#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

ARTEFICIAL INTELLIGENCE IN DIABETIC RETINOPATHY SCREENING. A REVIEW


Authors: Z. Straňák *;  M. Penčák *;  M. Veith
Authors place of work: Oftalmologická klinika, 3. lékařská fakulta, Univerzita Karlova, a Fakultní nemocnice Královské Vinohrady, Praha
Published in the journal: Čes. a slov. Oftal., 77, 2021, No. 5, p. 224-231
Category: Review Article
doi: https://doi.org/10.31348/2021/6

Summary

Objective: The aim of this comprehensive paper is to acquaint the readers with evaluation of the retinal images using the arteficial intelligence (AI). Main focus of the paper is diabetic retinophaty (DR) screening. The basic principles of the artificial intelligence and algorithms that are already used in clinical practice or are shortly before approval will be described.

Methodology: Describing the basic characteristics and mechanisms of different approaches to the use of AI and subsequently literary minireview clarifying the current state of knowledge in the area.

Results: Modern systems for screening diabetic retinopathy using deep neural networks achieve a sensitivity and specificity of over 80 % in most published studies. The results of specific studies vary depending on the definition of the gold standard, number of images tested and on the evaluated parameters.

Conclusion: Evaluation of images using AI will speed up and streamline the diagnosis of DR. The use of AI will allow to keep the quality of the eye care at least on the same level despite the raising number of the patients with diabetes.

Keywords:

artificial intelligence – screening – Diabetic retinopathy


Zdroje

1. Kulkarni S, Seneviratne N, Baig MS, Khan AHA. Artificial Intelligence in Medicine: Where Are We Now? Acad Radiol. leden 2020;27(1):62–70.

2. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2. únor 2017;542(7639):115–118.

3. Witmer MT, Kiss S. Wide-field imaging of the retina. Surv Ophthalmol. duben 2013;58(2):143–154.

4. Piyasena MMPN, Gudlavalleti VSM, Gilbert C, Yip JL, Peto T, MacLeod D, et al. Development and Validation of a Diabetic Retinopathy Screening Modality Using a Hand-Held Nonmydriatic Digital Retinal Camera by Physician Graders at a Tertiary-Level Medical Clinic: Protocol for a Validation Study. JMIR Res Protoc. 10. prosinec 2018;7(12):e10900.

5. Liew G, Michaelides M, Bunce C. A comparison of the causes of blindness certifications in England and Wales in working age adults (16-64 years), 1999-2000 with 2009-2010. BMJ Open. nor 2014;4(2):e004015–e004015.

6. Cheung N, Mitchell P, Wong TY. Diabetic retinopathy. The Lancet. ervenec 2010;376(9735):124–136.

7. Ústav zfravotnických informací a statistiky. ZDRAVOTNICTVÍ ČR: Stručný přehled činnosti oboru diabetologie a endokrinologie za období 2007–2017 NZIS REPORT č. K/1 (08/2018). Ústav zfravotnických informací a statistiky; 2018.

8. Scanlon PH. The English National Screening Programme for diabetic retinopathy 2003-2016. Acta Diabetol. erven 2017;54(6):515–525.

9. Nguyen HV, Tan GSW, Tapp RJ, Mital S, Ting DSW, Wong HT, et al. Cost-effectiveness of a National Telemedicine Diabetic Retinopathy Screening Program in Singapore. Ophthalmology. 1. prosinec 2016;123(12):2571–2580.

10. 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 Winch Engl. prosinec 2016;20(92):1–72.

11. Fleming AD, Goatman KA, Philip S, Prescott GJ, Sharp PF, Olson JA. Automated grading for diabetic retinopathy: a large-scale audit using arbitration by clinical experts. Br J Ophthalmol. 1. prosinec 2010;94(12):1606.

12. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 1. květen 2015;521(7553):436–444.

13. Abràmoff MD, Lavin PT, Birch M, Shah N, Folk JC. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med. 28. srpen 2018;1:39–39.

14. Yang WH, Zheng B, Wu MN, Zhu SJ, Fei FQ, Weng M, Zhang X, Lu PR. An Evaluation System of Fundus Photograph-Based Intelligent Diagnostic Technology for Diabetic Retinopathy and Applicability for Research. Diabetes Ther. 2019 Oct;10(5):1811-1822. doi: 10.1007/s13300-019-0652-0. Epub 2019 Jul 9. PMID: 31290125; PMCID: PMC6778552

15. van der Heijden AA, Abramoff MD, Verbraak F, van Hecke MV, Liem A, Nijpels G. Validation of automated screening for referable diabetic retinopathy with the IDx-DR device in the Hoorn Diabetes Care System. Acta Ophthalmol (Copenh). nor 2018;96(1):63–68.

16. Verbraak FD, Abramoff MD, Bausch GCF, Klaver C, Nijpels G, Schlingemann RO, et al. Diagnostic Accuracy of a Device for the Automated Detection of Diabetic Retinopathy in a Primary Care Setting. Diabetes Care. 1. duben 2019;42(4):651.

17. Shah A, Clarida W, Amelon R, Hernaez-Ortega MC, Navea A, Morales- Olivas J, et al. Validation of Automated Screening for Referable Diabetic Retinopathy With an Autonomous Diagnostic Artificial Intelligence System in a Spanish Population. J Diabetes Sci Technol. 16. březen 2020;1932296820906212.

18. Grzybowski A, Brona P. A pilot study of autonomous artificial intelligence- based diabetic retinopathy screening in Poland. Acta Ophthalmol (Copenh) [Internet]. 3. květen 2019 [citován 6. říjen 2019];0(0). Dostupné z: https://doi.org/10.1111/aos.14132

19. Ting DSW, Cheung CY-L, Lim G, Tan GSW, Quang ND, Gan A, et al. Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes. JAMA. 12. prosinec 2017;318(22):2211–2223.

