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Zygomatic Osteotomy surgery design software based on skull CT scans - Self-supervised algo reduces workload
Xiaohui Qiu1, Chi Zhong2, Qiuyang Chen3
1National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China; Department of Plastic and Reconstructive Surgery, Xiangya III Hospital of Central South University, China.
Background:
The morphology of the zygomatic complex significantly influences facial appearance, leading to a focus on zygomatic osteotomy. The current technique, the "L-shaped" zygomatic osteotomy, requires a small incision and preoperative osteotomy design for an osteotomy guide. However, the use of multiple software programs in the design process makes it time-consuming and clinically challenging.
Method:
Artificial intelligence technology offers a solution by integrating digital medical technology into medicine. AI algorithms were developed based on point cloud models, using 2000 cases of three-dimensional CT data for training. Eighty CT data sets were randomly chosen for both AI and manual skull segmentation designs. The effectiveness, symmetry, safety, and aesthetic outcomes were compared.
Result:
The AI zygomatic osteotomy showed superior performance in symmetry and aesthetics compared to manual zygomatic osteotomy. The complex structure of the zygomatic arch highlights the advantages of AI-driven osteotomy design, especially in intricate cases. Additionally, the AI osteotomy scheme demonstrated no compromise in safety indicators compared to the manual approach.
Conclusion:
AI zygomatic osteotomy proves to be a safe and effective alternative to manual zygomatic osteotomy, showcasing enhanced symmetry and aesthetic outcomes. The efficiency and precision of AI-driven design in complex zygomatic osteotomies make it a promising advancement in this field.
