Machine Learning in Orthodontics: Introducing a 3d Auto-segmentation and Auto-landmark Finder of Cbct Images To Assess Maxillary Constriction in Unilateral Impacted Canine patients | Zendy

Si Chen | Zendy; Li Wang | Zendy; Gang Li | Zendy; TaiHsien Wu | Zendy; Shan Diachina | Zendy; Beatriz Tejera | Zendy; Jane Kwon | Zendy; FengChang Lin | Zendy; Yan-Ting Lee | Zendy; Tianmin Xu | Zendy; Dinggang Shen | Zendy; ChingChang Ko | Zendy

Open Access

Machine Learning in Orthodontics: Introducing a 3d Auto-segmentation and Auto-landmark Finder of Cbct Images To Assess Maxillary Constriction in Unilateral Impacted Canine patients

Author(s) -

Si Chen,

Li Wang,

Gang Li,

TaiHsien Wu,

Shan Diachina,

Beatriz Tejera,

Jane Kwon,

FengChang Lin,

Yan-Ting Lee,

Tianmin Xu,

Dinggang Shen,

ChingChang Ko

Publication year - 2019

Publication title -

the angle orthodontist

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.116

H-Index - 86

eISSN - 1945-7103

pISSN - 0003-3219

DOI - 10.2319/012919-59.1

Subject(s) - maxilla , impaction , cone beam computed tomography , segmentation , voxel , orthodontics , medicine , mathematics , artificial intelligence , computer science , dentistry , computed tomography , radiology

To (1) introduce a novel machine learning method and (2) assess maxillary structure variation in unilateral canine impaction for advancing clinically viable information.

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