Prediction of the facial growth direction with Machine Learning methods
This addresses a long-standing challenge in medical diagnostics for orthodontists and clinicians, though it is an incremental step as it applies existing ML methods to this domain.
The paper tackles the problem of predicting facial growth direction from 2D X-ray images, achieving classification accuracy between 71% and 75% using various machine learning algorithms.
First attempts of prediction of the facial growth (FG) direction were made over half of a century ago. Despite numerous attempts and elapsed time, a satisfactory method has not been established yet and the problem still poses a challenge for medical experts. To our knowledge, this paper is the first Machine Learning approach to the prediction of FG direction. Conducted data analysis reveals the inherent complexity of the problem and explains the reasons of difficulty in FG direction prediction based on 2D X-ray images. To perform growth forecasting, we employ a wide range of algorithms, from logistic regression, through tree ensembles to neural networks and consider three, slightly different, problem formulations. The resulting classification accuracy varies between 71% and 75%.