Abstract
Oral and maxillofacial radiology plays a vital role in dental healthcare, but radiographic image interpretation is limited by a shortage of trained professionals. While AI approaches have shown promise, existing dental AI systems are restricted by their single-modality focus, task-specific design, and reliance on costly labeled data, hindering their generalization across diverse clinical scenarios. To address these challenges, we introduce DentVFM, the first family of vision foundation models (VFMs) designed for dentistry. DentVFM generates task-agnostic visual representations for a wide range of dental applications and uses self-supervised learning on DentVista, a large curated dental imaging dataset with approximately 1.6 million multi-modal radiographic images from various medical centers. DentVFM includes 2D and 3D variants based on the Vision Transformer (ViT) architecture. To address gaps in dental intelligence assessment and benchmarks, we introduce DentBench, a comprehensive benchmark covering eight dental subspecialties, more diseases, imaging modalities, and a wide geographical distribution. DentVFM shows impressive generalist intelligence, demonstrating robust generalization to diverse dental tasks, such as disease diagnosis, treatment analysis, biomarker identification, and anatomical landmark detection and segmentation. Experimental results indicate DentVFM significantly outperforms supervised, self-supervised, and weakly supervised baselines, offering superior generalization, label efficiency, and scalability. Additionally, DentVFM enables cross-modality diagnostics, providing more reliable results than experienced dentists in situations where conventional imaging is unavailable. DentVFM sets a new paradigm for dental AI, offering a scalable, adaptable, and label-efficient model to improve intelligent dental healthcare and address critical gaps in global oral healthcare.
Abstract (translated)
口腔和颌面放射学在牙科医疗保健中扮演着至关重要的角色，但放射图像的解读受限于训练有素的专业人员短缺。虽然人工智能方法显示出巨大潜力，但现有的牙科AI系统因其单一模态聚焦、任务特异性设计以及对昂贵标记数据的依赖性，在不同临床场景中的泛化能力受到限制。为解决这些挑战，我们推出了DentVFM，这是首个专为牙科设计的视觉基础模型（VFMs）家族。DentVFM生成适用于广泛牙科应用的任务无关视觉表示，并通过在DentVista上进行自我监督学习来实现这一点——这是一个包含来自各种医疗机构的大约160万张多模态放射图像的大型策划数据库。 DentVFM包括基于视觉变换器（ViT）架构的2D和3D变体。为填补牙科智能评估与基准测试中的空白，我们推出了DentBench，这是一个涵盖八个牙科亚专科、更多疾病类型、成像模式以及广泛地理分布的全面基准。实验结果表明，DentVFM在多种牙科任务中展现了强大的通才智慧能力，如疾病诊断、治疗分析、生物标志物识别和解剖地标检测与分割。 实验证明，DentVFM在泛化性能、标签效率和可扩展性方面显著优于监督式学习、自我监督学习以及弱监督基础模型。此外，DentVFM支持跨模态诊断，在传统成像不可用的情况下提供比经验丰富的牙医更为可靠的诊断结果。DentVFM为牙科AI树立了新的范例，通过一个可扩展的、适应性强且标签高效的模型来改进智能牙科医疗保健，并填补全球口腔健康护理中的关键空白。
URL
https://arxiv.org/abs/2510.14532
