ophthalmologyОфтальмологияOphthalmology in Russia1816-50952500-0845Ophthalmology10.18008/1816-5095-2020-1-20-31ophthalmology-1127Research ArticleОБЗОРЫREVIEWSМетоды машинного обучения в офтальмологии. Обзор литературыМethods of Machine Learning in Ophthalmology: Reviewhttps://orcid.org/0000-0001-6385-4925ГарриД. Д.GarriD. D.

Гарри Денис Дмитриевич аспирант кафедры глазных болезней ФДПО, специалист по работе с медицинскими данными

ул. Делегатская, 20, стр. 1, Москва, 127473; ул. Козлова, 30, Москва, 121357

Garri Denis D. рostgraduate student of the eye diseases department, medical data specialist

Delegatskaya str., 20, p. 1, Moscow, 127473; Kozlov str., 30, Moscow, 121357

ldenisl@inbox.ruСаакянС. В.SaakyanS. V.

Саакян Светлана Владимировна доктор медицинских наук, профессор, начальник отдела офтальмоонкологии и радиологии, зав. учебной частью кафедры глазных болезней ФДПО

ул. Садовая-Черногрязская, 14/19, Москва, 105062; ул. Делегатская, 20, стр. 1, Москва, 127473

Saakyan Svetlana V. MD, professor, head of the ophthalmic oncology and radiology department, head of the academic eye diseases department

Sadovaya-Chernogryazskaya str.,14/19, Moscow, 105062;Delegatskaya str., 20, p. 1, Moscow, 127473

Хорошилова-МасловаИ. П.Khoroshilova-MaslovaI. P.

Хорошилова-Маслова Инна Петровна доктор медицинских наук, профессор, начальник отдела патологической анатомии и гистологии

ул. Садовая-Черногрязская, 14/19, Москва, 105062

Khoroshilova-Maslova Inna P. MD, professor, head of the pathology department

Sadovaya-Chernogryazskaya str., 14/19, Moscow, 105062

ЦыганковА. Ю.TsygankovA. Yu.

Цыганков Александр Юрьевич кандидат медицинских наук, врач-офтальмолог, младший научный сотрудник отдела офтальмоонкологии и радиологии, ассистент кафедры глазных болезней ФДПО

ул. Садовая-Черногрязская, 14/19, Москва, 105062; ул. Делегатская, 20, стр. 1, Москва, 127473

Tsygankov Alexander Y. PhD, ophthalmologist, junior researcher of the ophthalmic oncology and radiology department, assistant of the eye diseases department

Sadovaya-Chernogryazskaya str., 14/19, Moscow, 105062; Delegatskaya str., 20, p. 1, Moscow, 127473

НикитинО. И.NikitinO. I.

Никитин Олег Игоревич генеральный директор

ул. Козлова, 30, Москва, 121357

Nikitin Oleg I. General manager

Kozlov str.. 30, Moscow, 121357

ТарасовГ. Ю.TarasovG. Yu.

Тарасов Григорий Юрьевич старший программист

ул. Козлова, 30, Москва, 121357

Tarasov Grigory Y. Senior programmer

Kozlov str., 30, Moscow, 121357

ГБОУ ВО «Московский государственный медико-стоматологический университет им. А.И. Евдокимова» Министерства здравоохранения Российской Федерации; ООО «Искусственные сети и технологии»РоссияMoscow State Medical Stomatological University; Limited Liability Company “Artificial networks and technologies”Russian FederationГБОУ ВО «Московский государственный медико-стоматологический университет им. А.И. Евдокимова» Министерства здравоохранения Российской Федерации; Национальный медицинский научно-исследовательский центр глазных болезней им. ГельмгольцаРоссияMoscow State Medical Stomatological University; Helmholtz National Medical Сenter of Eye DiseasesRussian FederationНациональный медицинский научно-исследовательский центр глазных болезней им. ГельмгольцаРоссияHelmholtz National Medical Сenter of Eye DiseasesRussian FederationООО «Искусственные сети и технологии»РоссияLimited Liability Company “Artificial networks and technologies”Russian Federation2020020420201712031Copyright © Гарри Д.Д., Саакян С.В., Хорошилова-Маслова И.П., Цыганков А.Ю., Никитин О.И., Тарасов Г.Ю., 20202020Гарри Д.Д., Саакян С.В., Хорошилова-Маслова И.П., Цыганков А.Ю., Никитин О.И., Тарасов Г.Ю.Garri D.D., Saakyan S.V., Khoroshilova-Maslova I.P., Tsygankov A.Y., Nikitin O.I., Tarasov G.Y.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.ophthalmojournal.com/opht/article/view/1127

