API for orthodontic treatment length estimator
Our API lets you get an accurate result, allowing your application functionality to be extended with our AI capability within a few steps.
This API will allow developers to easily integrate orthodontic length estimator functions into their systems, enabling them to provide users with more comprehensive and accurate information.
API case studies
Treatment duration is one of the most important factors that patients consider when deciding 20 whether to have orthodontic treatment or not. This study aimed to build and compare Machine 21 Learning (ML) models for prediction of orthodontic treatment length and to identify factors affect-22 ing the duration of orthodontic treatment using the ML approach. Records of 518 patients who suc-23 cessfully finished orthodontic treatment were used in this study. 70% of the patient data was used 24 for training ML models, and 30% of data was used for testing these models. We applied and com-25 pared nine machine-learning algorithms: Simple Linear Regression, Modified Simple Linear Regres-26 sion, Polynomial Linear Regression, K Nearest Neighbor, Simple Decision Tree, Bagging Regressor, 27 Random Forest, Gradient Boosting Regression, and AdaBoost Regression. We then calculated the 28 importance of patient data features for the ML models with the highest performance. The best over-29 all performance was obtained through Bagging Regressor and AdaBoost Regression ML methods. 30 The most important features in predicting treatment length were age, crowding, artificial intelli-31 gence case difficulty score, overjet, and overbite. Without patient information, several ML algo-32 rithms showed comparable performance for predicting treatment length. Bagging and AdaBoost 33 showed the best performance when patient information, including age, malocclusion, and crowd-34 ing, was provided.
We achieved our objective of developing predictive models-based ML methods. Bagging 247 and AdaBoost ML methods provided good predictability for orthodontic treatment length 248 when patient information, such as age, malocclusion, and crowding, was provided. Fur-249 thermore, the study demonstrated the relative importance of each factor. Additional stud-250 ies should be done on large, diverse datasets to include more variables and improve the 251 performance of ML models for understanding orthodontic treatment length.
This study aimed to build and compare machine learning (ML) models for the prediction of orthodontic treatment length
How this API works
Doctors or clinics will be able to use this API to estimate orthodontic treatment length, and the treatment needed based on the condition of the teeth so that the doctors or clinics can set prices and make "Dental Treatment Planning". Patients only need to input their name, age, height, race, and other relevant information.
Current U.S. Class: 1/1
Current CPC Class: G06N 3/04 20130101; G06N 3/08 20130101
International Class: G06N 3/08 20060101 G06N003/08; G06N 3/04 20060101 G06N003/04