# Deep Learning for Orthopedic Disease Based on Medical Image Analysis: Present and Future
## 2 Deep Learning for Fractures  
Fractures are the most familiar ailments to orthopedists and the medical area in which deep learning methods were first applied. In 2018, [PERSON] et al. published a CNN model for diagnosing and classifying proximal humerus fractures. Three specialists labeled 1891 anteroposterior shoulder radiographs as normal shoulders (n = 515) and 4 proximal humerus fracture types (greater tuberosity: 346; surgical neck: 514; 3-part: 269; and 4-part: 247) [15]. After labeling, a CNN model (ResNet-152) was trained with a training dataset created through augmentation of the labeled data. The CNN model recorded 96% accuracy for the normal shoulders and proximal humerus fractures, showing a higher accuracy than a general orthopedist (92.8% accuracy). This model showed a top-1 accuracy of 65-86% and an area under the curve (AUC) of 0.90-0.98 for classifying the fracture types. A recently published paper introduced a model with improved classification accuracy. In 2020, [PERSON] et al. introduced a deep learning model to diagnose and classify humerus fractures using the exemplar pyramid method, a novel, stable feature extraction approach which showed a high classification accuracy of 99.12% [16]. [PERSON] et al. trained the VGG-16 CNN model using hip plain radiographs (1773 intertrochanter hip fracture images and 1573 normal hip images) and showed an accuracy of 95.5% [17]. [PERSON] et al. trained the CNN model (Xception architectural) based on 3123 hip plain and lateral radiography images, and the trained model classified fractures with 98% accuracy, which is better than orthopedists (92.2% accuracy) [18]. For the hip, as with the shoulder, there has been an attempt to classify fractures by training the CNN model. [PERSON] et al. introduced a CNN model for training 786 anteroposterior pelvic plan radiographs using GoogLeNet-inception v3 [19]. The model classified a proximal femur fracture into type A (trochanter region), type B (femur neck) and type C (femoral head) according to AO/OTA classification with an overall accuracy of 86.8%, showing a reasonable result. [PERSON] et al. trained a ResNet-based CNN with anteroposterior and lateral knee radiographs, amounting to 6768 images [20]. The trained CNN model classified knee radiographic images according to the AO/OTA classification system and classified proximal tibia fractures, patellar fractures and distal femur fractures with AUCs of 0.87, 0.89 and 0.89, respectively. The trained CNN diagnosed and classified fractures at a relatively high level in the large appendices of the shoulder, knee and hip. By contrast, a CNN model trained to diagnose and classify fractures in small joints or axial joints showed a relatively low AUC and accuracy. [PERSON] et al. trained a PCANet-based CNN model that classified calcaneal fractures according to Sanders classification using computer tomography with 5534 datasets [21]. The trained CNN model showed 72% accuracy. In addition, [PERSON] et al. trained a CNN model based on ResNet50 with 390 anteroposterior wrist radiographic images [22]. The AUC of the learned CNN was 0.84, showing a relatively satisfactory result, but it was lower than that of experienced orthopedists. [PERSON] et al. compared the scaphoid fracture diagnostic accuracy between a deep learning algorithm and an orthopedist [23]. They trained the VGG16 CNN model with 150 radiographic images of scaphoid fractures and 150 images of normal wrist radiography without a fracture. Of the 150 images with scaphoid fractures, 23 could not be judged by the radiographic images and could only be confirmed through magnetic resonance imaging (MRI). The accuracy of the trained CNN model was 72%, which was lower than that of an orthopedic surgeon (84%). However, five of six occult scaphoid fractures were missed by all human observers. An attempt was also made to diagnose the compression fractures in the spine using a trained CNN. The results showed a significant difference depending on the type of data used for learning. [PERSON] et al.