Архів офтальмології та щелепно-лицевої хірургії України Том 1, №1, 2024

Актуальність. Сучасні цефалометричні аналізи надають дані анатомічних вимірювань, що необхідні як для ортодонтів, так і для щелепно-лицевих хірургів. Мета: дослідити точність і ефективність автоматизованого визначення орієнтирів на основі штучного інтелекту (ШІ) для цефалометричного аналізу на двовимірних (2D) бічних цефалограмах та бічних цефалограмах, отриманих із тривимірних (3D) конусно-променевих комп’ютерних томографічних (КПКТ) зображень, у сучасній ортодонтичній практиці. Матеріали та методи. Пошукові дослідження проводили в базах PubMed, Web of Science та Embase за період до 2024 року. Використовували двосторонню стратегію пошуку, яка включала поєднання технічного інтересу (ШI, машинне й глибоке навчання) і діагностичної мети (визначення анатомічних орієнтирів для аналізу рентгенограми черепа). Кожне поняття включало терміни MeSH та ключові слова. Для мінімізації ризику системної помилки був проведений всебічний пошук сірої літератури з використанням таких баз даних, як ProQuest, Google Scholar, OpenThesis і OpenGrey. Результати. Після видалення дублікатів, скринінгу назв і рефератів, повнотекстового читання було відібрано 34 публікації. Серед них у 27 дослідженнях оцінювали точність автоматизованого маркування на 2D бічних цефалограмах на основі ШІ, тоді як 7 досліджень включали 3D-КПКТ зображення. У більшості робот продемонстрований високий ризик системної помилки при виборі даних (n = 27) і референтного стандарту (n = 29). Висновки. ШІ-цефалометричне визначення орієнтирів як на 2D-, так і на бічних цефалограмах, синтезованих із 3D-зображень, показало досить великий потенціал з точки зору точності й ефективності використання часу.

Background. Modern cephalometric analyses provide necessary anatomical measurement data that is essential for both orthodontists and craniomaxillofacial surgeons. The purpose was to investigate the accuracy and efficiency of automated landmark detection based on artificial intelligence (AI) for cephalometric analysis on two-dimensional (2D) lateral cephalograms and lateral cephalograms that were obtained from three-dimensional (3D) cone beam computed tomographic (CBCT) images in modern orthodontics practice. Materials and methods. Searches were performed in PubMed, Web of Science, and Embase up to 2024. A two-pronged search strategy was used, which included a combination of technical interest (AI, machine and deep learning) and the diagnostic goal (landmark detection for skull radiograph analysis). Each concept included MeSH terms and keywords. A comprehensive grey literature search was performed using databases such as ProQuest, Google Scholar, OpenThesis, and OpenGrey to minimize the risk of bias. Results. After duplicate removal, title, and abstract screening, full-text reading, 34 publications were selected. Amongst these, 27 studies evaluated the accuracy of automated landmarking on 2D lateral cephalograms based on AI, while 7 studies involved 3D-CBCT images. Most studies exhibited a high risk of bias in data selection (n = 27) and reference standard (n = 29). Conclusions. The AI cephalometric landmark detection performance on both 2D and la­teral cephalograms synthesized from 3D images showed quite a huge potential in aspects of accuracy and time efficiency.

ортодонтія; анатомічні орієнтири; цефалометрія; штучний інтелект

orthodontics; anatomic landmarks; cephalometry; artificial intelligence

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