Artificial Intelligence: Applications in Ophthalmology. Part 2
https://doi.org/10.18008/1816-5095-2026-2-223-233
Abstract
Choroidal melanoma is a serious oncological disease that requires timely detection for effective treatment and saving the patient’s life. Traditional diagnostic methods have limitations in sensitivity and accuracy, especially in the early stages of the tumor process. Artificial intelligence (AI) promises to revolutionize ophthalmology, allowing for automated analysis of fundus images and the detection of subtle signs of tumors.
This review article discusses the current capabilities of AI in the early diagnosis of choroidal melanoma. The advantages and limitations of using AI in ophthalmology are analyzed, and existing studies on the development and implementation of AI systems for the diagnosis of fundus tumors are described. The future of AI in ophthalmology and the prospects for the development of this area are discussed.
The article is intended for ophthalmologists, researchers, and specialists in the field of artificial intelligence interested in increasing the efficiency of early diagnosis of choroidal melanoma and improving the vital prognosis of patients.
About the Authors
A. O. Ukina
Gatchina Interdistrict Clinical Hospital
Russian Federation
Russian Federation
Ukina Anastasiia O. - ophthalmologist
Roshchinskaya str., 15a, bld. 1, Gatchina, Leningrad region, 188300
E. B. Myakoshina
Moscow Helmholtz Research Institute of Eye Diseases
Russian Federation
Russian Federation
Myakoshina Elena B. - MD, senior research officer of Ophthalmology and Radiology Department
Sadovaya-Chernogryazskaya str., 14/19, Moscow, 105062
References
1. Maurya RP, Maurya M. Applications of artificial intelligence in ocular oncology. IP Int J Ocul Oncol Oculoplasty. 2022;8(2):84–87. doi: 10.18231/j.ijooo.2022.019.
2. Dimililer K, Ever YK, Ratemi H. Intelligent eye tumour detection system. Procedia Comput. Sci. 2016;102:325–332.
3. Abdulkader H. ITDS: Iris tumor detection system using image processing techniques. Int. J. Sci. Eng. Res. 2014;35:45–80.
4. Jiang Z, Wang L, Wang Y, Jia G, Zeng G, Wang J, Li Y, Chen D, Qian G, Jin Q. A SelfSupervised Learning Based Framework for Eyelid Malignant Melanoma Diagnosis in Whole Slide Images. IEEE/ACM Trans Comput Biol Bioinform. 2024 JulAug;21(4):701–714. doi: 10.1109/TCBB.2022.3207352.
5. Wang L, Ding L, Liu Z, Sun L, Chen L, Jia R, Dai X, Cao J, Ye J. Automated identification of malignancy in whole-slide pathological images: identification of eyelid malignant melanoma in gigapixel pathological slides using deep learning. Br J Ophthalmol. 2020 Mar;104(3):318–323. doi: 10.1136/bjophthalmol-2018-313706.
6. Wang L, Dai X, Liu Z, Zhao Y, Sun Y, Mao B, Wu S, Zhu T, Huang F, Maimaiti N, CaiX, Li SZ, Sheng J, Guo T, Ye J. AI-driven eyelid tumor classification in ocular oncology using proteomic data. NPJ Precis Oncol. 2024 Dec 23;8(1):289. doi: 10.1038/s41698024-00767-8.
7. Bassi A, Krance SH, Pucchio A, Pur DR, Miranda RN, Felfeli T. The Application of Artificial Intelligence in the Analysis of Biomarkers for Diagnosis and Management of Uveitis and Uveal Melanoma: A Systematic Review. Clin Ophthalmol. 2022 Aug 30;16:2895–2908. doi: 10.2147/OPTH.S377358.
8. Chandrabhatla AS, Horgan TM, Cotton CC, Ambati NK, Shildkrot YE. Clinical Applications of Machine Learning in the Management ofIntraocular Cancers: A Narrative Review. Invest Ophthalmol Vis Sci. 2023 Jul 3;64(10):29. doi: 10.1167/iovs.64.10.29.
9. Farabi Maleki S, Yousefi M, Hajiesmailpoor Z, Jafarizadeh A, Pedrammehr S, Alizadehsani R, Gorriz Saez JM. Role of artificial intelligence in ocular tumors: A systematic review. J. Clin. Oncol. 2024;42(16_suppl):e15070–e15070. doi: 10.1200/JCO.2024.42.16_suppl.e15070.
10. Koseoglu ND, Corrêa ZM, Liu TYA. Artificial intelligence for ocular oncology. Curr Opin Ophthalmol. 2023 Sep 1;34(5):437–440. doi: 10.1097/ICU.0000000000000982.
11. Alotaibi SS, Rehman A, Hasnain M. Revolutionizing ocular cancer management: a narrative review on exploring the potential role of ChatGPT. Front Public Health. 2023 Dec 15;11:1338215. doi: 10.3389/fpubh.2023.1338215.
12. Chawla B, Ganesh KB. Applications of artificial intelligence in ocular oncology. Adv. Ophthalmol. Optom. 2023;8(1):111–122. doi: 10.1016/j.yaoo.2023.02.002.
