In The Angle orthodontist ; h5-index 34.0
OBJECTIVES : To identify predictors regarding the type and severity of malocclusion that affect total Invisalign treatment duration based on an intraoral digital scan.
MATERIALS AND METHODS : The subjects of this retrospective clinical cohort were 116 patients treated with Invisalign. A deep learning method was used for automated tooth segmentation and landmark identification of the initial and final digital models. The changes in the six degrees of freedom (DOF), representing types of malalignment, were measured. Linear regression was performed to find the contributing factors associated with treatment time. In addition, the Peer Assessment Rating (PAR) score and a composite score combining 6 DOF were correlated separately to the treatment time.
RESULTS : The number of trays differed between sexes (P = .0015). The absolute maximum torque was marginally associated with the total number of trays (P = .0518), while the rest of the orthodontic tooth movement showed no correlation. The composite score showed a higher correlation with the total number of trays (P = .0045) than did individual tooth movement. Pretreatment upper and lower anterior segment PAR scores were positively associated with the treatment time (P < .001).
CONCLUSIONS : There is not enough evidence to conclude that certain types of tooth movement affect the total aligner treatment time. A composite score seems to be a better predictor for total treatment time than do individual malalignment factors in aligner treatment. Upper and lower anterior malalignment factors have a significant effect on the treatment duration.
Lee Sanghee, Wu Tai-Hsien, Deguchi Toru, Ni Ai, Lu Wei-En, Minhas Sumeet, Murphy Shaun, Ko Ching-Chang
2022-Nov-03
Aligner, Artificial intelligence, Deep learning, Invisalign
