# Deep-Learning Algorithms for Prescribing Insoles to Patients with Foot Pain
###### Abstract  
Foot pain is a common musculoskeletal disorder. Orthotic insoles are widely used in patients with foot pain. Inexperienced clinicians have difficulty prescribing orthotic insoles appropriately by considering various factors associated with the alteration of foot alignment. We attempted to develop deep-learning algorithms that can automatically prescribe orthotic insoles to patients with foot pain and assess their accuracy. In total, 838 patients were included in this study; 70% (m = 586) and 30% (n = 252) were used as the training and validation sets, respectively. The resting calcaneal stance position and data related to pelvic elevation, pelvic tilt, and pelvic rotation were used as input data for developing the deep-learning algorithms for insole prescription. The target data were 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. In the results, regarding the foot posture index for the modified root technique, for the left foot, the mean absolute error (MAE) and root mean square error (RMSE) of the validation dataset for the developed model were 1.408 and 3.365, respectively. For the right foot, the MAE and RMSE of the validation dataset for the developed model were 1.601 and 3.549, respectively. The accuracies for heel lift, entire lift, and lateral wedge, medial wedge, and calcaneocuboid arch supports were 89.7%, 94.8%, 72.2%, 98.4%, and 79.8%, respectively. The micro-average area under the receiver operating characteristic curves for heel lift, entire lift, and lateral wedge, medial wedge, and calcaneocuboid arch supports were 0.949, 0.941, 0.826, 0.792, and 0.827, respectively. In conclusion, our deep-learning models automatically prescribed orthotic insoles in patients with foot pain and showed outstanding to acceptable accuracy. insole; foot pain; deep learning; prescription +
Footnote †: journal: 9 February 2023
in abnormally distributed loads on the foot [6, 7]. Orthotic insoles are frequently used to correct altered foot alignments [8, 9]. The usefulness of orthotic insoles for patients with foot pain has been demonstrated in several previous studies, and they have been widely applied in clinical practice for the treatment of foot pain [10, 11, 12]. Additionally, the asymmetrical motion between the right and left lower limbs and pelvic malalignment is kinetically associated with abnormal foot motion [13]. A misalignment can create excessive load on certain foot structures, causing foot pain. To correct the symmetry between the lower limbs and pelvic malalignment, the height of orthotic insoles can be adjusted by lifting the insole. The asymmetric movement of the pelvis can be corrected by applying lateral wedge, medial wedge, and calcaneocuboid arch supports to the insole and lifting the heel [14, 15]. The rotation of the pelvis to one side can be blocked using lateral wedge, medial wedge, and calcaneocuboid arch supports. The tilt of one side of the pelvis toward the anterior region can be corrected by a heel lift. A lot of experience should be accumulated to prescribe orthotic insoles appropriately. To apply the appropriate orthotic insole, clinicians should consider various factors such as foot alignment, pelvic movement, and leg length discrepancy [13, 16]. The comprehensive consideration of various factors associated with the alteration of foot alignment is difficult for inexperienced clinicians. An algorithm that automatically prescribes the orthotic insole would be of great help to clinicians treating patients with foot pain. Machine learning is an artificial intelligence technique that involves system learning rules and patterns from given data [17, 18, 19]. It has several advantages for determining the appropriate prescription for patients with various disorders [17, 18, 19]. The deep-learning technique is an advanced machine learning approach [20, 21].