Prediction Equations For Widths Of Unerupted Canine And Premolars In Mixed Dentition Among Different Malocclusion Groups

Objective: Mixed dentition analysis is to predict the tooth size ofunerupted permanent canine and premolars，to determine the discrepancybetween the available space of each arch and required space of permanent tooth,that is, whether crowding and crowding severity. With increase in the amountof the early orthodontic patients, it is important for the orthodontist toaccurately estimate whether there is a lack of arch space and to develop timelyand appropriate treatment plan. Accurate mixed dentition space analysis is oneof the important criteria in the mixed dentition to develop orthodontic treatmentplanning, treatment may include the serial extractions, space regaining,spacemaintenance, guidance of eruption, or only need regularly observation if thedegree of crowding is not serious. Regression equations is the most commonapproaches of mixed dentition analysis for prediction of the size of theunerupted permanent teeth. To develop a regression equation based oncorrelation analysis between the widths of the tooth in the permanentdentition.The prediction value can be used to detect tooth size-arch lengthdiscrepancy or the discrepancy between the jaws,as one of the basis forguiding the development of orthodontic treatment plan. Scholars from variouscountries made a lot of research about the factors that affect the permanenttooth-size, most studies concluded that tooth-size differences among differentethnic groups, sex, and malocclusion categories. Permanent tooth-sizedifferences will affect the establishment of the prediction equation, includingtwo aspects, the predicting tooth segment and the correlation coefficient of theregression equation, thus affecting the accuracy of the prediction. Prediction of the unerupted permanent tooth width, regression equation are different in ethnicgroups, gender, jaws, and all the sample are selected from normal occlusion inthe existing researches. In this experiment, the new regression equation derivedfrom the sample are malocclusion patients with permanent dentition，which ismore applicable to the dentofacial characteristics of malocclusion, to improveprediction accuracy.Methods: Three hundred pretreatment dental casts of patients in treatmentat the orthodontic clinic at the JiLin University were selected and evaluatedbased on predetermined exclusion and inclusion criteria. Our subjects included150female and150male patients. Malocclusion category was assigned basedon coincident Angle classification (Class Ⅰ, Class Ⅱ, or Class Ⅲ, based onocclusal relationship),the ClassⅡ2subjects were excluded.All the subjects withnewly diagnosed to rule out severe skeletal malocclusion,were non-surgicaltreatment to adapt. A vernier caliper accurate to0.1mm was used formeasurements. On each patient’s dental cast, each tooth was measured at thelargest mesiodistal dimension and recorded with the patient’s sex, andmalocclusion category. The measured values were subjected to correlation andlinear regression analysis.Results: Forecast tooth segments and the correlation coefficient weredifferent from malocclusion categories, respectively, to establish newprediction equations.ClassⅠ:males, U345=0.692L1L2+15.158,L345=0.769L1L2+3.6491；females, U345=1.083L1L2+10.162，L345=1.517L1L2+4.123；Class Ⅱ:males,U345=0.736U1U6L1+4.692, L345=0.732U1U6L1+4.204；females, U345=0.665U1U6L1+6.432，L345=0.576U1U6L1+7.719；ClassⅢ:males, U345=0.281U1U6L1+16.242,L345=0.355U1U6L1+13.51;females, U345=0.665U1U6L1+6.432，L345=0.819U1U6L1+1.663.Conclusion: As the subjects of this study were malocclusion with permanent dentition and the regression equation are different in types ofmalocclusion in order to improve the prediction accuracy of the analysis.