# Deep Learning for Orthopedic Disease Based on Medical Image Analysis: Present and Future
## 5 Miscellaneous  
Concerning bone age, attempts to create a model that automatically predicts a bone's age through the learning of plain X-rays of carpal bones have been conducted since before the first deep learning algorithm was developed. [PERSON] et al. presented a bone age prediction model with an accuracy of 82% in 2000, using a regression model and a Bayesian estimator [48]. A CNN model using a deep learning algorithm was developed, and it is now possible to predict the bone age with improved accuracy. In addition, [PERSON] et al. proposed a model with 97.6% accuracy by training the Inception ResNet v2 model with 5876 hand radiographs [39]. For pediatrics, developmental dysplasia of the hip is one of the most common hip joint disorders in infants and young children, and its diagnosis is difficult owing to the extensive variations in pediatric pelvic anatomy [49]. To create a deep learning algorithm that can diagnose developmental dysplasia of the hip, [PERSON] et al. trained a CNN model (based on ResNet-101) using 10,219 pelvic anteroposterior radiographs of children. The trained model showed a high AUC of 0.975 [50]. An acute pediatric elbow fracture is also difficult to diagnose, owing to the existence of multiple cartilaginous ossification centers and a highly variable appearance [51]. [PERSON] et al. trained a CNN using 901 lateral elbow radiographs, and the trained model diagnoses elbow fractures with a high AUC of 0.985 [52]. Central dual-energy X-ray absorptiometry is the reference standard for diagnosing osteoporosis and osteopenia. A CNN model for diagnosing osteopenia and osteoporosis using plain radiography without dual-energy X-ray absorptiometry was recently introduced. [PERSON] et al. trained a CNN model with 2564 lumbar X-ray images, and this model showed an AUC of 0.767 and 0.810 for osteoporosis and osteopenia, respectively [53]. [PERSON] et al. trained a CNN with 1131 hip X-rays, and this model diagnosed osteoporosis with an accuracy of 0.885 [54]. For alignment, [PERSON] et al. published an interesting study using a deep learning algorithm to automatically measure the hip-knee-ankle angle. They trained a CNN model with  
Figure 3: Each row is the same MR slice, and each column is an unsegmented slice (MR Image), an expert measured value (Ground Truth), a trained CNN model predicted value (Prediction) and an overlay of manual and predicted segmentations (Contours Overlay). Figure obtained from a study performed by [PERSON] et al. [47]. 796 unilateral lower limb X-rays, showing a difference of 0.49\({}^{\circ}\) from the ground truth measured directly by orthopedic surgeons [55]. In addition, [PERSON] and [PERSON] et al. trained a CNN model with 600 hip anteroposterior and 600 hip lateral X-rays taken after total hip arthroplasty and programmed this model to automatically derive the acetabular component inclination and version. Compared with the ground truth, this model showed a difference of 1.35\({}^{\circ}\) for the inclination and 1.39\({}^{\circ}\) for the anteversion [56]. [PERSON] et al. presented a CNN model trained using biplanar radiographs of the spine. The model automatically calculated the T4-T12 kyphosis, L1-L5 lordosis, Cobb angle of scoliosis, pelvic incidence, sacral slope and pelvic tilt. Among them, the pelvic tilt showed a difference of 2.7\({}^{\circ}\) compared with the ground truth, whereas the L1-L5 lordosis showed a difference of 11.5\({}^{\circ}\) from the ground truth [56]. Concerning metastasis and infections in the spine, the spine is a joint that receives a high blood supply and is relatively easily exposed to metastasis compared with other joints [57]. Therefore, studies for diagnosing metastatic lesions using deep learning algorithms have mainly focused on the spine. [PERSON] et al. reported that a CNN model trained with sagittal fat-suppressed T2 2D Fast Spin-Echo spine images localized metastatic lesions with a sensitivity of 90% [58]. In addition, [PERSON] et al.