of the 22nd Int. Database Engineering & Applications Symposium, IDEAS, June 2018, pp. 262–266. ACM, New York (2018)
40.
go back to reference Astorino, A., Fuduli, A., Veltri, P., Vocaturo, E.: On a recent algorithm for multiple instance learning. Preliminary applications in image classification. In: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1615–1619 (2017) Astorino, A., Fuduli, A., Veltri, P., Vocaturo, E.: On a recent algorithm for multiple instance learning. Preliminary applications in image classification. In: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1615–1619 (2017)
Metadata
Title
AI for the Detection of the Diabetic Retinopathy
Authors
Eugenio Vocaturo
Ester Zumpano
Copyright Year
2022
DOI
https://doi.org/10.1007/978-3-030-91181-2_8