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

Neil Abraham Barnes; S. Sharath; Winniecia Dkhar; Yogesh Chhaparwal; Kaushik Nayak

doi:10.1016/j.cegh.2024.101760

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

Neil Abraham Barnes
, S. Sharath
, Winniecia Dkhar^*
, Yogesh Chhaparwal
, Kaushik Nayak

^*Corresponding author for this work

Research output: Contribution to journal › Review article › peer-review

Abstract

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.

Original language	English
Article number	101760
Journal	Clinical Epidemiology and Global Health
Volume	29
DOIs	https://doi.org/10.1016/j.cegh.2024.101760
Publication status	Published - 01-09-2024

All Science Journal Classification (ASJC) codes

Epidemiology
Public Health, Environmental and Occupational Health
Microbiology (medical)
Infectious Diseases

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.cegh.2024.101760

Cite this

@article{55279d62630640c2a3e91fbac15143ef,

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

abstract = "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.",

author = "Barnes, \{Neil Abraham\} and S. Sharath and Winniecia Dkhar and Yogesh Chhaparwal and Kaushik Nayak",

note = "Publisher Copyright: {\textcopyright} 2024 The Authors",

year = "2024",

month = sep,

day = "1",

doi = "10.1016/j.cegh.2024.101760",

language = "English",

volume = "29",

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TY - JOUR

T1 - CBCT segmentation of the mandibular canal with both semi-automated and fully automated methods

T2 - A systematic review

AU - Barnes, Neil Abraham

AU - Sharath, S.

AU - Dkhar, Winniecia

AU - Chhaparwal, Yogesh

AU - Nayak, Kaushik

PY - 2024/9/1

Y1 - 2024/9/1

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85201499393

UR - https://www.scopus.com/pages/publications/85201499393#tab=citedBy

U2 - 10.1016/j.cegh.2024.101760

DO - 10.1016/j.cegh.2024.101760

M3 - Review article

AN - SCOPUS:85201499393

SN - 2213-3984

VL - 29

JO - Clinical Epidemiology and Global Health

JF - Clinical Epidemiology and Global Health

M1 - 101760

ER -

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

Abstract

All Science Journal Classification (ASJC) codes

UN SDGs

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