Computer Assisted Research and Development Group, Balgrist University Hospital, University of Zurich, Forchstrasse 340, CH-8008 Zurich, Switzerland; Laboratory for Orthopaedic Biomechanics, Institute for Biomechanics, ETH Zürich, Balgrist Campus, Lengghalde 5, CH-8008 Zurich, Switzerland.
Computer Assisted Research and Development Group, Balgrist University Hospital, University of Zurich, Forchstrasse 340, CH-8008 Zurich, Switzerland; Department of Orthopaedics, Balgrist University Hospital, University of Zurich, Forchstrasse 340, CH-8008 Zurich, Switzerland.
Med Image Anal. 2020 Feb;60:101598. doi: 10.1016/j.media.2019.101598. Epub 2019 Nov 2.
Three-dimensional (3D) computer-assisted corrective osteotomy has become the state-of-the-art for surgical treatment of complex bone deformities. Despite available technologies, the automatic generation of clinically acceptable, ready-to-use preoperative planning solutions is currently not possible for such pathologies. Multiple contradicting and mutually dependent objectives have to be considered, as well as clinical and technical constraints, which generally require iterative manual adjustments. This leads to unnecessary surgeon efforts and unbearable clinical costs, hindering also the quality of patient treatment due to the reduced number of solutions that can be investigated in a clinically acceptable timeframe. In this paper, we propose an optimization framework for the generation of ready-to-use preoperative planning solutions in a fully automatic fashion. An automatic diagnostic assessment using patient-specific 3D models is performed for 3D malunion quantification and definition of the optimization parameters' range. Afterward, clinical objectives are translated into the optimization module, and controlled through tailored fitness functions based on a weighted and multi-staged optimization approach. The optimization is based on a genetic algorithm capable of solving multi-objective optimization problems with non-linear constraints. The framework outputs a complete preoperative planning solution including position and orientation of the osteotomy plane, transformation to achieve the bone reduction, and position and orientation of the fixation plate and screws. A qualitative validation was performed on 36 consecutive cases of radius osteotomy where solutions generated by the optimization algorithm (OA) were compared against the gold standard solutions generated by experienced surgeons (Gold Standard; GS). Solutions were blinded and presented to 6 readers (4 surgeons, 2 planning engineers), who voted OA solutions to be better in 55% of the time. The quantitative evaluation was based on different error measurements, showing average improvements with respect to the GS from 20% for the reduction alignment and up to 106% for the position of the fixation screws. Notably, our algorithm was able to generate feasible clinical solutions which were not possible to obtain with the current state-of-the-art method.
三维（3D）计算机辅助矫正截骨术已成为治疗复杂骨畸形的最新技术。尽管有可用的技术，但对于此类病变，目前还无法自动生成临床可接受的、可直接使用的术前规划解决方案。必须考虑多个相互矛盾且相互依存的目标，以及临床和技术限制，这通常需要进行迭代的手动调整。这导致了不必要的手术努力和难以承受的临床成本，也由于在临床可接受的时间内可以研究的解决方案数量减少，从而影响了患者治疗的质量。在本文中，我们提出了一种优化框架，用于全自动生成可直接使用的术前规划解决方案。使用患者特定的 3D 模型进行自动诊断评估，用于 3D 愈合不良的定量评估和优化参数范围的定义。此后，临床目标被转换到优化模块，并通过基于加权和多阶段优化方法的定制适应度函数进行控制。优化基于能够解决具有非线性约束的多目标优化问题的遗传算法。该框架输出完整的术前规划解决方案，包括截骨平面的位置和方向、实现骨减少的变换，以及固定板和螺钉的位置和方向。在 36 例连续桡骨截骨病例中进行了定性验证，其中优化算法（OA）生成的解决方案与经验丰富的外科医生生成的黄金标准解决方案（GS）进行了比较。解决方案是盲目的，并呈现给 6 位读者（4 位外科医生，2 位规划工程师），其中 55%的时间读者投票认为 OA 解决方案更好。定量评估基于不同的误差测量，与 GS 相比，平均改善幅度为 20%的减少对齐度，高达 106%的固定螺钉位置。值得注意的是，我们的算法能够生成可行的临床解决方案，而这是当前最先进的方法无法获得的。
