Prediction of the Facial Growth Direction is Challenging
This work addresses a novel problem in machine learning for clinical applications, though it is incremental in method.
The paper tackles the problem of predicting facial growth direction to aid individualized therapy, achieving a 2.81% improvement in classification accuracy through feature selection and data augmentation.
Facial dysmorphology or malocclusion is frequently associated with abnormal growth of the face. The ability to predict facial growth (FG) direction would allow clinicians to prepare individualized therapy to increase the chance for successful treatment. Prediction of FG direction is a novel problem in the machine learning (ML) domain. In this paper, we perform feature selection and point the attribute that plays a central role in the abovementioned problem. Then we successfully apply data augmentation (DA) methods and improve the previously reported classification accuracy by 2.81%. Finally, we present the results of two experienced clinicians that were asked to solve a similar task to ours and show how tough is solving this problem for human experts.