Digital DiagnosticsDigital DiagnosticsDigital DiagnosticsDigital Diagnostics2712-84902712-8962Eco-Vector62413110.17816/DD624131Original Study ArticlesОригинальные исследования原创性科研成果Research ArticleExploring the possibilities of an artificial intelligence program in the diagnosis of macular diseasesИзучение возможностей программы искусственного интеллекта в диагностике заболеваний макулярной области人工智能程序在黄斑疾病诊断中的可行性研究https://orcid.org/0000-0002-7770-575X2736-9089KhabazovaMargarita R.ХабазоваМаргарита РобертовнаKhabazovaMargarita R.
Russian Federation
rita.khabazova@mail.ruhttps://orcid.org/0009-0001-0828-98447868-4425PonomarevaElena N.ПономареваЕлена НиколаевнаPonomarevaElena N.
Russian Federation
ponomareva.en@fnkc-fmba.ruhttps://orcid.org/0000-0003-0057-33385845-6058LoskutovIgor A.ЛоскутовИгорь АнатольевичLoskutovIgor A.
Russian Federation

MD, Dr. Sci. (Medicine)

д-р мед. наук

MD, Dr. Sci. (Medicine)

loskoutigor@mail.ruhttps://orcid.org/0000-0002-5710-92057849-8890KatalevskayaEvgenia А.КаталевскаяЕвгения АлексеевнаKatalevskayaEvgenia А.
Russian Federation

MD, Cand. Sci. (Medicine)

канд. мед. наук

MD, Cand. Sci. (Medicine)

ekatalevskaya@mail.ruhttps://orcid.org/0000-0003-3338-40154468-1730SizovAlexander Yu.СизовАлександр ЮрьевичSizovAlexander Yu.
Russian Federation
sizov_ost_vk@mail.ruhttps://orcid.org/0000-0002-0759-31071802-3224GabaraevGeorgiy М.ГабараевГеоргий МалхазовичGabaraevGeorgiy М.
Russian Federation
geor_gabaraev1@mail.ruFederal Research and Clinical Center of Specialized Medical Care and Medical TechnologiesФедеральный научно-клинический центр специализированных видов медицинской помощи и медицинских технологийFederal Research and Clinical Center of Specialized Medical Care and Medical TechnologiesMoscow Regional Research and Clinical InstituteМосковский областной научно-исследовательский клинический институт имени М.Ф. ВладимирскогоMoscow Regional Research and Clinical InstituteDigital Vision Solutions LLCООО «Диджитал Вижн Солюшнс»Digital Vision Solutions LLCNizhny Novgorod State Technical University n.a. R.E. AlekseevНижегородский государственный технический университет им. Р.Е. АлексееваNizhny Novgorod State Technical University n.a. R.E. Alekseev11032024190420245117283011202312022024Copyright ©; 2024, Eco-VectorCopyright ©; 2024, Эко-векторCopyright ©; 2024, Eco-Vector2024Eco-VectorЭко-векторEco-Vectorhttps://creativecommons.org/licenses/by-nc-nd/4.0/https://jdigitaldiagnostics.com/DD/article/view/624131

BACKGROUND: Macular diseases are a large group of pathological conditions that cause vision loss and visual impairment. Early diagnosis of such changes plays an important role in treatment selection and is one of the crucial factors in predicting outcomes.

AIM: To examine the potential of an artificial intelligence program in the diagnosis of macular diseases using structural optical coherence tomography scans.

MATERIALS AND METHODS: The study included patients examined and treated at the Federal Research and Clinical Center of Specialized Medical Care and Medical Technologies and Moscow Regional Research and Clinical Institute. In total, 200 eyes with macular diseases were examined, as well as eyes without macular pathologies. A comparative clinical analysis of structural optical coherence tomography scans obtained using an RTVue XR 110-2 tomograph was conducted. The Retina.AI software was used to analyze optical coherence tomography scans.

RESULTS: In the analysis of optical coherence tomography scans using Retina.AI, various pathological structures of the macula were identified, and a probable pathology was then determined. The results were compared with the diagnoses made by ophthalmologists. The sensitivity, specificity, and accuracy of the method were 95.16%, 97.76%, and 97.38%, respectively.

CONCLUSION: Retina.AI allows ophthalmologists to automatically analyze optical coherence tomography scans and identify various pathological conditions of the fundus.

