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% (n = 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.
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