Meniscal injury represents a common type of knee injury, accounting for over 50% of all knee injuries. The clinical diagnosis and treatment of meniscal injury heavily rely on magnetic resonance imaging (MRI). However, accurately diagnosing the meniscus from a comprehensive knee MRI is challenging due to its limited and weak signal, significantly impeding the precise grading of meniscal injuries. In this study, a visual interpretable fine grading (VIFG) diagnosis model has been developed to facilitate intelligent and quantified grading of meniscal injuries. Leveraging a multilevel transfer learning framework, it extracts comprehensive features and incorporates an attributional attention module to precisely locate the injured positions. Moreover, the attention-enhancing feedback module effectively concentrates on and distinguishes regions with similar grades of injury. The proposed method underwent validation on FastMRI_Knee and Xijing_Knee dataset, achieving mean grading accuracies of 0.8631 and 0.8502, surpassing the state-of-the-art grading methods notably in error-prone Grade 1 and Grade 2 cases. Additionally, the visually interpretable heatmaps generated by VIFG provide accurate depictions of actual or potential meniscus injury areas beyond human visual capability. Building upon this, a novel fine grading criterion was introduced for subtypes of meniscal injury, further classifying Grade 2 into 2a, 2b, and 2c, aligning with the anatomical knowledge of meniscal blood supply. It can provide enhanced injury-specific details, facilitating the development of more precise surgical strategies. The efficacy of this subtype classification was evidenced in 20 arthroscopic cases, underscoring the potential enhancement brought by intelligent-assisted diagnosis and treatment for meniscal injuries.

半月板损伤是膝关节损伤的一种常见类型，占所有膝关节损伤的 50% 以上。半月板损伤的临床诊断和治疗很大程度上依赖于磁共振成像（MRI）。然而，由于综合膝关节 MRI 信号有限且微弱，准确诊断半月板具有挑战性，严重阻碍了半月板损伤的精确分级。在这项研究中，开发了一种视觉可解释精细分级（VIFG）诊断模型，以促进半月板损伤的智能和量化分级。利用多级迁移学习框架，提取综合特征并结合归因注意模块来精确定位受伤位置。此外，注意力增强反馈模块有效地集中并区分具有相似损伤等级的区域。所提出的方法在 FastMRI_Knee 和 Xijing_Knee 数据集上进行了验证，平均分级精度达到 0.8631 和 0.8502，超过了最先进的分级方法，特别是在容易出错的 1 级和 2 级情况下。此外，VIFG 生成的视觉可解释热图可准确描述超出人类视觉能力的实际或潜在半月板损伤区域。在此基础上，针对半月板损伤的亚型引入了一种新的精细分级标准，将 2 级进一步分为 2a、2b 和 2c，与半月板血液供应的解剖学知识保持一致。它可以提供增强的损伤特定细节，促进更精确的手术策略的制定。该亚型分类的有效性已在20例关节镜病例中得到证实，凸显了半月板损伤智能辅助诊断和治疗带来的潜在增强。