Future of artificial intelligence for the diagnosis and treatment of retinal diseases
DOI: 10.32364/2311-7729-2022-22-1-36-43
E.A. Katalevskaya1, D.Yu. Katalevskiy1,2, M.I. Tyurikov1,3, I.A. Velieva4, A.V. Bol’shunov4
1LLC "Digital Vision Solutions", Moscow, Russian Federation
2Russian Presidential Academy of National Economy and Public Administration (RANEPA), Moscow, Russian Federation
3Nizhny Novgorod State Technical University named after R.E. Alekseev, Nizhny Novgorod, Russian Federation
4Scientific Research Institute of Eye Diseases, Moscow, Russian Federation
Artificial intelligence (AI) is a branch of informatics concerned with developing programs capable of imitating certain functions of the human mind. AI technologies are applied in different areas of medicine, including ophthalmology. Acquisition and analysis of images are critical for diagnosing and treating ophthalmic disorders, particularly retinal diseases. This paper reviews the use of AI algorithms for diagnosing and treating retinal disorders. Modern AI algorithms are trained to analyze images obtained by digital retinal photography, optical coherence tomography, and optical coherence tomography angiography. The most promising AI algorithms are those used for screening and treating chronic diseases (e.g., diabetic retinopathy, diabetic macular edema, age-related macular degeneration) to provide a personalized management strategy. Therefore, developing standards regulating AI technology application and standards providing a reliable evaluation of safety, accuracy, and reliability of AI-based system functioning is particularly important for healthcare.
Keywords: artificial intelligence, computer vision, ophthalmic screening, diabetic retinopathy, diabetic macular edema, age-related macular degeneration.
For citation: Katalevskaya E.A., Katalevskiy D.Yu., Tyurikov M.I. et al. Future of artificial intelligence for the diagnosis and treatment of retinal diseases. Russian Journal of Clinical Ophthalmology. 2022;22(1):36–43 (in Russ.). DOI: 10.32364/2311-7729-2022-22-1-36-43.
About the authors:
1Evgeniya A. Katalevskaya — C. Sc. (Med.), opthahlmologist, Principal Investigator of the RETINA AI; ORCID iD 0000-0002-5710-9205.
1,2Dmitriy Yu. Katalevskiy — C. Sc. (Econ.), CEO; associate professor of the Institute of Business and Business Administration of the RANEPA; ORCID iD 0000-0002-3920-5041.
1,3Maksim I. Tyurikov — chief engineer programmer; assistant of the Department of Machinery Automation; ORCID iD 0000-0003-1839-4506.
4Indzhi A. Velieva — C. Sc. (Med.), ophthalmologist, researcher; ORCID iD 0000-0002-9230-3150.
4Andrey V. Bolshunov — Dr. Sc. (Med.), Professor; ORCID iD 0000-0002-2316-9920.
1LLC "Digital Vision Solutions", 86, Tsentralnaya str., Vnukovo, Moscow, 119027, Russian Federation.
2Russian Presidential Academy of National Economy and Public Administration (RANEPA), 82, Vernadskiy av., Moscow, 119571, Russian Federation.
3Nizhny Novgorod State Technical University named after R.E. Alekseev, 24, Minin str., Nizhny Novgorod, 603950, Russian Federation.
4Scientific Research Institute of Eye Diseases, 11A, Rossolimo str., Moscow, 119021, Russian Federation.
Contact information: Evgeniya A. Katalevskaya, e-mail: ekatalevskaya@mail.ru.
Financial Disclosure: no authors have a financial or property interest in any material or method mentioned.
There is no conflict of interests.
Received 21.12.2020.
2. Toprak A. Extreme Learning Machine (ELM)-Based Classification of Benign and Malignant Cells in Breast Cancer. Med Sci Monit. 2018;24:6537–6543. DOI: 10.12659/MSM.910520.
3. Esteva A., Kuprel B., Novoa R.A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115–118. DOI: 10.1038/nature21056.
4. Voelker R. Cardiac Ultrasound Uses Artificial Intelligence to Produce Images. JAMA. 2020;323(11):1034. DOI: 10.1001/jama.2020.2547.
5. Балашевич Л.И., Измайлов А.С. Диабетическая офтальмопатия. СПб.: Человек; 2012. [Balashevich L.I., Izmailov A.S. Diabetic ophthalmopathy. SPb.: Chelovek; 2012 (in Russ.)].
6. Руководство Международного совета по офтальмологии по ведению пациентов с диабетической ретинопатией. (Электронный ресурс.) URL: https://www.urmc.rochester.edu/MediaLibraries/URMCMedia/eye-institute/images/ICOPH.pdf — 28.01.2022 (дата обращения: 25.01.2022). [International Council of Ophthalmology guidelines for the management of patients with diabetic retinopathy. (Electronic resource.) URL: https://www.urmc.rochester.edu/MediaLibraries/URMCMedia/eye-institute/images/ICOPH.pdf — 28.01.2022 (access date: 25.01.2022) (in Russ.)].
