Accuracy of deep learning-based integrated tooth models by merging intraoral scans and CBCT scans for 3D evaluation of root position during orthodontic treatment.
Artificial intelligence
CBCT
Deep learning
Intraoral scans
Root position
Tooth models
Journal
Progress in orthodontics
ISSN: 2196-1042
Titre abrégé: Prog Orthod
Pays: Germany
ID NLM: 100936353
Informations de publication
Date de publication:
09 May 2022
09 May 2022
Historique:
received:
29
12
2021
accepted:
12
04
2022
entrez:
8
5
2022
pubmed:
9
5
2022
medline:
11
5
2022
Statut:
epublish
Résumé
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. Intraoral scans and corresponding CBCT scans before and after treatment were obtained from 15 patients who completed orthodontic treatment with premolar extraction. A total of 600 ITMs were generated using deep learning technology and manual methods by merging the intraoral scans and CBCT scans at pretreatment. Posttreatment intraoral scans were integrated into the tooth model, and the resulting estimated root positions were compared with the actual root position at posttreatment CBCT. Discrepancies between the estimated and actual root position including average surface differences, arch widths, inter-root distances, and root axis angles were obtained in both the deep learning and manual method, and these measurements were compared between the two methods. The average surface differences of estimated and actual ITMs in the manual method were 0.02 mm and 0.03 mm for the maxillary and mandibular arches, respectively. In the deep learning method, the discrepancies were 0.07 mm and 0.08 mm for the maxillary and mandibular arches, respectively. For the measurements of arch widths, inter-root distances, and root axis angles, there were no significant differences between estimated and actual models both in the manual and in the deep learning methods, except for some measurements. Comparing the two methods, only three measurements showed significant differences. The procedure times taken to obtain the measurements were longer in the manual method than in the deep learning method. Both deep learning and manual methods showed similar accuracy in the integration of intraoral scans and CBCT images. Considering time and efficiency, the deep learning automatic method for ITMs is highly recommended for clinical practice.
Identifiants
pubmed: 35527317
doi: 10.1186/s40510-022-00410-x
pii: 10.1186/s40510-022-00410-x
pmc: PMC9081076
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
15Subventions
Organisme : National Research Foundation of Korea
ID : No. 2020R1F1A1070617
Informations de copyright
© 2022. The Author(s).
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