Prediction of the Facial Growth Direction is Challenging

TL;DR

This paper addresses the challenge of predicting facial growth direction using machine learning, highlighting the importance for personalized treatment and demonstrating improved accuracy through feature selection and data augmentation.

Contribution

It introduces a novel ML approach for FG prediction, identifies key features, applies data augmentation, and compares human expert performance to machine learning results.

Findings

Data augmentation improved classification accuracy by 2.81%.

Feature selection identified key attributes for FG prediction.

Clinicians found the task challenging compared to ML models.

Abstract

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.