7. Shah A., Clarida W., Amelon R. 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. 2021;15(3):655–663. DOI: 10.1177/1932296820906212.
8. Grzybowski A., Brona P., Lim G. et al. Artificial intelligence for diabetic retinopathy screening: a review. Eye (Lond). 2020;34(3):451–460. DOI: 10.1038/s41433-019-0566-0.
9. Wang Y., Shi D., Tan Z. et al. Screening Referable Diabetic Retinopathy Using a Semi-automated Deep Learning Algorithm Assisted Approach. Front Med (Lausanne). 2021;8:740987. DOI: 10.3389/fmed.2021.740987.
10. Каталевская Е.А., Каталевский Д.Ю., Тюриков М.И. и др. Алгоритм сегментации визуальных признаков диабетической ретинопатии и диабетического макулярного отека на цифровых фотографиях глазного дна. Российский журнал телемедицины и электронного здравоохранения. 2021;7(4):17–26. DOI: 10.29188/2712-9217-2021-7-4-17-26. [Katalevskaya E.A., Katalevsky D.Yu., Tyurikov M.I. et al. Algorithm for Segmentation of visual signs of diabetic retinopathy and diabetic macular edema in digital fundus images. Russian Journal of Telemedicine and E-Health. 2021;7(4)17–26 (in Russ.)]. DOI: 10.29188/2712-9217-2021-7-4-17-26.
11. World Health Organization. Global Diabetes Report. (Electronic resource.) URL: https://www.who.int/health-topics/diabetes#tab=tab_1 (access date: 28.01.2022).
12. Ogurtsova K., da Rocha Fernandes J.D., Huang Y. et al. IDF Diabetes Atlas: Global estimates for the prevalence of diabetes for 2015 and 2040. Diabetes Res Clin Pract. 2017;128:40–50. DOI: 10.1016/j.diabres.2017.03.024.
13. Beagley J., Guariguata L., Weil C., Motala A.A. Global estimates of undiagnosed diabetes in adults. Diabetes Res Clin Pract. 2014;103(2):150–160. DOI: 10.1016/j.diabres.2013.11.001.
14. Diabetic retinopathy screening: a short guide. Increase effectiveness, maximize benefits and minimize harm. Copenhagen: WHO Regional Office for Europe; 2020. (Electronic resource.) URL: https://apps.who.int/iris/bitstream/handle/10665/336660/9789289055321-eng.pdf?sequence=1&isAllowed=y (access date: 25.01.2022).
15. Digital diagnostics. IDx-DR. (Electronic resource.) URL: https://www.digitaldiagnostics.com/products/eye-disease/idx-dr/ (access date: 24.01.2022).
16. EYENUK. Immediate, Fully-automated, On-site Diabetic Retinopathy Screening. (Electronic resource.) URL: https://www.eyenuk.com/en/products/eyeart/ (access date: 24.01.2022).
17. Chakroborty S., Gupta M., Devishamani C.S. et al. Narrative review of artificial intelligence in diabetic macular edema: Diagnosis and predicting treatment response using optical coherence tomography. Indian J Ophthalmol. 2021;69(11):2999–3008. DOI: 10.4103/ijo.IJO_1482_21.
18. Hwang D.K., Chou Y.B., Lin T.C. et al. Optical coherence tomography-based diabetic macula edema screening with artificial intelligence. J Chin Med Assoc. 2020;83(11):1034–1038. DOI: 10.1097/JCMA.0000000000000351.
19. Wang Z., Zhang W., Sun Y. et al. Detection of Diabetic Macular Edema in Optical Coherence Tomography Image Using an Improved Level Set Algorithm. Biomed Res Int. 2020;2020:6974215. DOI: 10.1155/2020/6974215.
20. Семенова Н.С. Оптическая когерентная томография: от спектральной к swept source. Атлас избранных клинических случаев. Под ред. Акопяна В.С. М.; 2019. [Semenova N.S. Optical coherence tomography: from spectral to swept source. Atlas of selected clinical cases. Ed. Akopyan V.S. M.; 2019 (in Russ.)].
21. Saha S., Nassisi M., Wang M. et al. Automated detection and classification of early AMD biomarkers using deep learning. Sci Rep. 2019;9(1):10990. DOI: 10.1038/s41598-019-47390-3.
22. Schmidt-Erfurth U., Bogunovic H., Grechenig C. et al. Role of Deep Learning-Quantified Hyperreflective Foci for the Prediction of Geographic Atrophy Progression. Am J Ophthalmol. 2020;216:257–270. DOI: 10.1016/j.ajo.2020.03.042.
23. Wilson M., Chopra R., Wilson M.Z. et al. Validation and Clinical Applicability of Whole-Volume Automated Segmentation of Optical Coherence Tomography in Retinal Disease Using Deep Learning. JAMA Ophthalmol. 2021;139(9):964–973. DOI: 10.1001/jamaophthalmol.2021.2273.
24. Prahs P., Märker D., Mayer C., Helbig H. Deep Learning zur Unterstützung der Therapieentscheidung bei intravitrealen Injektionen. Ophthalmologe. 2018;115(9):722–727 (in German.). DOI: 10.1007/s00347-018-0708-y.
