Department of Computer Science, ETH Zurich, 8092, Zurich, Switzerland.
DisneyResearch|Studios, 8006, Zurich, Switzerland.
Int J Comput Assist Radiol Surg. 2023 Jun;18(6):1119-1125. doi: 10.1007/s11548-023-02858-6. Epub 2023 Apr 2.
Presurgical orthopedic plates are widely used for the treatment of cleft lip and palate, which is the most common craniofacial birth defect. For the traditional plate fabrication, an impression is taken under airway-endangering conditions, which recent digital alternatives overcome via intraoral scanners. However, these alternatives demand proficiency in 3D modeling software in addition to the generally required clinical knowledge of plate design.
We address these limitations with a data-driven and fully automated digital pipeline, endowed with a graphical user interface. The pipeline adopts a deep learning model to landmark raw intraoral scans of arbitrary mesh topology and orientation, which guides the nonrigid surface registration subsequently employed to segment the scans. The plates that are individually fit to these segmented scans are 3D-printable and offer optional customization.
With the distance to the alveolar ridges closely centered around the targeted 0.1 mm, our pipeline computes tightly fitting plates in less than 3 min. The plates were approved in 12 out of 12 cases by two cleft care professionals in a printed-model-based evaluation. Moreover, since the pipeline was implemented in clinical routine in two hospitals, 19 patients have been undergoing treatment utilizing our automated designs.
The results demonstrate that our automated pipeline meets the high precision requirements of the medical setting employed in cleft lip and palate care while substantially reducing the design time and required clinical expertise, which could facilitate access to this presurgical treatment, especially in low-income countries.
术前矫形板广泛应用于治疗唇腭裂，这是最常见的颅面先天缺陷。对于传统的板制造，需要在危及气道的条件下进行印模，而最近的数字替代方案通过口腔内扫描仪来克服这一问题。然而，这些替代方案除了通常需要的板设计临床知识外，还需要熟练掌握 3D 建模软件。
我们通过一个具有图形用户界面的数据驱动和完全自动化的数字管道来解决这些限制。该管道采用深度学习模型来标记任意网格拓扑和方向的原始口腔内扫描，该模型指导随后使用的非刚性表面注册来分割扫描。单独适合这些分割扫描的板是可 3D 打印的，并提供可选的定制。
我们的管道计算出的紧密贴合的板，其距离牙槽嵴的距离接近目标值 0.1 毫米，不到 3 分钟即可完成。在基于打印模型的评估中，有两位腭裂护理专业人员批准了 12 个案例中的 12 个板。此外，由于该管道已在两家医院的临床常规中实施，已有 19 名患者正在接受我们的自动化设计治疗。
结果表明，我们的自动化管道满足了在唇腭裂护理中使用的医疗环境的高精度要求，同时大大减少了设计时间和所需的临床专业知识，这可能有助于获得这种术前治疗，特别是在低收入国家。
