BACKGROUND

Anterior cruciate ligament (ACL) and meniscal injuries are commonly diagnosed with multi-plane magnetic resonance imaging (MRI), but most artificial intelligence (AI) models use single-plane input, leaving the roles of each plane underexplored. This study aimed to investigate the differential impacts and interactions of sagittal, coronal, and axial knee MRI planes on the detection of ACL tears and meniscal tears within a deep learning (DL) model.

METHODS

The MRNet dataset, consisting of 1,130 training cases and 120 validation cases, was employed to develop the TripleMRNet model. This model was trained on images from one, two, or three planes, resulting in seven combinations. This study systematically compared diagnostic performance across these combinations with gradient-weighted class activation mapping (Grad-CAM) providing interpretability analysis.

RESULTS

For ACL tear detection, the three-plane model demonstrated the highest performance, achieving an accuracy (ACC) of 0.925, sensitivity (SEN) of 0.944, specificity (SPE) of 0.909, and F1 score of 0.919. The coronal model had the lowest ACC (0.842), SPE (0.833), and F1 score (0.829). For meniscal tear detection, although SEN remained similar across all seven models, the 3-plane model demonstrated superior performance in terms of ACC (0.783), SPE (0.824), and F1 score (0.745). The axial model ranked just below the 3-plane model across these three metrics, with only a slight margin. Conversely, the sagittal model performed the worst, with an ACC of 0.633, SPE of 0.545, and an F1 score of 0.639.

CONCLUSIONS

The sagittal plane was shown to be the most effective for detecting ACL tears, with the axial MR images also demonstrating significant utility. For meniscal tear detection, the axial plane markedly outperformed the other two planes, and the sagittal plane exhibited the poorest performance.