The Zygomaticomaxillary Suture is a key circummaxillary structure that connects the zygomatic bone and the maxilla, which serves as a primary site of resistance during maxillary advancement, and its maturation status directly influences the timing and efficacy of orthopedic interventions. However, accurate staging of ZMS maturation remains challenging due to subtle high-frequency transitions in suture lines and the global semantic ambiguity between adjacent stages. To address this, we present the first public ZMS dataset, comprising 3,790 ZMS images covering the entire age range from 4 to 24 years. Based on this dataset, we propose SKMamba, a Structure-aware and Knowledge-guided Mamba-based multi-modal framework for automated ZMS maturation assessment. SKMamba adopts a decoupled dual-path architecture that mimics the hierarchical diagnostic process used by experienced orthodontists. We first introduce an Implicit Edge Extractor (IEE), which leverages structural pre-training to reduce trabecular noise and accentuate sutural boundaries. Complementarily, a Cross-Modal Semantic Alignment (CSA) module is designed to incorporate anatomical descriptions from a large language model (LLM). This module helps align local morphological cues with global semantic descriptions while ensuring that objective morphological evidence remains the primary basis for decisions. Extensive experiments on our ZMS dataset demonstrate that SKMamba achieves state-of-the-art performance compared to existing methods. Code is available at https://github.com/galaxygxq1116/SKMamba.

翻译：颧上颌缝是连接颧骨与上颌骨的关键性上颌周围结构，是上颌前移过程中的主要阻力位点，其成熟状态直接影响矫形干预的时机与疗效。然而，由于缝线区域存在细微的高频过渡特征及相邻分期间的全局语义模糊性，ZMS成熟度的准确分期仍面临挑战。为此，我们首次公开包含3790张覆盖4至24岁全年龄段ZMS图像的公共数据集。基于该数据集，我们提出SKMamba——一种基于结构感知与知识引导的Mamba多模态框架，用于自动化ZMS成熟度评估。SKMamba采用解耦双路径架构，模拟经验丰富的正畸医师的分级诊断流程。首先引入隐式边缘提取器（IEE），通过结构预训练降低骨小梁噪声并突显缝线边界。作为补充，我们设计跨模态语义对齐（CSA）模块，融合来自大语言模型（LLM）的解剖学描述，协助将局部形态线索与全局语义描述对齐，同时确保客观形态学证据仍为决策的主要依据。在ZMS数据集上的大量实验表明，SKMamba相较现有方法取得了最先进的性能。代码开源于https://github.com/galaxygxq1116/SKMamba。