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Background: Hinge fractures in varus-producing distal femoral osteotomies (DFOs) lead to decreased axial and torsional stability. The purpose of this study was to assess (1) which hinge width has a high risk of hinge fracture in DFO for lateral opening wedge (LOW) and medial closing wedge (MCW) osteotomies, (2) which osteotomies allow for greater correction before risking a fracture, (3) whether patient-specific instrumentation (PSI) allows accurate hinge width planning., Methods: Thirty porcine femoral bones were divided into two groups: LOW, MCW with hinge widths of 5 mm, 7.5 mm, and 10 mm as subgroups. Osteotomies were performed in a PSI-navigated fashion. A force parallel to the longitudinal bone axis was applied in a uniaxial testing machine until a fracture occurred., Results: The maximum correction was 6.7 ± 1.1° for LOW and 13.4 ± 1.9° for MCW (β 0  < 0.001 , β 1  = 0.002, β 2  = 0.02 , β 3  = 0.005). The relative error of the planned hinge width compared with the actual hinge width was -3.7 ± 12.3% for LOW (P = 0.25) and 12.3 ± 13.1% for MCW (P = 0.003)., Conclusions: Increasing the hinge width allows for greater correction in MCW osteotomies. For LOW osteotomies, a smaller hinge width seems to be advantageous because it allows a greater correction without the risk of hinge fracture. With PSI-guided LOW osteotomies, the planned hinge width could be achieved intraoperatively with greater accuracy than with MCW osteotomies. However, the MCW osteotomy appears to be the preferred option when larger corrections are desired because a larger correction angle can be achieved without the risk of intraoperative hinge fracture., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

3D preoperative planning for high tibial osteotomies (HTO) has increasingly replaced 2D planning but is complex, time-consuming and therefore expensive. Several interdependent clinical objectives and constraints have to be considered, which often requires multiple rounds of revisions between surgeons and biomedical engineers. We therefore developed an automated preoperative planning pipeline, which takes imaging data as an input to generate a ready-to-use, patient-specific planning solution. Deep-learning based segmentation and landmark localization was used to enable the fully automated 3D lower limb deformity assessment. A 2D-3D registration algorithm allowed the transformation of the 3D bone models into the weight-bearing state. Finally, an optimization framework was implemented to generate ready-to use preoperative plannings in a fully automated fashion, using a genetic algorithm to solve the multi-objective optimization (MOO) problem based on several clinical requirements and constraints. The entire pipeline was evaluated on a large clinical dataset of 53 patient cases who previously underwent a medial opening-wedge HTO. The pipeline was used to automatically generate preoperative solutions for these patients. Five experts blindly compared the automatically generated solutions to the previously generated manual plannings. The overall mean rating for the algorithm-generated solutions was better than for the manual solutions. In 90% of all comparisons, they were considered to be equally good or better than the manual solution. The combined use of deep learning approaches, registration methods and MOO can reliably produce ready-to-use preoperative solutions that significantly reduce human workload and related health costs.