An Automated Framework for Mandibular Reconstruction: Evaluation and Clinical Application.
Authors
Affiliations (3)
Affiliations (3)
- Department of Oral Surgery, Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, and Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai, China.
- Institute of Biomedical Manufacturing and Life Quality Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China.
- Department of Oral Maxillofacial & Head and Neck Oncology, Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, School of Medicine, Shanghai, China.
Abstract
Mandibular reconstruction suffers from limitations in automation and objectivity. This study aimed to develop an automated framework to address these challenges. A novel approach combining statistical shape modeling and 3D deep learning was developed. Trained on 200 CT scans and validated in 80 clinical cases, its performance was compared to expert manual planning by evaluating reconstruction accuracy, bone contact area, and planning time. Finally, design experiments were performed in five clinical cases. The automated method demonstrated high accuracy (DSC: 0.874) and real-time efficiency. It significantly outperformed manual planning in bone contact area (106.2 vs. 94.6 mm<sup>2</sup>) and reduced planning time by 85%. Postoperative results showed excellent implant alignment (2.1 mm deviation) and high patient satisfaction. The proposed automated framework successfully enhances the efficiency and quality of mandibular reconstruction, meeting clinical needs. Future work will integrate more complex biomechanical considerations.