Department of Orthodontics, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing, People's Republic of China.
Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing, People's Republic of China.
BMC Oral Health. 2023 Jan 11;23(1):16. doi: 10.1186/s12903-023-02706-8.
Machine learning based auto-segmentation of 3D images has been developed rapidly in recent years. However, the application of this new method in the research of patients with unilateral cleft lip and palate (UCLP) is very limited. In this study, a machine learning algorithm utilizing 3D U-net was used to automatically segment the maxilla, fill the cleft and evaluate the alveolar bone graft in UCLP patients. Cleft related factors and the surgery impact on the development of maxilla were analyzed.
Preoperative and postoperative computed tomography images of 32 patients (64 images) were obtained. The deep-learning-based protocol was used to segment the maxilla and defect, followed by manual refinement. Paired t-tests and Mann-Whitney tests were performed to reveal the changes of the maxilla after surgery. Two-factor, two-level analysis for repeated measurement was used to examine the different trends of growth on the cleft and non-cleft sides of the maxilla. Pearson and Spearman correlations were used to explore the relationship between the defect and the changes of the maxillary cleft side.
One-year after the alveolar bone grafting surgery, different growth amount was found on the cleft and non-cleft sides of maxilla. The maxillary length (from 34.64 ± 2.48 to 35.67 ± 2.45 mm) and the alveolar length (from 36.58 ± 3.21 to 37.63 ± 2.94 mm) increased significantly only on the cleft side while the maxillary anterior width (from 11.61 ± 1.61 to 12.01 ± 1.41 mm) and posterior width (from 29.63 ± 2.25 to 30.74 ± 2.63 mm) increased significantly only on the non-cleft side after surgery. Morphology of the cleft was found to be related to the pre-surgical maxillary dimension on the cleft side, while its correlation with the change of the maxilla after surgery was low or not statistically significant.
The auto-segmentation of the maxilla and the cleft could be performed very efficiently and accurately with the machine learning method. Asymmetric growth was found on the cleft and non-cleft sides of the maxilla after alveolar bone graft in UCLP patients. The morphology of the cleft mainly contributed to the pre-operation variance of the maxilla but had little impact on the maxilla growth after surgery.
近年来，基于机器学习的 3D 图像自动分割技术发展迅速。然而，这种新方法在单侧唇裂腭裂（UCLP）患者研究中的应用非常有限。本研究利用基于深度学习的 3D U-net 算法自动分割上颌骨、填充裂隙并评估 UCLP 患者牙槽骨植骨的情况。分析了裂隙相关因素和手术对上颌骨发育的影响。
获取 32 例患者（64 张图像）的术前和术后 CT 图像。采用深度学习协议对上颌骨和裂隙进行分割，然后进行手动细化。采用配对 t 检验和 Mann-Whitney 检验比较术后上颌骨的变化。采用两因素两水平重复测量方差分析比较上颌骨裂隙侧和非裂隙侧的不同生长趋势。采用 Pearson 和 Spearman 相关性分析探讨裂隙与上颌骨裂隙侧变化的关系。
牙槽骨植骨术后 1 年，发现上颌骨裂隙侧和非裂隙侧的生长量不同。仅在裂隙侧，上颌骨长度（从 34.64±2.48 毫米增加到 35.67±2.45 毫米）和牙槽骨长度（从 36.58±3.21 毫米增加到 37.63±2.94 毫米）显著增加，而仅在上颌骨非裂隙侧，上颌骨前宽（从 11.61±1.61 毫米增加到 12.01±1.41 毫米）和后宽（从 29.63±2.25 毫米增加到 30.74±2.63 毫米）显著增加。术后发现裂隙形态与裂隙侧上颌骨术前尺寸有关，但其与上颌骨术后变化的相关性较低或无统计学意义。
利用机器学习方法可以非常高效、准确地对上颌骨和裂隙进行自动分割。在 UCLP 患者牙槽骨植骨后，上颌骨裂隙侧和非裂隙侧出现不对称生长。裂隙形态主要对上颌骨术前变异起作用，但对上颌骨术后生长影响较小。
