# Deep Learning for Orthopedic Disease Based on Medical Image Analysis: Present and Future
## 4 Deep Learning for Joint-Specific Soft Tissue Disease  
As for deep learning approaches, an algorithm specialized for detection based on learned images and an algorithm for segmentation by analyzing features have structural differences and have developed into different areas of application [3]. In particular, segmentation has technical difficulties in that it is necessary to preserve spastic information that is easily lost in the outer-layer process of synthesizing the results of the CNN model being trained [38]. Recent studies have attempted to overcome these limitations through techniques such as FCN-based semantic segmentation.  
These differences in deep learning algorithms also affect the use of deep learning in the orthopedic field. The deep learning-based studies introduced above are cases of diagnosing and classifying diseases based on X-ray images, and a CNN model specialized for segmentation is not always required [39]. By contrast, for diseases that are diagnosed and classified based on images such as ultrasound or MRI, a satisfactory level of accuracy can be obtained using only a CNN model specialized for segmentation. For example, a CNN model for diagnosing rotator cuff tears is more appropriate for inferring such tears based on the outline of the normal rotator cuff (segmentation) than a method of diagnosis applied by specifying the location where the tear occurred (regional detection).  
Therefore, CNN models for diagnosing soft tissue disease in the orthopedic field have mainly been published after 2018, which was when the segmentation technology began to mature. [PERSON] et al. trained a CNN model using a shoulder MRI dataset of 240 patients. The trained model identified the muscle region of the rotator cuff with an accuracy of 99.9% and graded fatty infiltration at a high level [40]. [PERSON] et al. also conducted a similar study using a shoulder computed tomography of 103 patients as a dataset. The trained CNN model measured fatty infiltration with an accuracy of 91% [41].  
[PERSON] et al. introduced a model for segmenting the rotator cuff muscle with 98% accuracy by applying a CNN model trained using the shoulder MRIs of 258 patients [42]. Furthermore, [PERSON] and [PERSON] et al. introduced a model for evaluating the presence of tears and their sizes in the rotator cuff by training a Voxception-ResNet (VRN)-based CNN with 2124 shoulder MRIs. The trained CNN model diagnosed and classified rotator cuff tears with accuracies of 92.5% and 76.5%, respectively [10]. In addition, [PERSON] et al. developed a new deep learning architecture using an integrated positive loss function and a pre-trained encoder. Using this, the location of the rotator cuff tear can be relatively accurately determined, even when imbalanced and noisy ultrasound images are provided [43].  
Recent studies suggesting a CNN model for diagnosing meniscal tears, cartilage lesions and anterior cruciate ligament (ACL) ruptures in the knee joint have also been published. [PERSON] et al. presented a model that trains a Mask-RCNN with 1828 T2-weighted 2D Fast Spin-Echo images to classify the torn part from the normal area of the meniscus and do so according to the location of the tear [44]. This model diagnosed and classified meniscal tears with an AUC of 0.91. [PERSON] et al. also proposed a model for diagnosing meniscal tears in a similar way, detecting meniscal tears with an AUC of 0.94 [45].  
[PERSON] et al. presented a model for diagnosing complete ACL tears by training a U-Net-based CNN using 320 coronal proton density-weighted 2D Fast Spin-Echo images, demonstrating an AUC of 0.97 [46]. In addition, [PERSON] et al. trained a modified U-Net-based CNN and evaluated the level of segmentation of the model. The segmentation level suggested by the trained model did not show a statistically significant difference from the ground truth (the value actually suggested by an expert) (Figure 3) [47].