Обзор технологий визуализации глазного дна

Визуализация глазного дна играет решающую роль в диагностике и мониторинге заболеваний сетчатки, зрительного нерва и хориоидеи. Технологии визуализации глазного дна за последние десятилетия прошли путь от простых офтальмоскопов до высокоточных томографов и многоспектральных систем. В обзоре рассмотрены принципы, преимущества и ограничения основных технологий визуализации глазного дна. Современные разработки направлены на повышение их портативности, автоматизацию и доступность для широкого применения.

1. GBD 2019 Blindness and Vision Impairment Collaborators; Vision Loss Expert Group of the Global Burden of Disease Study. Causes of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: the Right to Sight. The Lancet Global Health. 2021; 9 (2): e144–e160. doi: 10.1016/S2214-109X(20)30489-7

2. Cen LP, Ji J, Lin JW, et al. Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks. Nat Commun. 2021; 12, 4828. https://doi.org/10.1038/s41467-021-25138-w

3. Cordero I. Understanding and caring for an indirect ophthalmoscope. Community Eye Health. 2016; 29 (95): 57. PMID: 28289323.

4. Shanmugam MP, Mishra DK, Rajesh R, Madhukumar R. Unconventional techniques of fundus imaging: A review. Indian J Ophthalmol. 2015 Jul; 63 (7): 582–5. doi: 10.4103/0301-4738.167123

5. Panwar N, Huang P, Lee J, et al. Fundus photography in the 21st century — A Review of recent technological advances and their implications for worldwide healthcare. Telemed J E Health. 2016 Mar; 22 (3): 198–208. doi: 10.1089/tmj.2015.0068

6. Nunez do Rio JM, Sen P, Rasheed R, et al. Deep learning-based segmentation and quantification of retinal capillary non-perfusion on ultra-wide-field retinal fluorescein angiography. J Clin Med. 2020 Aug 6; 9 (8): 2537. doi: 10.3390/jcm9082537

7. Mainster MA, Desmettre T, Querques G, Turner PL, Ledesma-Gil G. Scanning laser ophthalmoscopy retroillumination: applications and illusions. Int J Retina Vitreous. 2022 Sep 30; 8 (1): 71. https://doi.org/10.1186/s40942-022-00421-0

8. Patel CK, Buckle M. Ultra-widefield imaging for pediatric retinal disease. Asia Pac J Ophthalmol (Phila). 2018 May-Jun; 7 (3): 208–14. doi: 10.22608/APO.2018100

9. Antropoli A, Arrigo A, La Franca L, et al. Peripheral and central capillary non-perfusion in diabetic retinopathy: An updated overview. Front Med. 2023; 10: 1125062. doi: 10.3389/fmed.2023.1125062

10. Ashrafkhorasani M, Habibi A, Nittala MG, et al. Peripheral retinal lesions in diabetic retinopathy on ultra-widefield imaging. Saudi J Ophthalmol. 2024 Jan 6; 38 (2): 123–31. doi: 10.4103/sjopt.sjopt_151_23

11. Brown K, Keane D, Turner L, Peto T. Impact of ultra-widefield imaging on understanding the pathophysiology of peripheral retinal degeneration. Medical Research Archives. 2024 aug.; 12 (8). https://doi.org/10.18103/mra.v12i8.5507

12. Huang D, Swanson EA, Lin CP, et al. Optical Coherence Tomography. Science. 1991; 254:1178-81. doi: 10.1126/science.1957169

13. Lu CD, Kraus MF, Potsaid BJ, et al. Handheld ultrahigh speed swept source optical coherence tomography instrument using a MEMS scanning mirror. Biomed Opt Express. 2013 Dec 20; 5 (1): 293–311. doi: 10.1364/BOE.5.000293

14. Jung W, Kim J, Jeon M, et al. Handheld optical coherence tomography scanner for primary care diagnostics. IEEE Trans Biomed Eng. 2011 Mar; 58 (3): 741–4. doi: 10.1109/TBME.2010.2096816

15. Roorda A. Applications of adaptive optics scanning laser ophthalmoscopy. Optometry and Vision Science. 2010 April; 87 (4): 260–8. doi: 10.1097/OPX.0b013e3181d39479

16. DuBose T, Nankivil D, LaRocca F, et al. Handheld adaptive optics scanning laser ophthalmoscope. Optica. 2018 Sep 20; 5 (9): 1027–36. doi: 10.1364/OPTICA.5.001027

17. Maman R, Mualem E, Mazurski N, Engelberg J, Levy U. Achromatic imaging systems with flat lenses enabled by deep learning. ACS Photonics. 2023; 10 (12): 4494–500. https://pubs.acs.org/doi/10.1021/acsphotonics.3c01349

18. Wroblewski JJ, Sanchez-Buenfil E, Inciarte M, et al. Diabetic retinopathy screening using smartphone-based fundus photography and deep-learning artificial intelligence in the Yucatan peninsula: A field study. J Diabetes Sci Technol. 2025 Mar; 19 (2): 370–6. doi:10.1177/19322968231194644

19. Penha FM, Priotto BM, Hennig F, et al. Single retinal image for diabetic retinopathy screening: performance of a handheld device with embedded artificial intelligence. Int J Retin Vitreos. 2023 Jul 10; 9 (1): 41. doi: 10.1186/s40942-023-00477-6