Automated detection of anterior crossbite on intraoral images and videos utilizing deep learning.

The deep learning (DL) model exhibited remarkable classification accuracy in identifying anterior crossbite through both intraoral images and videos. This proficiency has the potential to expedite the detection of severe malocclusions, facilitating timely classification for appropriate treatment and, consequently, mitigating the risk of complications.

Authors

Publication Details

Journal
PubMed
Published
2025-09-01
DOI
https://doi.org/10.3290/j.ijcd.b5290567
Primary Topic
Dental Radiography and Imaging
Type
article
Field-Weighted Citation Impact
0.00
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article

Automated detection of anterior crossbite on intraoral images and videos utilizing deep learning.

Jinlin Song, Zhaowu Chai, Chao Zhang, Zhengyu Wu
PubMed
Dental Radiography and Imaging
article

Automated detection of anterior crossbite on intraoral images and videos utilizing deep learning.

Jinlin Song, Zhaowu Chai, Chao Zhang, Zhengyu Wu
article en

Abstract

The deep learning (DL) model exhibited remarkable classification accuracy in identifying anterior crossbite through both intraoral images and videos. This proficiency has the potential to expedite the detection of severe malocclusions, facilitating timely classification for appropriate treatment and, consequently, mitigating the risk of complications.

PubMedVol. 28(3)
No poverty
Openalex Percentile: Top 100%
Dental Radiography and Imaging
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