METHOD AND SYSTEM FOR PROVIDING DYNAMIC ORTHODONTIC ASSESSMENT AND TREATMENT PROFILES
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.