Currently, few methods are available to measure orthodontic treatment need and treatment outcome from the lay perspective. The objective of this study was to explore the function of an eye-tracking method to evaluate orthodontic treatment need and treatment outcome from the lay perspective as a novel and objective way when compared with traditional assessments.Methods:
The scanpaths of 88 laypersons observing the repose and smiling photographs of normal subjects and pretreatment and posttreatment malocclusion patients were recorded by an eye-tracking device. The total fixation time and the first fixation time on the areas of interest (eyes, nose, and mouth) for each group of faces were compared and analyzed using mixed-effects linear regression and a support vector machine. The aesthetic component of the Index of Orthodontic Treatment Need was used to categorize treatment need and outcome levels to determine the accuracy of the support vector machine in identifying these variables.Results:
Significant deviations in the scanpaths of laypersons viewing pretreatment smiling faces were noted, with less fixation time (P <0.05) and later attention capture (P <0.05) on the eyes, and more fixation time (P <0.05) and earlier attention capture (P <0.05) on the mouth than for the scanpaths of laypersons viewing normal smiling subjects. The same results were obtained when comparing posttreatment smiling patients, with less fixation time (P <0.05) and later attention capture on the eyes (P <0.05), and more fixation time (P <0.05) and earlier attention capture on the mouth (P <0.05). The pretreatment repose faces exhibited an earlier attention capture on the mouth than did the normal subjects (P <0.05) and posttreatment patients (P <0.05). Linear support vector machine classification showed accuracies of 97.2% and 93.4% in distinguishing pretreatment patients from normal subjects (treatment need), and pretreatment patients from posttreatment patients (treatment outcome), respectively.Conclusions:
The eye-tracking device was able to objectively quantify the effect of malocclusion on facial perception and the impact of orthodontic treatment on malocclusion from the lay perspective. The support vector machine for classification of selected features achieved high accuracy of judging treatment need and treatment outcome. This approach may represent a new method for objectively evaluating orthodontic treatment need and treatment outcome from the perspective of laypersons.