# Deep Learning for Orthopedic Disease Based on Medical Image Analysis: Present and Future
## 6 Discussion  
Orthopedics, along with dermatology, ophthalmology and cardiology, is the medical field in which research into deep learning algorithms is most actively conducted. Related research has been explosively increasing since 2017, and this trend is expected to continue until the \"new winter\", when the development of artificial intelligence will reach its limit. To date, image analysis studies of orthopedic diseases using deep learning have shown excellent results overall. Several studies have reported that in fractures and osteoarthritis, a trained CNN model has a diagnostic accuracy comparable to that of an expert. The studies also presented satisfactory results for the classification of fractures and osteoarthritis. However, the accuracy of multiclass classification did not reach detection, and studies on small joints presented relatively poor results compared with studies on large joints. Nevertheless, it is expected that this limitation can be overcome for two reasons. First, the CNN model for medical image analysis aims for accurate diagnosis and appropriate classification, and the types of classes required for this purpose are relatively small. When there are few class types, [PERSON] et al. proved that the accuracy can be improved using a CNN model structured as a deeper layer [63]. Therefore, it is expected that the development of a CNN model with deep hyperparameters will increase the accuracy of multiclass classification through medical image analysis. Second, medical images are extremely refined data compared with images used to learn road traffic conditions or climate predictions; that is, researchers can relatively easily obtain appropriate image data without noise, such as different heights of traffic lights or flying birds. This means that even with simple data augmentation such as an affine transformation, an appropriate dataset for training the CNN model can be provided. Therefore, the authors expect that the development of a CNN model and the accumulation of additional medical images will increase the classification accuracy of fractures and osteoarthritis, which are relatively weak compared with the accuracy of diagnosis. In the same context, it is also expected that the diagnosis and classification of joint-specific soft tissue will be improved, owing to the development of deep learning algorithms advantageous for segmentation. Indeed, there are several recent studies that have completed segmentation at a high level [64, 65]. In particular, [PERSON] et al. and others segmented the psoas major muscle through a U-net-based CNN model, and the trained U-net-based CNN model showed an average of 86.6% intersection over union (IoU). U-net is one of the most important semantic segmentation frameworks of CNNs [66] and has the strength of having an architecture that can recognize structural edges. Therefore, U-net is expected to be widely used for segmentation of medical images [67]. Although not in the field of orthopedics, new CNN architectures based on U-Net are continuously being introduced and reporting notable results [68]. [PERSON] et al. performed prostate zonal segmentation with USE-Net, incorporating Squeeze-and-Excitation blocks (SE) into U-Net [69]. [PERSON] et al. showed that the model trained with a dual attention-gated CNN (Focus U-Net), which improved the U-Net, segmented the polyp of the colonoscopy image to a satisfactory level [70]. Studies published in the field of orthopedic surgery have thus far been unable to present a CNN model with a higher level of diagnosis and classification than experts. An in-depth discussion is needed as to whether these results are a problem that can be overcome through data accumulation or the development of a better CNN, or whether they are a natural limitation of a CNN model learned from image data. The authors offer two approaches. First, experts do not solve problems with image data alone.