Artificial intelligence (AI)-enabled diagnostics in maxillofacial pathology require structured, high-quality multimodal datasets. However, existing resources provide limited ameloblastoma coverage and lack the format consistency needed for direct model training. We present a newly curated multimodal dataset specifically focused on ameloblastoma, integrating annotated radiological, histopathological, and intraoral clinical images with structured data derived from case reports. Natural language processing techniques were employed to extract clinically relevant features from textual reports, while image data underwent domain specific preprocessing and augmentation. Using this dataset, a multimodal deep learning model was developed to classify ameloblastoma variants, assess behavioral patterns such as recurrence risk, and support surgical planning. The model is designed to accept clinical inputs such as presenting complaint, age, and gender during deployment to enhance personalized inference. Quantitative evaluation demonstrated substantial improvements; variant classification accuracy increased from 46.2 percent to 65.9 percent, and abnormal tissue detection F1-score improved from 43.0 percent to 90.3 percent. Benchmarked against resources like MultiCaRe, this work advances patient-specific decision support by providing both a robust dataset and an adaptable multimodal AI framework.

翻译：颌面病理学中的人工智能辅助诊断需要结构化、高质量的多模态数据集。然而，现有资源对成釉细胞瘤的覆盖有限，且缺乏模型直接训练所需的格式一致性。我们提出了一个专门针对成釉细胞瘤新构建的多模态数据集，该数据集整合了带标注的放射学、组织病理学及口内临床图像，以及从病例报告中提取的结构化数据。我们采用自然语言处理技术从文本报告中提取临床相关特征，同时对图像数据进行了特定领域的预处理与增强。基于此数据集，我们开发了一个多模态深度学习模型，用于对成釉细胞瘤亚型进行分类、评估复发风险等行为模式，并支持手术规划。该模型在设计上可在部署时接收主诉、年龄、性别等临床输入，以增强个性化推理能力。定量评估显示性能显著提升：亚型分类准确率从46.2%提高至65.9%，异常组织检测的F1分数从43.0%提升至90.3%。与MultiCaRe等资源相比，本研究通过提供一个稳健的数据集及一个可适应的多模态人工智能框架，推进了针对患者个体的决策支持。