发表机构
Cleveland Clinic; Cleveland State University; Case Western Reserve University; Kent State University（克利夫兰诊所; 克利夫兰州立大学; 凯斯西储大学; 肯特州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出Knee3DVLM，一种利用DESS和TSE双序列全容积MRI的视觉-语言模型，预测57个MOAKS诊断目标，在1,074例队列中实现最高性能，支持全面膝关节评估。
AI 中文摘要
视觉-语言模型（VLMs）正日益应用于三维医学影像，但其在膝关节MRI中的应用仍然有限，尤其是在解释临床实践中使用的互补序列方面。我们提出了Knee3DVLM，一种序列感知的VLM，利用全容积DESS和液体敏感TSE MRI来预测从MRI骨关节炎膝关节评分（MOAKS）中导出的57个解剖学解析的二元诊断目标，用于结构化报告。我们使用受试者不相交的骨关节炎倡议分区评估了仅DESS、仅TSE以及配对DESS-TSE配置。在一个由1,074例检查组成的留出队列中，融合模型达到了72.98%的平均准确率、71.17%的平衡准确率、78.96%的平均ROC-AUC和78.74%的宏ROC-AUC，这是三种配置中最高的值。在与发布的3DReasonKnee队列对齐的次要多类分析中，Knee3DVLM在五个病理类别上的数值高于最强的已报告3DReasonKnee配置。这些发现支持双序列全容积建模用于全面的膝关节MRI评估。
英文摘要
Vision-language models (VLMs) are increasingly being applied to three-dimensional medical imaging, but their application to knee MRI remains limited, particularly for interpreting the complementary sequences used in clinical practice. We introduce Knee3DVLM, a sequence-aware VLM that uses full-volume DESS and fluid-sensitive TSE MRI to predict 57 anatomically resolved binary diagnostic targets derived from the MRI Osteoarthritis Knee Score (MOAKS) for structured reporting. We evaluated DESS-only, TSE-only, and paired DESS-TSE configurations using subject-disjoint Osteoarthritis Initiative partitions. In a held-out cohort of 1,074 examinations, the fused model achieved 72.98% average accuracy, 71.17% balanced accuracy, 78.96% mean ROC-AUC, and 78.74% macro ROC-AUC, the highest values among the three configurations. In a secondary multiclass analysis aligned with the released 3DReasonKnee cohort, Knee3DVLM was numerically higher than the strongest reported 3DReasonKnee configuration across five pathology categories. These findings support dual-sequence full-volume modeling for comprehensive knee MRI assessment.
Comments11 pages, 2 figures, 5 tables
