Method and system for providing dynamic orthodontic assessment and treatment profiles

Abstract

Method and system including receiving one or more parameters associated with an orthodontic condition, receiving a treatment goal information associated with the orthodontic condition, and providing a predefined template associated with the received treatment goal information, wherein the predefined template includes at least one orthodontic condition related information, are provided.

1 . (canceled) 
     
     
         2 . A method of assessing risks in orthodontic treatments, comprising:
 clustering, by a data driven analyzer, patient histories based on parameters associated with orthodontic conditions of the patients in the patient histories into a plurality of clusters, wherein the data driven analyzer is trained by patient history data to identify statistically significant patterns of different treatment outcomes;   receiving patient-specific data in relation to a planned orthodontic treatment;   determining, by the data driven analyzer, probabilities of risks for undesirable outcomes in the planned orthodontic treatment based at least in part on the patient-specific data and the parameters of the plurality of clusters;   providing, to a clinician, feedback on the probabilities of risks for undesirable outcomes for the planned orthodontic treatment.   
     
     
         3 . The method of  claim 2 , wherein determining probabilities of risks comprises comparing the statistically significant patterns of different treatment outcomes. 
     
     
         4 . The method of  claim 2 , wherein the patient-specific data comprises a three-dimensional model representing a patient's dentition, wherein the three-dimensional model is collected from an intraoral scan. 
     
     
         5 . The method of  claim 2 , wherein the patient-specific data comprises one or more patient-specific parameters associated with an orthodontic condition of a patient. 
     
     
         6 . The method of  claim 2 , wherein the data driven analyzer is trained as part of a neural network. 
     
     
         7 . The method of  claim 2 , wherein the data driven analyzer is trained with more than one training sessions. 
     
     
         8 . The method of  claim 2 , wherein the data driven analyzer is trained with a data set, wherein the data set is a separate test set for training purposes. 
     
     
         9 . The method of  claim 2 , wherein the data driven analyzer is trained with cross-validation. 
     
     
         10 . The method of  claim 2 , wherein the data driven analyzer is trained with a data set, and wherein the data set comprises data gathered by data mining software. 
     
     
         11 . The method of  claim 2 , wherein the feedback further comprises a suggested treatment approach, appliance design, or manufacturing protocol. 
     
     
         12 . A non-transitory computing device readable medium storing instructions executable by a processor to cause a computing device to perform a method, the method comprising:
 clustering, by a data driven analyzer, patient histories based on parameters associated with orthodontic conditions of the patients in the patient histories into a plurality of clusters, wherein the data driven analyzer is trained by patient history data to identify statistically significant patterns of different treatment outcomes;   receiving patient-specific data in relation to a planned orthodontic treatment;   determining, by the data driven analyzer, probabilities of risks for undesirable outcomes in the planned orthodontic treatment based at least in part on the patient-specific data and the parameters of the plurality of clusters;   providing, to a clinician, feedback on the probabilities of risks for undesirable outcomes for the planned orthodontic treatment.   
     
     
         13 . The non-transitory computing device readable medium of  claim 12 , wherein determining probabilities of risks comprises comparing the statistically significant patterns of different treatment outcomes. 
     
     
         14 . The non-transitory computing device readable medium of  claim 12 , wherein the patient-specific data comprises a three-dimensional model representing a patient's dentition, wherein the three-dimensional model is collected from an intraoral scan. 
     
     
         15 . The non-transitory computing device readable medium of  claim 12 , wherein the patient-specific data comprises one or more patient-specific parameters associated with an orthodontic condition of a patient. 
     
     
         16 . The non-transitory computing device readable medium of  claim 12 , wherein the data driven analyzer is trained as part of a neural network. 
     
     
         17 . The non-transitory computing device readable medium of  claim 12 , wherein the data driven analyzer is trained with more than one training sessions. 
     
     
         18 . The non-transitory computing device readable medium of  claim 12 , wherein the data driven analyzer is trained with a data set, wherein the data set is a separate test set for training purposes. 
     
     
         19 . The non-transitory computing device readable medium of  claim 12 , wherein the data driven analyzer is trained with cross-validation. 
     
     
         20 . The non-transitory computing device readable medium of  claim 12 , wherein the data driven analyzer is trained with a data set, and wherein the data set comprises data gathered by data mining software. 
     
     
         21 . The non-transitory computing device readable medium of  claim 12 , wherein the feedback further comprises a suggested treatment approach, appliance design, or manufacturing protocol.