AI-Assisted CBCT Data Management in Modern Dental Practice: Benefits, Limitations and Innovations

被引：35

作者：

Urban, Renata ^{[1
]}

Haluzova, Sara ^{[1
]}

Strunga, Martin ^{[1
]}

Surovkova, Jana ^{[1
]}

Lifkova, Michaela ^{[1
]}

Tomasik, Juraj ^{[1
]}

Thurzo, Andrej ^{[1
]}

机构：

[1] Comenius Univ, Fac Med, Dept Orthodont Regenerat & Forens Dent, Bratislava 81250, Slovakia

来源：

ELECTRONICS | 2023年 / 12卷 / 07期

关键词：

CBCT; AI; deep learning; medical image analysis; image processing; image segmentation; dental nurse; ChatGPT; Diagnocat; Anatomage Invivo 7.0; machine learning; NEURAL-NETWORK; SEGMENTATION;

D O I：

10.3390/electronics12071710

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Within the next decade, artificial intelligence (AI) will fundamentally transform the workflow of modern dental practice. This paper reviews the innovations and new roles of dental assistants in CBCT data management with the support of AI. Its use in 3D data management brings new roles for dental assistants. Cone beam computed tomography (CBCT) technology is, together with intraoral 3D scans and 3D facial scans, commonly used 3D diagnostic in a modern digital dental practice. This paper provides an overview of the potential benefits of AI implementation for semiautomated segmentations in standard medical diagnostic workflows in dental practice. It discusses whether AI tools can enable healthcare professionals to increase their reliability, effectiveness, and usefulness, and addresses the potential limitations and errors that may occur. The paper concludes that current AI solutions can improve current digital workflows including CBCT data management. Automated CBCT segmentation is one of the current trends and innovations. It can assist professionals in obtaining an accurate 3D image in a reduced period of time, thus enhancing the efficiency of the whole process. The segmentation of CBCT serves as a helpful tool for treatment planning as well as communicating the problem to the patient in an understandable way. This paper highlights a high bias risk due to the inadequate sample size and incomplete reporting in many studies. It proposes enhancing dental workflow efficiency and accuracy through AI-supported cbct data management

引用

页数：14

共 65 条

[1]

Alamri Hadi Mohammed, 2012, General Dentistry, V60, P390

[2] Artificial Intelligence (AI) for Detection and Localization of Unobturated Second Mesial Buccal (MB2) Canals in Cone-Beam Computed Tomography (CBCT) [J].