Обоснование. Заболевания макулярной области представляют собой большую группу патологических состояний, приводящих к потере зрения и слабовидению. Ранняя диагностика таких изменений играет большую роль в выборе тактики лечения и является одной из определяющих в прогнозировании результатов.

Цель — изучить возможности программы искусственного интеллекта в диагностике заболеваний макулярной области на основе анализа сканов структурной оптической когерентной томографии.

Материалы и методы. В исследование были включены пациенты, проходившие обследование и лечение в Федеральном научно-клиническом центре специализированных видов медицинской помощи и медицинских технологий и Московского областного научно-исследовательского клинического института им. М.Ф. Владимирского. Обследовано 200 глаз с заболеваниями макулярной области, а также глаза без макулярной патологии. Проведён сравнительный клинический анализ сканов структурной оптической когерентной томографии, выполненных на офтальмологическом томографе RTVue XR 110-2. Для анализа сканов оптической когерентной томографии использовалось программное обеспечение Retina.AI.

Результаты. В ходе анализа сканов оптической когерентной томографии с помощью программы были выявлены различные патологические структуры макулярной области, а затем сформулировано заключение о вероятной патологии. Полученные результаты сравнивались с заключениями врачей-офтальмологов. Чувствительность метода составила 95,16%; специфичность — 97,76%; точность — 97,38%.

Заключение. Платформа Retina.AI позволяет офтальмологам успешно проводить автоматизированный анализ сканов структурной оптической когерентной томографии и выявлять различные патологические состояния глазного дна.

论证。黄斑疾病是一大类病症。它们会导致视力丧失和视力低下。对这些病变的早期诊断对治疗策略的选择起着重要作用，它是疗效预测的决定性因素之一。

目的。本研究的目的是研究人工智能程序在基于对结构光学相干断层扫描图片的分析诊断黄斑疾病方面的可行性。

材料与方法。本研究对象包括在俄罗斯联邦医疗和生物局联邦专业医疗救护和医疗技术科学与临床中心以及以M.F.弗拉基米尔斯基莫斯科州临床研究所接受检查和治疗的患者。对200只有黄斑病变的眼和无黄斑病变的眼进行了检查。对RTVue XR 110-2眼科断层扫描仪上的结构光学相干断层扫描进行了临床对比分析。利用Retina.AI软件对光学相干断层扫描进行分析。

结果。使用该程序分析光学相干断层扫描图片时，确定了黄斑区的各种病理结构。此外，还得出了关于可能病理的结论。对获得的结果与眼科医生的结论进行了比较。该方法的灵敏度为95.16%；特异性为97.76%；准确率为97.38%。

