CBCT segmentation of the mandibular canal with both semi-automated and fully automated methods: A systematic review

被引：0

作者：

Barnes, Neil Abraham ^{[1
]}

Sharath, S. ^{[1
]}

Dkhar, Winniecia ^{[1
]}

Chhaparwal, Yogesh ^{[2
]}

Nayak, Kaushik ^{[1
]}

机构：

[1] Manipal Acad Higher Educ, Manipal Coll Hlth Profess, Dept Med Imaging Technol, Manipal, Karnataka, India

[2] Manipal Acad Higher Educ, Manipal Coll Dent Sci, Dept Oral Med & Radiol, Manipal, Karnataka, India

来源：

CLINICAL EPIDEMIOLOGY AND GLOBAL HEALTH | 2024年 / 29卷

关键词：

Cone beam computed tomography; Mandibular canal; Third molar; Machine learning; Deep learning; BEAM; MOLARS; IMAGES;

D O I：

10.1016/j.cegh.2024.101760

中图分类号：

R1 [预防医学、卫生学];

学科分类号：

1004 ; 120402 ;

摘要：

Background: The application of AI algorithms for the detection of the mandibular canal in Cone Beam Computed Tomography (CBCT) holds immense promise in dentistry. Aim: This review aimed to identify the semi and fully automated algorithm to localize the mandibular canal. An extensive search was conducted and, out of which 12 articles are considered for review. The result revealed using various AI algorithms achieved better accuracy in localizing the mandibular canal with reporting sensitivity and specificity above 90 %. In conclusion, it is noted that the application of AI algorithms in dentistry can provide significant benefits like improving the accuracy of reporting.

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页数：6

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