一种用于通用膝部病理的多模态视觉基础模型
A multimodal vision foundation model for generalizable knee pathology
AI总结：
OrthoFoundation是一种多模态视觉基础模型，通过自监督学习在膝关节影像上实现高泛化能力，提升骨科影像诊断的准确性和效率。
摘要：
肌肉骨骼疾病是全球残疾的主要原因之一，这催生了对医学影像精确解读的迫切需求。当前在骨科领域的人工智能（AI）方法大多依赖于任务特定的监督学习范式。这些方法本质上是碎片化的，需要大量的标注数据集，并且往往缺乏在不同模态和临床场景中的泛化能力。在该领域开发基础模型受到大规模、经过整理和开源的肌肉骨骼数据集稀缺的限制。为了解决这些挑战，我们引入了OrthoFoundation，一种针对肌肉骨骼病理优化的多模态视觉基础模型。我们构建了一个包含120万张未标注的膝关节X光和MRI图像的预训练数据集，来自内部和公共数据库。利用Dinov3主干网络，该模型通过自监督对比学习进行训练，以捕捉稳健的放射学表示。OrthoFoundation在14个下游任务上实现了最先进的（SOTA）性能。它在X光骨关节炎诊断中表现出卓越的准确性，并在MRI结构损伤检测中排名第一。该模型展示了显著的标签效率，仅使用50%的标注数据即可匹配监督基线。此外，尽管是在膝关节图像上预训练的，OrthoFoundation在髋部、肩部和踝部等其他解剖部位也表现出色的跨解剖泛化能力。OrthoFoundation代表了迈向通用骨科影像AI的重要进展。通过从大规模多模态数据中学习基本的、关节无关的放射学语义，它克服了传统模型的局限性，提供了一个坚固的框架，以减少标注负担并提高临床诊断的准确性。
英文摘要：
Musculoskeletal disorders represent a leading cause of global disability, creating an urgent demand for precise interpretation of medical imaging. Current artificial intelligence (AI) approaches in orthopedics predominantly rely on task-specific, supervised learning paradigms. These methods are inherently fragmented, require extensive annotated datasets, and often lack generalizability across different modalities and clinical scenarios. The development of foundation models in this field has been constrained by the scarcity of large-scale, curated, and open-source musculoskeletal datasets. To address these challenges, we introduce OrthoFoundation, a multimodal vision foundation model optimized for musculoskeletal pathology. We constructed a pre-training dataset of 1.2 million unlabeled knee X-ray and MRI images from internal and public databases. Utilizing a Dinov3 backbone, the model was trained via self-supervised contrastive learning to capture robust radiological representations. OrthoFoundation achieves state-of-the-art (SOTA) performance across 14 downstream tasks. It attained superior accuracy in X-ray osteoarthritis diagnosis and ranked first in MRI structural injury detection. The model demonstrated remarkable label efficiency, matching supervised baselines using only 50% of labeled data. Furthermore, despite being pre-trained on knee images, OrthoFoundation exhibited exceptional cross-anatomy generalization to the hip, shoulder, and ankle. OrthoFoundation represents a significant advancement toward general-purpose AI for musculoskeletal imaging. By learning fundamental, joint-agnostic radiological semantics from large-scale multimodal data, it overcomes the limitations of conventional models, which provides a robust framework for reducing annotation burdens and enhancing diagnostic accuracy in clinical practice.
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