结论。Retina.AI平台使眼科医生能够成功地对结构光学相干断层扫描图片进行自动分析，并检测眼底的各种病理状态。

optical coherence tomographyartificial intelligencediagnosismacular edemaage-related macular degenerationоптическая когерентная томографияискусственный интеллектвитреомакулярный интерфейсдиагностикадиабетический макулярный отёквозрастная макулярная дегенерация光学相干断层扫描人工智能玻璃体黄斑界面诊断糖尿病性黄斑水肿老年性黄斑变性Report of the 2030 targets on effective coverage of eye care [Internet]. Geneva: World Health Organization. c2024. [cited 2023 Jan 1]. Available from: https://www.who.int/publications/i/item/9789240058002Report of the 2030 targets on effective coverage of eye care [Internet]. Geneva : World Health Organization. c2024. [дата обращения: 1.01.2023]. Доступ по ссылке: https://www.who.int/publications/i/item/9789240058002Report of the 2030 targets on effective coverage of eye care [Internet]. Geneva: World Health Organization. c2024. [cited 2023 Jan 1]. Available from: https://www.who.int/publications/i/item/9789240058002GBD 2019 Blindness and Vision Impairment Collaborators. 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: an analysis for the Global Burden of Disease Study. Lancet Glob Health. 2021;9(2):144–160. doi: 10.1016/S2214-109X(20)30489-7GBD 2019 Blindness and Vision Impairment Collaborators. 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: an analysis for the Global Burden of Disease Study // Lancet Glob Health. 2021. Vol. 9, N 2. P. 144–160. doi: 10.1016/S2214-109X(20)30489-7GBD 2019 Blindness and Vision Impairment Collaborators. 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: an analysis for the Global Burden of Disease Study. Lancet Glob Health. 2021;9(2):144–160. doi: 10.1016/S2214-109X(20)30489-7Samanta A, Aziz AA, Jhingan M, et al. Emerging Therapies in Neovascular Age-Related Macular Degeneration in 2020. Asia Pac J Ophthalmol (Phila). 2020;9(3):250–259. doi: 10.1097/APO.0000000000000291Samanta A., Aziz A.A., Jhingan M., et al. Emerging Therapies in Neovascular Age-Related Macular Degeneration in 2020 // Asia Pac J Ophthalmol (Phila). 2020. Vol. 9, N 3. P. 250–259. doi: 10.1097/APO.0000000000000291Samanta A, Aziz AA, Jhingan M, et al. Emerging Therapies in Neovascular Age-Related Macular Degeneration in 2020. Asia Pac J Ophthalmol (Phila). 2020;9(3):250–259. doi: 10.1097/APO.0000000000000291Stahl A. The Diagnosis and Treatment of Age-Related Macular Degeneration. Dtsch Arztebl Int. 2020;117:513–520. doi: 10.3238/arztebl.2020.0513Stahl A. The Diagnosis and Treatment of Age-Related Macular Degeneration // Dtsch Arztebl Int. 2020. Vol. 117. P. 513–520. doi: 10.3238/arztebl.2020.0513Stahl A. The Diagnosis and Treatment of Age-Related Macular Degeneration. Dtsch Arztebl Int. 2020;117:513–520. doi: 10.3238/arztebl.2020.0513Teo ZL, Tham YC, Yu M, et al. Global Prevalence of Diabetic Retinopathy and Projection of Burden through 2045: Systematic Review and Meta-analysis. Ophthalmology. 2021;128(11):1580–1591. doi: 10.1016/j.ophtha.2021.04.027Teo Z.L., Tham Y.C., Yu M., et al. Global Prevalence of Diabetic Retinopathy and Projection of Burden through 2045: Systematic Review and Meta-analysis // Ophthalmology. 2021. Vol. 128, N 11. P. 1580–1591. doi: 10.1016/j.ophtha.2021.04.027Teo ZL, Tham YC, Yu M, et al. Global Prevalence of Diabetic Retinopathy and Projection of Burden through 2045: Systematic Review and Meta-analysis. Ophthalmology. 2021;128(11):1580–1591. doi: 10.1016/j.ophtha.2021.04.027Schaal S, Kaplan HJ, editors. Cystoid Macular Edema. Switzerland: Springer International Publishing; 2017. doi: 10.1007/978-3-319-39766-5Schaal S., Kaplan H.J., editors. Cystoid Macular Edema. Switzerland : Springer International Publishing, 2017. doi: 10.1007/978-3-319-39766-5Schaal S, Kaplan HJ, editors. Cystoid Macular Edema. Switzerland: Springer International Publishing; 2017. doi: 10.1007/978-3-319-39766-5Bikbov MM, Fayzrakhmanov RR, Zaynullin RM, et al. Macular oedema as manifestation of diabetic retinopathy. Diabetes mellitus. 2017;20(4):263–269. EDN: ZMZAON doi: 10.14341/DM8328Бикбов М.М., Файзрахманов Р.Р., Зайнуллин Р.М., и др. Макулярный отёк как проявление диабетической ретинопатии // Сахарный диабет. 