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.

翻译：脸部畸形或畸形往往与面部的异常增长有关。 预测面部增长方向的能力将使临床医生能够准备个性化治疗,以增加成功治疗的机会。 预测FG方向是机器学习领域一个新问题。 在本文中, 我们进行特征选择, 指出在上述问题中发挥核心作用的属性。 然后成功应用数据增强( DA) 方法, 并将先前报告的分类精确度提高2.81% 。 最后, 我们介绍两位有经验的临床医生的结果, 他们被要求解决与我们相似的任务, 并展示如何难以解决人类专家的这一问题 。