Accuracy of deep learning-based integrated tooth models by merging intraoral scans and CBCT scans for 3D evaluation of root position during orthodontic treatment.

Journal: Progress in orthodontics

PMID: 35527317

Abstract

OBJECTIVE: This study aimed to evaluate the accuracy of deep learning-based integrated tooth models (ITMs) by merging intraoral scans and cone-beam computed tomography (CBCT) scans for three-dimensional (3D) evaluation of root position during orthodontic treatment and to compare the fabrication process of integrated tooth models (ITMs) with manual method.

Authors

Suk-Cheol Lee

, Gwangju, Korea.
Hyeon-Shik Hwang

Department of Orthodontics, School of Dentistry, Chonnam National University, 33 Yongbong-ro, Buk-gu, Gwangju, 61186, Korea.
Kyungmin Clara Lee

Department of Orthodontics, School of Dentistry, Chonnam National University, 33 Yongbong-ro, Buk-gu, Gwangju, 61186, Republic of Korea. ortholkm@jnu.ac.kr.

Keywords

Cone-Beam Computed Tomography Deep Learning Humans Maxilla Models, Dental Spiral Cone-Beam Computed Tomography Tooth Root

External Resources

View on PubMed Access via DOI PubMed (35527317)

Accuracy of deep learning-based integrated tooth models by merging intraoral scans and CBCT scans for 3D evaluation of root position during orthodontic treatment.

Abstract

Authors

Keywords

External Resources

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