This study explores the potential of deep learning in detecting meniscus tears using magnetic resonance imaging (MRI), particularly focusing on the effectiveness of YOLOv8 and EfficientNetV2 models. Accurate diagnosis of meniscus injuries is crucial for proper treatment planning, as misdiagnoses can lead to unnecessary procedures or delayed care. Traditional MRI analysis relies heavily on radiologists, making the process time-consuming and subject to variability. The researchers aimed to determine whether artificial intelligence (AI) models could achieve high diagnostic accuracy even with a relatively small dataset. The study analyzed MRI scans from 642 knees, with two orthopedic surgeons manually annotating the images to serve as ground truth. The deep learning approach was divided into two phases: YOLOv8 was used to identify the meniscus location within the MRI, while EfficientNetV2 was applied to detect tears. Despite the small dataset, the models performed exceptionally well. The YOLOv8 model achieved a mean average precision (mAP@50) of 0.98 in the sagittal view and 0.985 in the coronal view, indicating near-perfect localization. Similarly, the EfficientNetV2 model demonstrated an area under the curve (AUC) of 0.97 and 0.98 in the sagittal and coronal views, respectively, showcasing its robust ability to distinguish between healthy and torn meniscus. These results suggest that deep learning models can rival or even exceed human-level accuracy in meniscus tear detection.