A multi-view graph neural network approach for magnetic resonance imaging-based diagnosis of knee injuries

The knee plays a pivotal role in the human anatomy, serving as a cornerstone for support, mobility, shock attenuation, and balance. Currently, magnetic resonance imaging (MRI) remains the preferred method for diagnosing knee injuries, including anterior cruciate ligament (ACL) tears and meniscal tears, due to its efficiency and accuracy in medical imaging. However, the interpretation and understanding of knee MRI images are time-consuming, laborious, require sufficient expertise, and are also prone to diagnostic errors. Thus, it is imperative to devise a computational method employing knee MRI for intelligent diagnosis of knee injuries, as this could expedite medical assessments by physicians, reduce costs, and substantially reduce the risk of misdiagnosis. Although several computational methods have been proposed to diagnose knee injuries, most rely heavily on local features in MRI images and exhibit low prediction accuracy. In this paper, we proposed a novel multi-view graph neural network, abbreviated as MVGNN, to identify knee injuries (specifically ACL tears and meniscal tears) by leveraging graph representations derived from multiple MRI views. Comprehensive experiments demonstrate that MVGNN achieves state-of-the-art results for diagnosing knee injuries, with a 5.9% improvement in accuracy on ACL data and a 6.5% improvement on Men data, compared to the second-best method, MVCNN.