25. Bogunovic H., Waldstein S.M., Schlegl T. et al. Prediction of Anti-VEGF Treatment Requirements in Neovascular AMD Using a Machine Learning Approach. Invest Ophthalmol Vis Sci. 2017;58(7):3240–3248. DOI: 10.1167/iovs.16-21053.
26. Schmidt-Erfurth U., Garcia-Arumi J., Bandello F. et al. Guidelines for the Management of Diabetic Macular Edema by the European Society of Retina Specialists (EURETINA). Ophthalmologica. 2017;237(4):185–222. DOI: 10.1159/000458539.
27. De Fauw J., Ledsam J.R., Romera-Paredes B. et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med. 2018;24(9):1342–1350. DOI: 10.1038/s41591-018-0107-6.
28. Kuwayama S., Ayatsuka Y., Yanagisono D. et al. Automated Detection of Macular Diseases by Optical Coherence Tomography and Artificial Intelligence Machine Learning of Optical Coherence Tomography Images. J Ophthalmol. 2019;2019:6319581. DOI: 10.1155/2019/6319581.
29. Takahashi H., Tampo H., Arai Y. et al. Applying artificial intelligence to disease staging: Deep learning for improved staging of diabetic retinopathy. PLoS One. 2017;12(6):e0179790. DOI: 10.1371/journal.pone.0179790.
30. Sramek C., Mackanos M., Spitler R. et al. Non-damaging retinal phototherapy: dynamic range of heat shock protein expression. Invest Ophthalmol Vis Sci. 2011;52(3):1780–1787. DOI: 10.1167/iovs.10-5917.
31. Van Rijssen T.J., van Dijk E.H.C., Yzer S. et al. Central serous chorioretinopathy: Towards an evidence-based treatment guideline. Prog Retin Eye Res. 2019;73:100770. DOI: 10.1016/j.preteyeres.2019.07.003.
32. Luttrull J.K., Chang D.B., Margolis B.W. et al. Laser resensitization of medically unresponsive neovascular age-related macular degeneration: Efficacy and Implications. Retina. 2015;35(6):1184–94. DOI: 10.1097/IAE.0000000000000458.
33. Moisseiev E., Abbassi S., Thinda S. et al. Subthreshold micropulse laser reduces anti-VEGF injection burden in patients with diabetic macular edema. Eur J Ophthalmol. 2018;28(1):68–73. DOI: 10.5301/ejo.5001000.
34. Maswadi S.M., Dodd S.J., Gao J.H., Glickman R.D. Temperature mapping of laser-induced hyperthermia in an ocular phantom using magnetic resonance thermography. J Biomed Opt. 2004;9(4):711–718. DOI: 10.1117/1.1751399.
35. Miura S., Nishiwaki H., Ieki Y. et al. Chorioretinal temperature monitoring during transpupillary thermotherapy for choroidal neovascularisation. Br J Ophthalmol. 2005;89(4):475–479. DOI: 10.1136/bjo.2004.049189.
36. Большунов А.В., Ардамакова А.В., Ларичев А.В. и др. Оптико-акустический мониторинг хориоретинального комплекса при лазерной коагуляции тканей глазного дна. Вестник офтальмологии. 2016;132(6):36–42. [Bol’shunov A.V., Ardamakova A.V., Larichev A.V. et al. Photoacoustic monitoring of chorioretinal complex during laser coagulation of ocular fundus. Bulletin of ophthalmology. 2016,132(6):36–42 (in Russ.)].
37. Pitkänen M., Kaikkonen O., Koskelainen A. A Novel Method for Mouse Retinal Temperature Determination Based on ERG Photoresponses. Ann Biomed Eng. 2017;45(10):2360–2372. DOI: 10.1007/s10439-017-1872-y.
38. Faes L., Liu X., Wagner S.K. et al. A Clinician’s Guide to Artificial Intelligence: How to Critically Appraise Machine Learning Studies. Transl Vis Sci Technol. 2020;9(2):7. DOI: 10.1167/tvst.9.2.7.
39. Liu X., Faes L., Calvert M.J. et al. Extension of the CONSORT and SPIRIT statements. Lancet. 2019;394(10205):1225. DOI: 10.1016/S0140-6736(19)31819-7.
40. Tata Consultancy Services. Getting smarter by the sector: how 13 global industries use artificial intelligence. 2018. (Electronic resource.) URL: https://sites.tcs.com/artificial-intelligence (access date: 20.01.2022).
41. Sullivan H.R., Schweikart S.J. Are Current Tort Liability Doctrines Adequate for Addressing Injury Caused by AI? AMA J Ethics. 2019;21(2):E160–166. DOI: 10.1001/amajethics.2019.160.
42. Shifting Liability: AI in Medical Devices. (Electronic resource.) URL: https://www.crowell.com/NewsEvents/AlertsNewsletters/all/Shifting-Liability-AI-in-Medical-Devices/pdf (access date 20.01.2022).
This work is licensed under a Creative Commons «Attribution» 4.0 License.