2017. Т. 20, № 4. С. 263–269. EDN: ZMZAON doi: 10.14341/DM8328Bikbov MM, Fayzrakhmanov RR, Zaynullin RM, et al. Macular oedema as manifestation of diabetic retinopathy. Diabetes mellitus. 2017;20(4):263–269. EDN: ZMZAON doi: 10.14341/DM8328Chernykh DV, Chernykh VV, Trunov AN. Cytokines and growth factors in the pathogenesis of proliferative diabetic retinopathy. Moscow: Oftal’mologiya; 2017. EDN: ZNDEWHЧерных Д.В., Черных В.В., Трунов А.Н. Цитокины и факторы роста в патогенезе пролиферативной диабетической ретинопатии. Москва : Офтальмология, 2017. EDN: ZNDEWHChernykh DV, Chernykh VV, Trunov AN. Cytokines and growth factors in the pathogenesis of proliferative diabetic retinopathy. Moscow: Oftal’mologiya; 2017. EDN: ZNDEWHGupta A, Tripathy K. Central Serous Chorioretinopathy [Internet]. [Updated 2022 Aug 22]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing, 2022. Available from: https://www.statpearls.com/point-of-care/96027Gupta A., Tripathy K. Central Serous Chorioretinopathy [Internet]. [Updated 2022 Aug 22]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing, 2022. Доступ по ссылке: https://www.statpearls.com/point-of-care/96027Gupta A, Tripathy K. Central Serous Chorioretinopathy [Internet]. [Updated 2022 Aug 22]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing, 2022. Available from: https://www.statpearls.com/point-of-care/96027Semeraro F, Morescalchi F, Russo A, et al. Central Serous Chorioretinopathy: Pathogenesis and Management. Clinical ophthalmology. 2019;13:2341–2352. doi: 10.2147/OPTH.S220845Semeraro F., Morescalchi F., Russo A., et al. Central Serous Chorioretinopathy: Pathogenesis and Management // Clinical ophthalmology. 2019. Vol. 13. P. 2341–2352. doi: 10.2147/OPTH.S220845Semeraro F, Morescalchi F, Russo A, et al. Central Serous Chorioretinopathy: Pathogenesis and Management. Clinical ophthalmology. 2019;13:2341–2352. doi: 10.2147/OPTH.S220845Oh KT, Lazzaro DR, editors. Macular Hole. [Internet]. Medscape, 2020. [cited 2020 Jan 02]. Available from: https://emedicine.medscape.com/article/1224320-overview#a6Oh K.T., Lazzaro D.R., editors. Macular Hole. [Internet]. Medscape, 2020. [Дата обращения: 02.01.2020]. Доступ по ссылке: https://emedicine.medscape.com/article/1224320-overview#a6Oh KT, Lazzaro DR, editors. Macular Hole. [Internet]. Medscape, 2020. [cited 2020 Jan 02]. Available from: https://emedicine.medscape.com/article/1224320-overview#a6Darian-Smith E, Howie AR, Allen PL, et al. Tasmanian macular hole study: whole population-based incidence of full thickness macular hole. Clinical & Experimental Ophthalmology. 2016;44(9):812–816. doi: 10.1111/ceo.12801Darian-Smith E., Howie A.R., Allen P.L., et al. Tasmanian macular hole study: whole population-based incidence of full thickness macular hole // Clin Exp Ophthalmol. 2016. Vol. 44, N 9. P. 812–816. doi: 10.1111/ceo.12801Darian-Smith E, Howie AR, Allen PL, et al. Tasmanian macular hole study: whole population-based incidence of full thickness macular hole. Clinical & Experimental Ophthalmology. 2016;44(9):812–816. doi: 10.1111/ceo.12801Fung AT, Galvin J, Tran T. Epiretinal membrane: A review. Clinical & Experimental Ophthalmology. 2021;49:289–308. doi: 10.1111/ceo.13914Fung A.T., Galvin J., Tran T. Epiretinal membrane: A review // Clin Experiment Ophthalmol. 2021. Vol. 49. P. 289–308. doi: 10.1111/ceo.13914Fung AT, Galvin J, Tran T. Epiretinal membrane: A review. Clinical & Experimental Ophthalmology. 2021;49:289–308. doi: 10.1111/ceo.13914Oh KT, Lazzaro DR, editors. Epiretinal Membrane [Internet]. Medscape, 2020. [cited 2020 Jan 02]. Available from: https://emedicine.medscape.com/article/1223882-overview#a4Oh K.T., Lazzaro D.R., editors. Epiretinal Membrane [Internet]. Medscape, 2020. [Дата обращения: 02.01.2020]. Доступ по ссылке: https://emedicine.medscape.com/article/1223882-overview#a4Oh KT, Lazzaro DR, editors. Epiretinal Membrane [Internet]. Medscape, 2020. [cited 2020 Jan 02]. Available from: https://emedicine.medscape.com/article/1223882-overview#a4World Health Organization. Regional Office for Europe. Screening for diabetic retinopathy: a