# Deep Learning for Orthopedic Disease Based on Medical Image Analysis: Present and Future
## 6 Discussion
Experts can utilize information other than images, such as the patient's demographic data, the degree of pain, the nature of the disease and a physical examination, which can affect the disease diagnosis and classification. Indeed, [PERSON] et al. reported that a CNN model trained by adding demographic information (age, sex and body mass index), alignment and metabolic data that could affect knee osteoarthritis showed a statistically significantly higher AUC [32]. Therefore, even if an improved CNN model is developed and high-quality image data are accumulated, there is a possibility that the image analysis-based CNN model using a deep learning algorithm will not reach the level of experts. Second, despite the opinions presented above, the possibility that CNN models will outperform experts in certain fields cannot be excluded, because the CNN model analyzes images from a different point of view than human beings. Among 150 images of scaphoid fractures, [PERSON] et al. included 23 scaphoid fracture image data that could only be confirmed through an MRI. The trained CNN model showed a lower level of accuracy than orthopedic surgeons, but it detected five of six occult scaphoid fractures that were missed by all human observers [23]. It is therefore necessary to carefully discuss whether an image analysis model using deep learning can outperform experts. It is clear that the present CNN models have room for improvement. However, this does not undermine the significance of the studies conducted to date. The currently developed CNN model can reduce the task intensity of the expert reader and can be used for the education of non-expert medical workers, such as medical students or specialists during training [71]. In addition, through a developed CNN model, a pediatrician can roughly estimate a patient's bone age using only X-rays without the help of an orthopedic surgeon. A step away from the fate of clinical doctors and CNN's accuracy battle, there are interesting and more practical studies that give practical help to patients and doctors. [PERSON] et al. converted native medical CT images to higher resolution images through generative adversarial networks (GANs) [72], and this study has the potential to be extended to MRI images [73]. Therefore, it can help a society that has no choice but to use low-quality MRI due to insufficient medical infrastructure or patients who have difficulty using high-quality MRI due to cost problems. The authors reviewed deep learning approaches for orthopedic diseases applied through image analysis and found some limitations. First, there are no models approved by the Food and Drug Administration, other than a CNN model for predicting the bone age in children and a model for diagnosing wrist fractures [74]. In other medical departments, several models have been approved by the Food and Drug Administration, starting with a deep learning-based model for the automatic diagnosis of diabetic retinopathy in April 2018. Second, no prospective studies have been conducted [75]. To improve the quality of research and continue applicable studies, a prospective and randomized trial according to the CONSORT-AI guidelines presented in 2020 will be necessary [76]. Third, recently described deep learning methods have mostly been designed to conduct a single task. To be useful in clinical practice, multiple deep learning algorithms will need to evaluate every possible abnormality. Some efforts have been made to overcome these limitations. For example, [PERSON] et al. presented a CNN model for diagnosing fractures, joint dislocation and osteoarthritis through plain shoulder radiographs [77]. Finally, there is a need to reduce expert bias on a given dataset. Orthopedic surgeons have traditionally used ultrasound, computed tomography or MRIs to diagnose soft tissue diseases. However, deep learning algorithms often make appropriate judgments beyond human cognition. [PERSON] et al.