Mandibular retrognathia (C2Rm) is one of the most common oral pathologies. Acquiring a better understanding of the points of impact of C2Rm on the entire skull is of major interest in the diagnosis, treatment, and management of this dysmorphism, but also permits us to contribute to the debate on the changes undergone by the shape of the skull during human evolution. However, conventional methods have some limits in meeting these challenges, insofar as they require defining in advance the structures to be studied, and identifying them using landmarks. In this context, our work aims to answer these questions using AI tools and, in particular, machine learning, with the objective of relaying these treatments automatically. We propose an innovative methodology coupling convolutional neural networks (CNNs) and interpretability algorithms. Applied to a set of radiographs classified into physiological versus pathological categories, our methodology made it possible to: discuss the structures impacted by retrognathia and already identified in literature; identify new structures of potential interest in medical terms; highlight the dynamic evolution of impacted structures according to the level of gravity of C2Rm; provide for insights into the evolution of human anatomy. Results were discussed in terms of the major interest of this approach in the field of orthodontics and, more generally, in the field of automated processing of medical images.

下颌后缩（C2Rm）是最常见的口腔疾病之一。更好地了解 C2Rm 对整个颅骨的影响点对于这种畸形的诊断、治疗和管理具有重大意义，同时也使我们能够为关于颅骨形状所经历的变化的辩论做出贡献在人类进化过程中。然而，传统方法在应对这些挑战方面存在一些局限性，因为它们需要提前定义要研究的结构，并使用地标来识别它们。在这种背景下，我们的工作旨在使用人工智能工具，特别是机器学习来回答这些问题，目的是自动传递这些治疗方法。我们提出了一种将卷积神经网络（CNN）和可解释性算法相结合的创新方法。我们的方法应用于一组分为生理与病理类别的放射线照片，使以下内容成为可能： 讨论受下颌后缩影响且已在文献中确定的结构；确定医学术语中潜在兴趣的新结构；根据 C2Rm 的重力水平突出受影响结构的动态演化；提供对人体解剖学进化的见解。根据该方法在正畸领域以及更广泛的医学图像自动处理领域的主要兴趣来讨论结果。