20. Ribeiro ML, Nunes SG, Cunha-Vaz JG. Microaneurysm turnover at the macula predicts risk of development of clinically significant macular edema in persons with mild nonproliferative diabetic retinopathy. Diabetes Care. 2012/11/30 vyd. květen 2013;36(5):1254–1259.

21. Ramos JD, Almeida N, Oliveira CM, Neves C, Ribeiro L. 28th Meeting of the European Association for the Study of Diabetes Eye Complications Study Group (EASDec): Belfast Northern Ireland 24th – 26th May 2018. Eur J Ophthalmol. 1. květen 2018;28(1_ suppl):1–38.

22. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, et al. Going Deeper with Convolutions. 16. září 2014;

23. Szegedy C, Ioffe S, Vanhoucke V, Alemi A. Inception-v4, inception- -resnet and the impact of residual connections on learning. ArXiv Prepr ArXiv160207261. 2016;

24. Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 13. prosinec 2016;316(22):2402–2410.

25. Krause J, Gulshan V, Rahimy E, Karth P, Widner K, Corrado GS, et al. Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic Retinopathy. Ophthalmology. 1. srpen 2018;125(8):1264–1272.

26. Raumviboonsuk P, Krause J, Chotcomwongse P, Sayres R, Raman R, Widner K, et al. Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program. NPJ Digit Med. 10. duben 2019;2:25–25.

27. Li Z, Keel S, Liu C, He Y, Meng W, Scheetz J, et al. An Automated Grading System for Detection of Vision-Threatening Referable Diabetic Retinopathy on the Basis of Color Fundus Photographs. Diabetes Care. 1. prosinec 2018;41(12):2509.

28. Sahlsten J, Jaskari J, Kivinen J, Turunen L, Jaanio E, Hietala K, et al. Deep Learning Fundus Image Analysis for Diabetic Retinopathy and Macular Edema Grading. Sci Rep. ervenec 2019;9(1):10750–10750.

29. Natarajan S, Jain A, Krishnan R, Rogye A, Sivaprasad S. Diagnostic Accuracy of Community-Based Diabetic Retinopathy Screening With an Offline Artificial Intelligence System on a Smartphone. JAMA Ophthalmol. 8. srpen 2019;137(10):1182–1188.

30. Sosale B, Aravind SR, Murthy H, Narayana S, Sharma U, Gowda SGV, et al. Simple, Mobile-based Artificial Intelligence Algorithm in the detection of Diabetic Retinopathy (SMART) study. BMJ Open Diabetes Res Care. leden 2020;8(1):e000892.

31. Sosale B, Sosale AR, Murthy H, Sengupta S, Naveenam M. Medios- An offline, smartphone-based artificial intelligence algorithm for the diagnosis of diabetic retinopathy. Indian J Ophthalmol. nor 2020;68(2):391–395.

32. Bhaskaranand M, Ramachandra C, Bhat S, Cuadros J, Nittala MG, Sadda S, et al. Automated Diabetic Retinopathy Screening and Monitoring Using Retinal Fundus Image Analysis. J Diabetes Sci Technol. nor 2016;10(2):254–261.

33. Tufail A, Rudisill C, Egan C, Kapetanakis VV, Salas-Vega S, Owen CG, et al. Automated Diabetic Retinopathy Image Assessment Software: Diagnostic Accuracy and Cost-Effectiveness Compared with Human Graders. Ophthalmology. 1. březen 2017;124(3):343–351.

34. Bhaskaranand M, Ramachandra C, Bhat S, Cuadros J, Nittala MG, Sadda SR, et al. The Value of Automated Diabetic Retinopathy Screening with the EyeArt System: A Study of More Than 100,000 Consecutive Encounters from People with Diabetes. Diabetes Technol Ther. 2019/08/07 vyd. listopad 2019;21(11):635–643.

35. Heydon P, Egan C, Bolter L, Chambers R, Anderson J, Aldington S, et al. Prospective evaluation of an artificial intelligence-enabled algorithm for automated diabetic retinopathy screening of 30 000 patients. Br J Ophthalmol. erven 2020;bjophthalmol- 2020-316594.

36. González-Gonzalo C, Sánchez-Gutiérrez V, Hernández-Martínez P, Contreras I, Lechanteur YT, Domanian A, et al. Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age-related macular degeneration. Acta Ophthalmol (Copenh). 2019/11/26 vyd. erven 2020;98(4): 368–377.

37. Riaz H, Park J, Choi H, Kim H, Kim J. Deep and Densely Connected Networks for Classification of Diabetic Retinopathy. Diagn Basel Switz. 2. leden 2020;10(1):24.

38. Goertzel B. Artificial General Intelligence: Concept, State of the Art, and Future Prospects. J Artif Gen Intell. 13. leden 2014;0.

39. Ethical Dimensions of Using Artificial Intelligence in Health Care. AMA J Ethics. 1. únor 2019;21(2):E121–124.

40. Lehman CD, Wellman RD, Buist DSM, Kerlikowske K, Tosteson ANA, Miglioretti DL, et al. Diagnostic Accuracy of Digital Screening Mammography With and Without Computer-Aided Detection. JAMA Intern Med. 1. listopad 2015;175(11):1828–1837.

Štítky
Ophthalmology
Prihlásenie
Zabudnuté heslo

Zadajte e-mailovú adresu, s ktorou ste vytvárali účet. Budú Vám na ňu zasielané informácie k nastaveniu nového hesla.

Prihlásenie

Nemáte účet?  Registrujte sa

#ADS_BOTTOM_SCRIPTS#