Методы машинного обучения имеют прикладное применение в каждой сфере человеческой деятельности, использующей цифровые данные. В последние годы было опубликовано множество работ, посвященных использованию искусственного интеллекта в задачах классификации, регрессии, сегментации в медицине и в офтальмологии в частности. Искусственный интеллект — подраздел информатики, его принципы и понятия зачастую непонятны или используются и трактуются врачами некорректно. Диагностика заболеваний у пациентов офтальмологического профиля связана с существенным количеством медицинских данных, которые могут быть использованы для дальнейшей программной обработки. С помощью методов машинного обучения можно узнать, обозначить и посчитать практически любые патологические признаки болезней, анализируя медицинские изображения, клинические и лабораторные данные. Машинное обучение включает модели и алгоритмы, которые имитируют архитектуру биологических нейронных сетей. Наибольший интерес представляют искусственные нейронные сети, в особенности сети на основе глубокого обучения, вследствие способности последних эффективно работать со сложными и многомерными базами данных вкупе с возрастающей доступностью баз данных и производительностью графических процессоров. Искусственные нейронные сети имеют потенциал для использования в автоматизированном скрининге, при определении стадии заболеваний, прогнозировании терапевтического эффекта лечения и исхода заболеваний. В работе рассматриваются труды, посвященные использованию искусственного интеллекта в анализе клинических данных больных диабетической ретинопатией, возрастной макулярной дегенерацией, глаукомой, катарактой, злокачественными новообразованиями глазного яблока, сочетанной патологией. Основными показателями в исследованиях явились размер обучающей и валидационной выборок, точность, чувствительность, специфичность, AUROC (Area Under Receiver Operating Characteristic Curve, площадь под кривой ошибок). В ряде исследований изучается сравнительная характеристика алгоритмов. Многие работы, представленные в обзоре, показывают результаты по точности, чувствительности, специфичности, AUROC, значениям ошибки, превышающие соответствующие показатели среднего специалиста-офтальмолога. Внедрение их в рутинную клиническую практику повысит диагностические, терапевтические и профессиональные возможности врача-специалиста, что особенно актуально в области офтальмоонкологии, в которой стоит вопрос выживаемости пациентов.

Machine learning is applied in every field of human activity using digital data. In recent years, many papers have been published concerning artificial intelligence use in classification, regression and segmentation purposes in medicine and in ophthalmology, in particular. Artificial intelligence is a subsection of computer science and its principles, and concepts are often incomprehensible or used and interpreted by doctors incorrectly. Diagnostics of ophthalmology patients is associated with a significant amount of medical data that can be used for further software processing. By using of machine learning methods, it’s possible to find out, identify and count almost any pathological signs of diseases by analyzing medical images, clinical and laboratory data. Machine learning includes models and algorithms that mimic the architecture of biological neural networks. The greatest interest in the field is represented by artificial neural networks, in particular, networks based on deep learning due to the ability of the latter to work effectively with complex and multidimensional databases, coupled with the increasing availability of databases and performance of graphics processors. Artificial neural networks have the potential to be used in automated screening, determining the stage of diseases, predicting the therapeutic effect of treatment and the diseases outcome in the analysis of clinical data in patients with diabetic retinopathy, age-related macular degeneration, glaucoma, cataracts, ocular tumors and concomitant pathology. The main characteristics were the size of the training and validation datasets, accuracy, sensitivity, specificity, AUROC (Area Under Receiver Operating Characteristic Curve). A number of studies investigate the comparative characteristics of algorithms. Many of the articles presented in the review have shown the results in accuracy, sensitivity, specificity, AUROC, error values that exceed the corresponding indicators of an average ophthalmologist. Their introduction into routine clinical practice will increase the diagnostic, therapeutic and professional capabilities of a clinicians, which is especially important in the field of ophthalmic oncology, where there is a patient survival matter.

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The authors declare that there are no conflicts of interest present.