Department of Orthopaedic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
BEIJING HURWA-ROBOT Medical Technology Co. Ltd, Beijing, China.
Int J Med Robot. 2021 Oct;17(5):e2300. doi: 10.1002/rcs.2300. Epub 2021 Jun 14.
Robotic-assisted total knee arthroplasty (TKA) was performed to promote the accuracy of bone resection and mechanical alignment. Among these TKA system procedures, 3D reconstruction of CT data of lower limbs consumes significant manpower. Artificial intelligence (AI) algorithms applying deep learning has been proved efficient in automated identification and visual processing.
CT data of a total of 200 lower limbs scanning were used for AI-based 3D model construction and CT data of 20 lower limbs scanning were utilised for verification.
We showed that the performance of an AI-guided 3D reconstruction of CT data of lower limbs for robotic-assisted TKA was similar to that of the operator-based approach. The time of 3D lower limb model construction using AI was 4.7 min. AI-based 3D models can be used for surgical planning.
AI was used for the first time to guide the 3D reconstruction of CT data of lower limbs for facilitating robotic-assisted TKA. Incorporation of AI in 3D model reconstruction before TKA might reduce the workload of radiologists.
机器人辅助全膝关节置换术（TKA）的应用旨在提高截骨的准确性和机械对线。在这些 TKA 系统程序中，下肢 CT 数据的 3D 重建需要耗费大量人力。应用深度学习的人工智能（AI）算法已被证明可有效实现自动识别和可视化处理。
共使用 200 例下肢 CT 数据进行基于 AI 的 3D 模型构建，另使用 20 例下肢 CT 数据进行验证。
我们发现，人工智能引导的机器人辅助 TKA 下肢 CT 数据 3D 重建的性能与基于操作人员的方法相似。使用 AI 构建 3D 下肢模型的时间为 4.7 分钟。基于 AI 的 3D 模型可用于手术规划。
本研究首次将人工智能用于指导机器人辅助 TKA 的下肢 CT 数据 3D 重建。在 TKA 之前将人工智能纳入 3D 模型重建中可能会减少放射科医生的工作量。
