Actual problems in dentistry Actual problems in dentistry Проблемы стоматологии 2077-7566 2412-9461 50213 10.18481/2077-7566-22-18-1-78-86 ТЕРАПЕВТИЧЕСКАЯ СТОМАТОЛОГИЯ THERAPEUTIC DENTISTRY ТЕРАПЕВТИЧЕСКАЯ СТОМАТОЛОГИЯ PROCESSING OF CBCT DATA WITH ARTIFICIAL INTELLIGENCE IN THE DIAGNOSIS OF CARIES AND ITS COMPLICATIONS ОБРАБОТКА КЛКТ-ДАННЫХ ИСКУССТВЕННЫМ ИНТЕЛЛЕКТОМ ПРИ ДИАГНОСТИКЕ КАРИЕСА И ЕГО ОСЛОЖНЕНИЙ Хабадзе Зураб Суликоевич Khabadze Zurab Sulikoevich dr.zura@mail.ru

кандидат медицинских наук;

candidate of medical sciences;

 Макеева Ирина Михайловна Makeeva Irina Mikhailovna makeeva_i_m@staff.sechenov.ru

доктор медицинских наук;

doctor of medical sciences;

 Морданов Олег Сергеевич Mordanov Oleg Sergeevich mordanov-os@rudn.ru Назарова Дарья Александровна Nazarova Daria A. 1032182486@rudn.ru Российский университет дружбы народов Москва Россия Peoples' Friendship University of Russia Moscow Russian Federation Первый Московский государственный медицинский университет имени И. М. Сеченова Москва Россия I. M. Sechenov First Moscow State Medical University Moscow Russian Federation Российский университет дружбы народов Москва Россия Peoples' Friendship University of Russia Moscow Russian Federation Российский университет дружбы народов Москва Россия Peoples' Friendship University of Russia Moscow Russian Federation 17 05 2022 17 05 2022 18 1 78 86 06 05 2022 https://dental-press.ru/en/nauka/article/50213/view

За последние несколько лет технологии искусственного интеллекта (ИИ) стали активно применяться во многих разделах медицины, в том числе в стоматологии. Цель исследования — определить диагностическую ценность ИИ в выявлении кариеса и его осложнений по данным конусно-лучевой компьютерной томографии (КЛКТ) в сопоставлении с клиническим обследованием. Материалы и методы. КЛКТ-изображения 15 пациентов с кариозными и периодонтальными поражениями были проанализированы опытным врачом-стоматологом, имеющим специализацию в области рентгенологии, и программным обеспечением Diagnocat AI. Также врачом-стоматологом был проведен визуальный осмотр данных пациентов. Результаты. Больше всего контактных кариесов было определено с помощью ИИ (n = 20), а окклюзионных кариесов — при клиническом обследовании (n = 10). Наибольшее количество периапикальных изменений было обнаружено при применении ИИ (n = 22). Разница между показателями выявления патологических очагов при оценке ИИ и врачом-рентгенологом была статистически незначимой, что говорит о равнозначности данных методов. Рентгенологическая оценка изображений позволила выявить большее число контактных кариесов по сравнению с клиническим осмотром (14 против 7, p < 0,05), но клинический осмотр оказался эффективнее в отношении выявления окклюзионных кариесов (10 против 2, p < 0,03). Заболевания периодонта были точнее диагностированы рентгенологическим методом (17 против 9, p < 0,05). Среднее время оценки КЛКТ-изображений врачом-рентгенологом составило 21,54 ± 4,4 минуты, а ИИ выполнил отчет за 4,6 ± 4,4 минуты от момента завершения загрузки КЛКТ (p < 0,01). Заключение. Применение технологий ИИ при анализе КЛКТ-изображений позволяет повысить точность диагностики кариеса и его осложнений до 98%, а также существенно ускорить время принятия диагностического решения.

Over the past few years, artificial intelligence (AI) technologies have been actively used in many areas of medicine, including dentistry. The aim of the study is to determine the diagnostic value of IS in the detection of caries and its complications according to cone beam computed tomography (CBCT) data in comparison with clinical examination. Materials and methods. CBCT images of 15 patients with carious and periodontal lesions were analyzed by an experienced dentist, who also specializes in radiology, and the Diagnocat AI software. The dentist also performed a visual examination of these patients. Results. Most of all contact caries were determined using AI (n = 20), and occlusal caries − during clinical examination (n = 10). The greatest number of periapical changes was also detected using IS (n = 22). The difference between the indicators of detection of pathological foci in the assessment of IS and the radiologist was statistically insignificant, which indicates the equivalence of these methods. X-ray image evaluation revealed more contact caries compared to clinical examination (14 vs. 7, p < 0.05), but clinical examination was superior in detecting occlusal caries (10 vs. 2, p < 0.03). Periodontal disease was more accurately diagnosed by X-ray (17 vs. 9, p < 0.05). The average time for evaluation of CBCT images by a radiologist was 21.54 ± 4.4 minutes, and the AI completed the report in 4.6 ± 4.4 minutes from the moment the loading of CBCT was completed (p < 0.01). Conclusion. The use of AI technologies in the analysis of CBCT images can improve the accuracy of diagnosing caries and its complications by up to 98%, as well as significantly speed up the time for making a diagnostic decision.

 искусственный интеллект кариес диагностика конусно-лучевая компьютерная томография периапикальные изменения artificial intelligence caries diagnostics cone beam computed tomography periapical changes

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