13. Selvam A, Ong J, Bollepalli SC, Chhablani J, Vupparaboina KK, Driban M. Artificial intelligence in choroid through optical coherence tomography: A comprehensive review. Authorea Preprints. 2023. doi: 10.36227/techrxiv.24076470.v2.
14. Goswami M. Deep learning models for benign and malign ocular tumor growth estimation. Comput Med Imaging Graph. 2021 Oct;93:101986. doi: 10.1016/j.compmedimag.2021.101986.
15. Myakoshina EB. Complex diagnostics of small choroidal melanoma. Russian electronic journal of radiology. 2016;6(4):19–28 (In Russ.). doi: 10.21569/2222-74152016-6-4-19-28.
16. Neroyev VV, Saakian SV, Miakoshina EB, Okhotsimskaia TD, Fadeeva VA. Role of optical coherence tomography angiography in diagnostics of early choroidal melanoma and circumscribed choroidal hemangioma. Russian Annals of Ophthalmology. 2018;134(3):4–18 (In Russ.). doi: 10.17116/oftalma201813434.
17. Miakoshina EB, Saakian SV. Optical coherence tomography in diagnostics ofsmall choroidal melanoma. RussianAnnals of Ophthalmology. 2020;136(1):56–64 (InRuss.). doi: 10.17116/oftalma202013601156.
18. Myakoshina EB, Kulikova IG, Balatskaya NV, Katargina LA, Saakyan SV. Predictive model of small choroidal melanoma progression after eye-saving treatment based on clinical, morphometric and immunological parameters. Medical Immunology. 2022;24(1):81–88 (In Russ.). doi: 10.15789/1563-0625-PMO-2180.
19. Saakyan SV, Myakoshina EV, Garry DD, Tarasov GYu, Myakoshin IYu. Computer (Electronic) Model of Vital Prognosis in Patients with Small Choroidal Melanoma. Effective Pharmacotherapy. 2023;19(11):52–56 (In Russ.). doi: 10.33978/23073586-2023-19-11-52-56.
20. Method for predicting patient survival after enucleation for early choroidal melanoma spreading to the optic disc: Saakyan SV, Myakoshina EB, Zakharova GP, Garry DD, Tarasov GYu. Patent RU 2778965, 08.29.2022.
21. Sabazade S, Lumia Michalski MA, Bartoszek J, Fili M, Holmström M, Stålhammar G. Development and Validation of a Deep Learning Algorithm for Differentiation of Choroidal Nevi from Small Melanoma in Fundus Photographs. Ophthalmol Sci. 2024 Aug 30;5(1):100613. doi: 10.1016/j.xops.2024.100613.
22. Hoffmann L, Runkel CB, Künzel S, Kabiri P, Rübsam A, Bonaventura T, Marquardt P, Haas V, Biniaminov N, Biniaminov S, Joussen AM, Zeitz O. Using Deep Learning to Distinguish Highly Malignant Uveal Melanoma from Benign Choroidal Nevi. J Clin Med. 2024 Jul 16;13(14):4141. doi: 10.3390/jcm13144141.
23. Allam E, Alfonse M, Salem ABM. Artificial intelligence techniques for classification of eye tumors: a survey. In: 2022 5th Int. Conf. Comput. Informat. (ICCI). IEEE, 2022:175–179. doi: 10.1109/ICCI54321.2022.9756067.
24. Ma J, Iddir SP, Ganesh S, Yi D, Heiferman MJ. Automated segmentation for early detection of uveal melanoma. Can J Ophthalmol. 2024 Dec;59(6):e784–e791. doi: 10.1016/j.jcjo.2024.04.003.
25. Poovizhi P. An approach towards the detection of ocular cancer using Fast R-CNN technique. In: 2023 8th Int. Conf. Commun. Electron. Syst. (ICCES). IEEE, 2023:1683–1688. doi: 10.1109/ICCES57224.2023.10192725.
26. Shakeri E. Deep learning-based detection and classification of uveal melanoma using convolutional neural networks and SHAP analysis. In: 2023 IEEE 24th Int. Conf. Inf. Reuse Integr. Data Sci. (IRI). IEEE, 2023:215–220. doi: 10.1109/IRI58017.2023.00044.
27. Wu CT, Lin TY, Lin CJ, Hwang DK. The future application of artificial intelligence and telemedicine in the retina: A perspective. Taiwan J Ophthalmol. 2023 Jun 13;13(2):133– 141. doi: 10.4103/tjo.TJO-D-23-00028. PMID: 37484624; PMCID: PMC10361422.
28. Katalevskaya EA, Katalevskiy DYu, Tyurikov MI. Future of artificial intelligence for the diagnosis and treatment ofretinal diseases. Russian Journal of Clinical Ophthalmology. 2022;22(1):36–43 (In Russ.). doi: 10.32364/2311-7729-2022-22-1-36-43.
29. Li Z, Qiang W, Chen H, Pei M, Yu X, Wang L, Li Z, Xie W, Wu X, Jiang J, Wu G. Artificial intelligence to detect malignant eyelid tumors from photographic images. NPJ Digit Med. 2022 Mar 2;5(1):23. doi: 10.1038/s41746-022-00571-3.
