口腔微笑美学评估是临床治疗规划的关键环节，笑线作为关键美学指标，其精准分类对优化美学修复重建治疗方案具有重要意义。然而，笑线分类需要对唇、龈和齿之间的复杂关系准确判断，医师分析的主观性可能造成误诊。本研究旨在以笑线分类任务为口腔微笑美学智能化初探，对比卷积神经网络（CNN）、大语言模型（LLM）及不同层次医师在口腔微笑线分类任务中的表现。

以公开高质量人脸数据集FFHQ为基础，经图像预处理与标准化标注后构建含1 000张样本的微笑图像标注数据集，标注内容为高、中、低笑线3种类型。采用7种经典CNN模型（VGG16、ResNet34等）与5种代表性多模态LLM（Qwen系列、LLaVA-1.5-7B）进行训练、验证和测试，通过准确率、精确率、召回率及F₁分数比较模型性能，并与不同层级临床医师的评估结果进行对比。

在7种常用CNN模型中，ResNet152模型总体表现最优，分类准确率达83.30%，显著优于其他CNN模型及多模态LLM；高级口腔医师分类准确率为83.00%，与ResNet152性能接近。注意力热力图显示ResNet152模型关注区域与医师相似。

CNN模型在笑线分类任务中具备更高的临床应用潜力，可达到专家水平；LLM的医学图像精细分类能力仍需优化。本研究为口腔美学智能评估系统的开发提供了实验依据与技术参考。

Smile aesthetic evaluation is a critical component of clinical treatment planning. As a key aesthetic indicator, the precise classification of the smile line is crucial for optimizing restorative and reconstructive treatment plans. However, classifying smile line requires accurate assessment of complex relationships among the lips, gingiva and teeth, and analysis by dentists involves a degree of subjectivity and chances for misdiagnosis. This study aimed to investigate smile line classification by comparing the performance of convolutional neural networks (CNNs) and large language models (LLMs) , as well as clinicians of varying expertise levels, in this task.

Based on the publicly available high-quality FFHQ facial dataset, a smile image annotation dataset comprising 1 000 samples was constructed following image preprocessing and standardized annotations of three types: high, medium and low smile line. Seven classic CNN models (VGG16, ResNet34, etc.) and five representative multimodal LLMs (Qwen series, LLaVA 1.5-7B) were employed for training, validation, and testing. Model performance was evaluated using accuracy, precision, recall, and F₁ scores, and compared against assessments made by clinicians of different seniority levels.

Among the seven commonly used CNN models, the ResNet152 model demonstrated optimal overall performance, achieving a mean classification accuracy of 83.30%, which significantly outperformed other CNN models and multimodal LLMs. Senior dentists achieved a classification accuracy of 83.00%, comparable to the performance of ResNet152. Heatmaps demonstrate similar attention regions between ResNet152 and dental practitioners.

CNN models demonstrated substantial clinical potential in smile line classification tasks, attaining expert-level performance. In contrast, large language models required further optimization for medical image fine-grained classification. This study provided experimental evidence and technical insights for developing intelligent aesthetic assessment systems in dentistry.