Jia Shanhang, Weng Yuanzhi, Wang Kai, Qi Huan, Yang Yuhua, Ma Chi, Lu Weijia William, Wu Hao
Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China.
Spine Center, China International Neuroscience Institute (CHINA-INI), Beijing, China.
Front Surg. 2023 Sep 11;10:1247527. doi: 10.3389/fsurg.2023.1247527. eCollection 2023.
Recent neurosurgical applications based on artificial intelligence (AI) have demonstrated its potential in surgical planning and anatomical measurement. We aimed to evaluate the performance of an AI planning software application on screw length/diameter selection and insertion accuracy in comparison with freehand surgery.
A total of 45 patients with 208 pedicle screw placements on thoracolumbar segments were included in this analysis. The novel AI planning software was developed based on a deep learning model. AI-based pedicle screw placements were selected on the basis of preoperative computed tomography (CT) data, and freehand surgery screw placements were observed based on postoperative CT data. The performance of AI pedicle screw placements was evaluated on the components of screw length, diameter, and Gertzbein grade in comparison with the results achieved by freehand surgery.
Among 208 pedicle screw placements, the average screw length/diameters selected by the AI model and used in freehand surgery were 48.65 ± 5.99 mm/7.39 ± 0.42 mm and 44.78 ± 2.99 mm/6.1 ± 0.27 mm, respectively. Among AI screw placements, 85.1% were classified as Gertzbein Grade A (no cortical pedicle breach); among free-hand surgery placements, 64.9% were classified as Gertzbein Grade A.
The novel AI planning software application could provide an accessible and safe pedicle screw placement strategy in comparison with traditional freehand pedicle screw placement strategies. The choices of pedicle screw dimensional parameters made by the model, including length and diameter, may provide potential inspiration for real clinical discretion.
基于人工智能（AI）的近期神经外科应用已证明其在手术规划和解剖测量方面的潜力。我们旨在评估一款人工智能规划软件应用在螺钉长度/直径选择及置入准确性方面的性能，并与徒手手术进行比较。
本分析纳入了45例患者，其胸腰段共置入208枚椎弓根螺钉。这款新型人工智能规划软件是基于深度学习模型开发的。基于术前计算机断层扫描（CT）数据选择基于人工智能的椎弓根螺钉置入，基于术后CT数据观察徒手手术的螺钉置入。将人工智能椎弓根螺钉置入的性能在螺钉长度、直径和格茨贝恩分级等方面与徒手手术的结果进行比较评估。
在208枚椎弓根螺钉置入中，人工智能模型选择并用于徒手手术的螺钉平均长度/直径分别为48.65±5.99 mm/7.39±0.42 mm和44.78±2.99 mm/6.1±0.27 mm。在人工智能螺钉置入中，85.1%被归类为格茨贝恩A级（无椎弓根皮质破裂）；在徒手手术置入中，64.9%被归类为格茨贝恩A级。
与传统的徒手椎弓根螺钉置入策略相比，这款新型人工智能规划软件应用可提供一种可行且安全的椎弓根螺钉置入策略。该模型做出的椎弓根螺钉尺寸参数选择，包括长度和直径，可能为实际临床决策提供潜在的启发。
