# Deep Learning for Orthopedic Disease Based on Medical Image Analysis: Present and Future
## 3 Deep Learning for Osteoarthritis and Prediction of Arthroplasty Implants  
Osteoarthritis is as familiar to orthopedists as fractures. Therefore, several attempts have been made to diagnose and classify osteoarthritis using deep learning algorithms. [PERSON] et al. trained a CNN model based on VGG-16 with 420 plain hip X-rays [27]. This is one of the earliest studies to apply deep learning methods to the orthopedic field, and the trained model diagnosed hip osteoarthritis with an accuracy of 92.8%. [PERSON] et al. also presented a model for diagnosing hip osteoarthritis using a similar research design, showing an accuracy of 90.2% [28]. [PERSON] et al. trained a CNN model to classify knee osteoarthritis according to the Kellgren-Lawrence grading scale using a Siamese classification CNN [29]. The model trained using plain knee X-rays showed a multiclass accuracy of 66.7%. In addition, [PERSON] et al. trained a Faster R-CNN using plain and lateral knee X-rays from the Multicenter Osteoarthritis Study dataset [30]. The multiclass accuracy of this model was 71.9%, which showed improved performance compared with the previous study conducted by [PERSON] et al. [PERSON] et al. trained a DenseNet-based CNN based on MRI-T2 images rather than X-ray data, as used in previous studies, and this model showed a high AUC of 0.83 [31]. [PERSON] et al. trained an SE-ResNet-based CNN model using 4366 knee anteroposterior X-rays as a dataset. Furthermore, they trained the model by adding demographic information (age, sex and body mass index), alignment and metabolic data information that can affect knee osteoarthritis, in addition to image information [32]. The diagnostic performance of the image data with additional patient information showed a significantly higher AUC (Table 2). Advanced osteoarthritis of the hip or knee often requires arthroplasty. Several studies have introduced a model for classifying arthroplasty implants used by patients with deep learning algorithms. [PERSON] et al. trained the InceptionV3 network-based CNN model using anteroposterior knee X-rays with nine different implant models inserted [33]. The trained model showed an accuracy of 99% and an AUC of 0.99, classifying the implant models at an almost perfect level. A similar attempt was made at the hip joint. In addition, [PERSON] et al. created a CNN model trained on 252 plain hip X-rays containing 3 different implant designs, and this model classified implants with 100% accuracy (Figure 2) [34]. [PERSON] et al. also developed a CNN model trained on 170 plain hip X-rays containing 29 different implant designs. This model also showed a high level of performance, with an AUC of 0.99 [35]. \begin{table}
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By contrast, the model classifying shoulder arthroplasty implants showed a relatively low AUC. [PERSON] et al. developed a CNN model trained on 597 plain shoulder X-rays with 16 different implant designs, showing an accuracy of 80% [36]. In addition, [PERSON] et al. proposed a model for classifying the different designs of four manufacturers using modified ResNet and DenseNet, showing an accuracy of 85.9% [37]. In summary, as in the case of using deep learning for fractures, binary classification of osteoarthritis has a higher accuracy than multiclass classification. In particular, the CNN-based model for specifying arthroplasty implants of the hip or knee shows a high accuracy. This may be because, unlike human bone, the implant design is highly standardized, demonstrating a clear margin on X-rays and providing clear contrast information to the CNN model. However, the classification of shoulder arthroplasty implants shows a low