short guide. Increase effectiveness, maximize benefits and minimize harm [Internet]. Copenhagen; 2021. [cited 2020 Jan 02]. Available from: https://www.who.int/europe/publications/i/item/9789289055321Всемирная организация здравоохранения. Европейское региональное бюро. Скрининг на диабетическую ретинопатию: Повышение эффективности, максимальное увеличение пользы и минимизация вреда, краткое руководство [Internet]. Копенгаген, 2021. [Дата обращения: 02.01.2020]. Доступ по ссылке: https://www.who.int/europe/publications/i/item/9789289055321World Health Organization. Regional Office for Europe. Screening for diabetic retinopathy: a short guide. Increase effectiveness, maximize benefits and minimize harm [Internet]. Copenhagen; 2021. [cited 2020 Jan 02]. Available from: https://www.who.int/europe/publications/i/item/9789289055321Qassimi AN, Kozak I, Karam AM, et al. Management of Diabetic Macular Edema: Guidelines from the Emirates Society of Ophthalmology. Ophthalmology and therapy. 2022;11:1937–1950. doi: 10.1007/s40123-022-00547-2Qassimi A.N., Kozak I., Karam A.M., et al. Management of Diabetic Macular Edema: Guidelines from the Emirates Society of Ophthalmology // Ophthalmology and therapy. 2022. Vol. 11. P. 1937–1950. doi: 10.1007/s40123-022-00547-2Qassimi AN, Kozak I, Karam AM, et al. Management of Diabetic Macular Edema: Guidelines from the Emirates Society of Ophthalmology. Ophthalmology and therapy. 2022;11:1937–1950. doi: 10.1007/s40123-022-00547-2Katalevskaya EA, Katalevskiy DYu, Tyurikov MI, Velieva IA, Bol’shunov AV. Future of artificial intelligence for the diagnosis and treatment of retinal diseases. Russian journal of clinical ophthalmology. 2022;22(1):36–43. EDN: AEBQGU doi: 10.32364/2311-7729-2022-22-1-36-43Каталевская Е.А., Каталевский Д.Ю., Тюриков М.И., Велиева И.А., Большунов А.В. Перспективы использования искусственного интеллекта в диагностике и лечении заболеваний сетчатки // РМЖ. Клиническая офтальмология. 2022. Т. 22, № 1. С. 36–43. EDN: AEBQGU doi: 10.32364/2311-7729-2022-22-1-36-43Katalevskaya EA, Katalevskiy DYu, Tyurikov MI, Velieva IA, Bol’shunov AV. Future of artificial intelligence for the diagnosis and treatment of retinal diseases. Russian journal of clinical ophthalmology. 2022;22(1):36–43. EDN: AEBQGU doi: 10.32364/2311-7729-2022-22-1-36-43Schmidt-Erfurth U, Reiter GS, Riedl S, et al. AI-based monitoring of retinal fluid in disease activity and under therapy. Prog Retin Eye Res. 2022;86. doi: 10.1016/j.preteyeres.2021.100972Schmidt-Erfurth U., Reiter G.S., Riedl S., et al. AI-based monitoring of retinal fluid in disease activity and under therapy // Prog Retin Eye Res. 2022. Vol. 86. doi: 10.1016/j.preteyeres.2021.100972Schmidt-Erfurth U, Reiter GS, Riedl S, et al. AI-based monitoring of retinal fluid in disease activity and under therapy. Prog Retin Eye Res. 2022;86. doi: 10.1016/j.preteyeres.2021.100972Altris.ai [Internet]. United States: Altris Inc. [cited 2022 Jan 01]. Available from: https://www.altris.aiAltris.ai [Internet]. United States : Altris Inc. [Дата обращения: 01.01.2022]. Доступ по ссылке: https://www.altris.aiAltris.ai [Internet]. United States: Altris Inc. [cited 2022 Jan 01]. Available from: https://www.altris.aiMalyugin BE, Sakhnov SN, Axenova LE, et al. A deep machine learning model development for the biomarkers of the anatomical and functional anti-VEGF therapy outcome detection on retinal OCT images. Fyodorov Journal of Ophthalmic Surgery. 2022;(S4):77–84. EDN: OWQLRM doi: 10.25276/0235-4160-2022-4S-77-84Малюгин Б.Э., Сахнов С.Н., Аксенова Л.Е., и др. Разработка модели глубокого машинного обучения для обнаружения биомаркёров анатомического и функционального исхода анти-VEGF-терапии на ОКТ-изображениях сетчатки // Офтальмохирургия. 2022. № S4. С. 77–84. EDN: OWQLRM doi: 10.25276/0235-4160-2022-4S-77-84Malyugin BE, Sakhnov SN, Axenova LE, et al. A deep machine learning model development for the biomarkers of the anatomical and functional anti-VEGF therapy outcome detection on retinal OCT images. Fyodorov Journal of Ophthalmic Surgery. 2022;(S4):77–84. EDN: OWQLRM doi: 10.25276/0235-4160-2022-4S-77-84