# Deep-Learning Algorithms for Prescribing Insoles to Patients with Foot Pain
## 5 Discussion  
In this study, we developed a deep-learning model for prescribing the appropriate orthotic inside for patients with foot pain. Our developed model automatically determined the foot posture index for the modified root technique and the necessity of heel lift, entire lift, and lateral wedge, medial wedge, and calcaneocuboid arch supports. The MAE of the foot posture index for the modified root technique in our model was 1.6. The micro-average ROC AUC values of the models for determining the necessity of applying heel lift and entire lift were >0.9, and those of lateral wedge, medial wedge, and calcaneocuboid arch supports were 0.79-0.83. Considering that ROC AUC values ranging from 0.7 to 0.8 are considered acceptable, those ranging from 0.8 to 0.9 are considered excellent, and those >0.9 are considered outstanding. Therefore, these results indicate that the ability of our developed model to automatically prescribe orthotic inside ranged from outstanding to acceptable, which supports our proposition [26]. Our developed deep-learning models automatically determine the foot posture index for the modified root technique and the necessity of heel lift, entire lift, and lateral wedge, medial wedge, and calcaneocuboid arch supports. The modified root technique considers forefoot-to-rearfoot structural problems and attempts to hold the rearfoot in its neutral position while maintaining the appropriate forefoot-to-rearfoot relation [27, 28, 29]. This technique modifies the ground reaction force around the subtalar joint axis [27, 28, 29]. The foot posture index is the amount of twisting of the insole to place the rear foot in a neutral position. Heel lift and entire lift are used when the right and left pelvic heights are different [30, 31]. In addition, when the side of the pelvic elevation is tilted anteriorly, a heel lift is applied to the other side. When the side of pelvic elevation is not tilted anteriorly, entire lift is applied. Lateral and medial wedge supports are applied to reduce varus and valgus torques, respectively, during walking [14, 15]. A calcaneocuboid arch support is used when either side of the pelvis moves backward. A calcaneocuboid arch support is applied  
Figure 4: Confusion matrix of the deep-learning model for the prescription of orthotic insoles. (**A**) heel lift (**B**) entire lift (**C**) lateral wedge (**D**) medial wedge (**E**) calcaneocuboid arch. to the side on which the pelvis moves more backward than forward. These prescriptions can be helpful for correcting the malalignment of the lower extremities, as they would effectively reduce pain due to malalignment that occurs in any part of the leg, not only limited to the foot. When prescribing orthotic insoles, clinicians should consider all the data of the RCSP and the static and dynamic states of the pelvis. Without a deep-learning model, clinicians should prescribe orthotic insoles by considering all these results. Therefore, for the appropriate prescription for patients with foot pain, a lot of experience in the field of foot pain and orthotic insoles is necessary. Incorrect prescription leads to the application of inappropriate insoles, which can aggravate patients' foot pain. We used data on insole prescriptions written by podiatrists with abundant experience in prescribing insoles. We believe that the automatic prescription deep-learning model that we developed can relieve the burden on clinicians of appropriately prescribing orthotic insoles. The DNN model was trained using an artificial neural network structure based on the neural network structure of the human brain [32]. A DNN is a powerful machine learning algorithm that is implemented by stacking hidden layers of neural networks between the input and output layers [20; 21]. It attempts high-level abstraction by combining several non-linear transformations [33]. Deep learning uses a large amount of data for training and creating a model capable of processing new data [34].