# Deep Learning for Orthopedic Disease Based on Medical Image Analysis: Present and Future
###### Abstract  
Since its development, deep learning has been quickly incorporated into the field of medicine and has had a profound impact. Since 2017, many studies applying deep learning-based diagnostics in the field of orthopedics have demonstrated outstanding performance. However, most published papers have focused on disease detection or classification, leaving some unsatisfactory reports in areas such as segmentation and prediction. This review introduces research published in the field of orthopedics classified according to disease from the perspective of orthopedic surgeons, and areas of future research are discussed. This paper provides orthopedic surgeons with an overall understanding of artificial intelligence-based image analysis and the information that medical data should be treated with low prejudice, providing developers and researchers with insight into the real-world context in which clinicians are embracing medical artificial intelligence.  
artificial intelligence; orthopedics; neural network; deep learning +
Footnote †: journal: Medical Image Analysis  
12 November 20214 January 2022  
11 January 2022  
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improve the explainability. Various strategies and suitable CNN architectures have been proposed to implement an appropriate XAI [7]. Unfortunately, the black box nature of deep learning has not been completely resolved, but there are some notable achievements [8]. As one of these achievements, in 2016, [PERSON] et al. introduced a method explaining how a CNN makes a decision through class activation mapping [9], and this method is widely used in the field of medical artificial intelligence (Figure 1) [10].  
In a similar context, there are attempts to improve the explainability by improving the existing CNN architecture [11]. [PERSON] et al. modified U-Net, a CNN architecture that has strength in image segmentation, to appropriately increase the explainability. They presented an interpretable version of U-Net (SAU-Net) using an attention module for the decoder part [12].  
Hence, studies introducing CNN models for diagnosing and classifying diseases using deep learning have been published in various fields of medicine, including ophthalmology and dermatology [13,14].  
This trend is spreading rapidly in the field of orthopedics. Since 2017, when orthopedic disease research using deep learning was first introduced, the number of related papers has increased rapidly, and more than 300 papers in this area have been published. The search was conducted using Pubmed, MEDLINE and Embase, and papers were screened from 1 January 2017 to 2 November 2021. The search query was (orthopedic OR orthopedic) AND (deep learning). Among these studies, two orthopedic surgeons ([PERSON] and [PERSON]) independently reviewed the full text of the retrieved papers. Among these studies, 48 studies which both authors judged to be interesting and practical within the clinical context of orthopedic surgery are introduced and classified according to disease. This paper aims to provide insight into how medical artificial intelligence can help orthopedic surgeons treat patients vividly and in what context clinicians are accepting medical artificial intelligence from developers and researchers.  
The authors introduce the selected papers by classifying them into the following sections: (1) Deep Learning for Fractures, (2) Deep Learning for Osteoarthritis and the Prediction of Arthroplasty Implants, (3) Deep Learning for Joint-Specific Soft Tissue Disease, (4) Miscellaneous and (5) Discussion.  
Figure 1: Image highlighting the location and size of a rotator cuff tear through a class activation map (CAM). Figure obtained from a study performed by [PERSON] et al. [10].