目的: 提出基于下颌骨数据库和全连接神经网络(fully connected neural network，FCNN) 的三维检索模型辅助下的下颌骨个性化重建方案，并验证该方案的临床可行性。方法: 建立一个300例正常中国北方汉族人下颌骨数据库，在头影测量的基础上，进一步筛选稳定性较好的下颌骨标志点，制定下颌骨标志点方案，提取下颌骨几何特征。开发三维检索算法，该算法能从上述数据库中检索出与待匹配下颌骨最相似的下颌骨。搭建FCNN训练上述算法以提高三维检索精度，使用Geomagic Control 2014软件评价基于上述下颌骨数据库和算法的三维检索模型匹配精度。从2019年12月到2021年3月，共有5例患者在北京大学口腔医院颌面外科接受了基于下颌骨数据库和FCNN的三维检索模型辅助下的下颌骨个性化重建手术。通过三维检索算法从上述下颌骨数据库中检索获得最相似下颌骨，使用最相似下颌骨恢复缺损区病变前形态和指导下颌骨重建。5例患者的下颌骨缺损均由髂骨瓣修复，使用个性化手术导板实现术前手术设计的转化和实施。结果: 通过筛选，可重复性和稳定性较高的下颌骨标志点被确定并组成下颌骨标志点方案。经过训练后，基于FCNN的三维检索模型在300例下颌骨数据库中检索获得的最相似下颌骨与待匹配下颌骨的平均偏差为(1.77±0.44) mm，均方根偏差为(2.58±0.86) mm。5例患者的下颌骨重建手术均成功，面部对称性和咬合功能得以恢复，所有患者都对术后外观恢复感到满意。三维比较显示，术后下颌骨与术前设计之间的平均偏差为(0.98±0.17) mm，偏差≤1 mm区域占比61.34%±14.13%，≤2 mm区域占比83.82%±7.35%，≤3 mm区域占比93.94%±2.87%。结论: 基于下颌骨数据库和FCNN的三维检索模型辅助下的下颌骨个性化重建具有临床可行性。

Objective: To propose a new protocol for personalized mandibular reconstruction assisted by three-dimensional (3D) retrieval model based on fully connected neural network (FCNN) and a database of mandibles, and to verify clinical feasibility of the protocol. Methods: A database of mandibles of 300 normal northern Chinese Han people was established. On the basis of cephalometry, the mandible landmarks with good stability were further screened. Mandibular landmarks were selected and geometric features of the mandible were extracted. A 3D retrieval algorithm was developed, which could retrieve the mandible most similar to a given mandible from the database. A FCNN was built to train the algorithm to improve accuracy of the 3D retrieval model. Using Geomagic Control 2014 software, matching accuracy of the 3D retrieval model was based on aforementioned mandible database and algorithm. From December 2019 to March 2021, a total of 5 patients underwent personalized mandibular reconstruction assisted by a 3D retrieval model based on mandible database and FCNN in the Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology. The most similar mandible was retrieved from mandible database through 3D retrieval algorithm. It was used to restore the premorbid morphology of defect area and guide mandibular reconstruction. For the 5 patients, mandible was reconstructed with iliac flap. Virtual surgical plan was transformed using individual surgical guides. Results: Through screening, mandibular landmarks with high reproducibility and stability were identified and composed of mandibular landmarker protocols. After training, the average deviation between most similar mandible retrieved from the 300-case mandible database through 3D retrieval model based on FCNN and given mandible was (1.77±0.44) mm. And the root-mean-square deviation between the most similar mandible retrieved from the database and given mandible was (2.58±0.86) mm. The mandibular reconstruction surgery was successful in all the 5 patients. Their facial symmetry and occlusion were restored. All the patients were satisfied with postoperative appearance. The mean deviation between postoperative mandible and preoperative design was (0.98±0.17) mm. The area with a deviation ≤1 mm accounted for 61.34%±14. 13%, ≤2 mm accounted for 83.82%±7.35%, and ≤3 mm accounted for 93.94%± 2.87%. Conclusion: The personalized mandibular reconstruction assisted by 3D retrieval model based on the 300-case mandible database and FCNN is feasible clinically.