Virtual Surgical Planning and Patient-Specific Instruments for Correcting Lower Limb Deformities in Pediatric Patients: Preliminary Results from the In-Office 3D Printing Point of Care.

(1) Background: Virtual reality and 3D printing are transforming orthopedic surgery by enabling personalized three-dimensional (3D) models for surgical planning and Patient-Specific Instruments (PSIs). Hospitals are establishing in-house 3D printing centers to reduce costs and improve patient care. Pediatric orthopedic surgery also benefits from these technologies, enhancing the precision and personalization of treatments. This study presents preliminary results of an In-Office 3D Printing Point of Care (PoC), outlining considerations and challenges in using this program for treating lower limb deformities in pediatric patients through Virtual Surgical Planning (VSP) and 3D-printed Patient-Specific Instruments (PSIs). (2) Materials and Methods: Pediatric patients with congenital or acquired lower limb deformities undergoing surgical correction based on VSP, incorporating 3D-printed PSIs when required, were included in this study. The entire process of VSP and 3D printing at the In-Office PoC was illustrated. Data about deformity characteristics, surgical procedures, and outcomes, including the accuracy of angular correction, surgical times, and complications, were reported. (3) Results: In total, 39 bone correction procedures in 29 patients with a mean age of 11.6 ± 4.7 years (range 3.1-18.5 years) were performed according to VSP. Among them, 23 procedures were accomplished with PSIs. Surgeries with PSIs were 45 min shorter, with fewer fluoroscopy shots. Optimal correction was achieved in 37% of procedures, while the remaining cases showed under-corrections (41%) or over-corrections (22%). Major complications were observed in four patients (13.8%). (4) Conclusions: The In-Office 3D Printing Point of Care is becoming an essential tool for planning and executing complex corrections of lower limb deformities, but additional research is needed for optimizing the prediction and accuracy of the achieved corrections.