Objective: To explore the clinical application pathway of the CT generative adversarial networks (CTGANs) algorithm in mandibular reconstruction surgery, aiming to provide a valuable reference for this procedure. Methods: A clinical exploratory study was conducted, 27 patients who visited the Department of Oral and Maxillofacial Surgery, Xiangya Hospital of Central South University between January 2022 and January 2024 and required mandibular reconstruction were selected. The cohort included 16 males and 11 females, with the age of (46.6±11.5) years; among them, 7 cases involved mandibular defects crossing the midline. The CTGANs generator produced 100 images, and the mean squared error (MSE) was calculated for differences between any two generated images. Preoperative cone-beam CT data from 5 patients were used to construct a labeled test database, divided into groups: normal maxilla, normal mandible, diseased mandible, and noise (each group containing 70 cross-sectional images). The CTGANs discriminator was used to evaluate the loss values for each group, and one-way ANOVA and intergroup comparisons were performed. Using the self-developed KuYe multioutcome-option-network generation system (KMG) software, the three-dimensional (3D) completion area of the mandible under cone-beam CT was defined for the 27 patients. The CTGANs algorithm was applied to obtain a reference model for the mandible. Virtual surgery was then performed, utilizing the fibular segment to reconstruct the mandible and design the surgical expectation model. The second-generation combined bone-cutting and prebent reconstruction plate positioning method was used to design and 3D print surgical guides, which were subsequently applied in mandibular reconstruction surgery for the 27 patients. Postoperative cone-beam CT was used to compare the morphology of the reconstructed mandible with the surgical expectation model and the mandibular reference model to assess the three-dimensional deviation. Results: The MSE for the CTGANs generator was 2 411.9±833.6 (95%CI: 2 388.7-2 435.1). No significant difference in loss values was found between the normal mandible and diseased mandible groups (P>0.05), while both groups demonstrated significantly lower loss values than the maxilla and noise groups (P<0.001). All 27 patients successfully obtained mandibular reference models and surgical expectation models. In total, 14 162 negative deviation points and 15 346 positive deviation points were observed when comparing the reconstructed mandible morphology with the surgical expectation model, with mean deviations of -1.32 mm (95%CI:-1.33--1.31 mm) and 1.90 mm (95%CI: 1.04-1.06 mm), respectively. Conclusions: The CTGANs algorithm is capable of generating diverse mandibular reference models that reflect the natural anatomical characteristics of the mandible and closely match individual patient morphology, thereby facilitating the design of surgical expectation models. This method shows promise for application in patients with mandibular defects crossing the midline.
目的： 探讨CT生成对抗神经网络（CTGANs）算法在下颌骨重建手术中的临床应用路径，以期为下颌骨重建提供参考。 方法： 采用临床探索性研究，选择2022年1月至2024年1月就诊于中南大学湘雅医院口腔医学中心、需进行下颌骨重建的患者27例，其中男性16例，女性11例，年龄（46.6±11.5）岁，其中下颌骨缺损跨中线者7例。使用CTGANs生成器生成100张下颌骨图像，通过均方误差（MSE）计算任意两幅生成图的差别。选择5例患者术前锥形束CT数据，构建有标签的测试库，分为正常上颌骨、正常下颌骨、病变下颌骨、噪声组（每组70张断层图像），使用CTGANs鉴别器评价各组损失值，并进行单因素方差分析和组间比较。使用自主研发的KuYe多输出智选生成系统（KMG）软件，对27例患者锥形束CT下颌骨定义三维补全区域，应用CTGANs算法，获得下颌骨参考模型；通过软件进行腓骨段重建下颌骨的虚拟手术，设计手术预期模型。用第二代联合骨切除和预弯重建板位置方法，设计手术导板并三维打印手术导板，借助手术导板对27例患者进行下颌骨重建手术，比较术后锥形束CT重建的下颌骨形态与手术预期模型、下颌骨参考模型的三维偏差。 结果： CTGANs生成器MSE值为2 411.9±833.6（95%CI：2 388.7~2 435.1）。正常下颌骨和病变下颌骨组损失值差异无统计学意义（P>0.05），两者损失值的绝对值均显著小于上颌骨和噪声组（P<0.001）。27例患者均成功获得下颌骨参考模型和手术预期模型；术后锥形束CT重建的下颌骨形态与手术预期模型相比，共观察到14 162个负偏差点和15 346个正偏差点，均值分别为-1.32 mm（95%CI：-1.33~-1.31 mm）和1.90 mm（95%CI：1.04~1.06 mm）。 结论： CTGANs能生成多样的、符合下颌骨自然特征且匹配患者自身形态的下颌骨参考模型，进而可用于设计手术预期模型。这一方法可应用于下颌骨缺损跨越中线的患者。.