30. Kaiserman I, Rosner M, Pe’er J. Forecasting the prognosis of choroidal melanoma with an artificial neural network. Ophthalmology. 2005 Sep;112(9):1608. doi: 10.1016/j.ophtha.2005.04.008.
31. Vaquero-Garcia J, Lalonde E, Ewens KG, Ebrahimzadeh J, Richard-Yutz J, Shields CL, Barrera A, Green CJ, Barash Y, Ganguly A. PRiMeUM: A Model for Predicting Risk of Metastasis in Uveal Melanoma. Invest Ophthalmol Vis Sci. 2017 Aug 1;58(10):4096–4105. doi: 10.1167/iovs.17-22255.
32. Sun M, Zhou W, Qi X, Zhang G, Girnita L, Seregard S, Grossniklaus HE, Yao Z, Zhou X, Stålhammar G. Prediction of BAP1 Expression in Uveal Melanoma Using Densely-Connected Deep Classification Networks. Cancers (Basel). 2019 Oct 16;11(10):1579. doi: 10.3390/cancers11101579/
33. Luo J, Chen Y, Yang Y, Zhang K, Liu Y, Zhao H, Dong L, Xu J, Li Y, Wei W. Prognosis Prediction of Uveal Melanoma After Plaque Brachytherapy Based on Ultrasound With Machine Learning. Front Med (Lausanne). 2022 Jan 21;8:777142. doi: 10.3389/fmed.2021.777142.
34. Zhang H, Liu Y, Zhang K, Hui S, Feng Y, Luo J, Li Y, Wei W. Validation of the Relationship Between Iris Color and Uveal Melanoma Using Artificial Intelligence With Multiple Paths in a Large Chinese Population. Front Cell Dev Biol. 2021 Aug 19;9:713209. doi: 10.3389/fcell.2021.713209.
35. Chen YN, Wang YN, Chen MX, Zhang K, Chen RT, Fang R, Wang H, Zhang HH, Huang YN, Feng Y, Luo JT, Lan YJ, Liu YM, Li Y, Wei WB. Machine learning models for outcome prediction of Chinese uveal melanoma patients: A 15-year follow-up study. Cancer Commun (Lond). 2022 Mar;42(3):273–276. doi: 10.1002/cac2.12253.
36. Donizy P, Krzyzinski M, Markiewicz A, Karpinski P, Kotowski K, Kowalik A, Orlowska-Heitzman J, Romanowska-Dixon B, Biecek P, Hoang MP. Machine learning models demonstrate that clinicopathologic variables are comparable to gene expression prognostic signature in predicting survival in uveal melanoma. Eur J Cancer. 2022 Oct;174:251–260. doi: 10.1016/j.ejca.2022.07.031.
37. Zabor EC, Raval V, Luo S, Pelayes DE, Singh AD. A Prediction Model to Discriminate Small Choroidal Melanoma from Choroidal Nevus. Ocul Oncol Pathol. 2022 Feb;8(1):71–78. doi: 10.1159/000521541.
38. Liu TYA, Chen H, Gomez C, Correa ZM, Unberath M. Direct Gene Expression Profile Prediction for Uveal Melanoma from Digital Cytopathology Images via Deep Learning and Salient Image Region Identification. Ophthalmol Sci. 2022 Oct 30;3(1):100240. doi: 10.1016/j.xops.2022.100240.
39. Rivas-Perea P, Baker E, Hamerly G, Shaw BF. Detection of leukocoria using a soft fusion of expert classifiers under non-clinical settings. BMC Ophthalmol. 2014 Sep 9;14:110. doi: 10.1186/1471-2415-14-110.
40. Strijbis VIJ, de Bloeme CM, Jansen R, Kebiri H. Multi-view convolutional neural networks for automated ocular structure and tumor segmentation in retinoblastoma. Sci. Rep. 2021;11(1):14590. doi: 10.1038/s41598-021-93905-2.
41. Tan E, Lin F, Sheck L, Salmon P, Ng S. A practical decision-tree model to predict complexity of reconstructive surgery after periocular basal cell carcinoma excision. J Eur Acad Dermatol Venereol. 2017 Apr;31(4):717–723. doi: 10.1111/jdv.14012.
42. HabibalahiA, BalaC, AllendeA, AnwerAG, Goldys EM. Novel automated non invasive detection of ocular surface squamous neoplasia using multispectral autofluorescence imaging. Ocul Surf. 2019 Jul;17(3):540–550. doi: 10.1016/j.jtos.2019.03.003.
43. Yoo TK, Choi JY, Kim HK, Ryu IH, Kim JK. Adopting low-shot deep learning forthe detection of conjunctival melanoma using ocular surface images. Comput Methods Programs Biomed. 2021 Jun;205:106086. doi: 10.1016/j.cmpb.2021.106086.
Review
For citations:
Ukina A.O., Myakoshina E.B. Artificial Intelligence: Applications in Ophthalmology. Part 2. Ophthalmology in Russia. 2026;23(2):223-233. (In Russ.) https://doi.org/10.18008/1816-5095-2026-2-223-233
Views:
16
JATS XML
Email this article 