Xu Xiong, Jia-Ming Liu, William Weijia Lu, Ke-Di Yang, Huan Qi, Zhi-Li Liu, Ning Zhang, Shan-Hu Huang
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The AI software utilizes lumbar spine CT data to perform screw trajectory planning and simulate AO screw trajectories for bilateral L3-5 vertebral bodies. Both screw trajectories were subdivided into unicortical and bicortical modes. The AI software automatically calculating the POF and pullout risk of every screw trajectory. The POF and risk of screw pullout for AI-planned screw trajectories and AO standard trajectories were compared and analyzed.</p><p><strong>Results: </strong>Forty-three patients were included. For the screw sizes, AI-planned screws were greater in diameter and length than those of AO screws (P<0.05). In groups B-E, the AI unicortical trajectories had a POF of over 200N higher than that of AO unicortical trajectories. POF was higher in all groups for the AI bicortical screw trajectories compared with the AO bicortical screw trajectories (P<0.05). AI unicortical trajectories in groups B-E had a lower risk of screw pullout compared with that of AO unicortical trajectories (P<0.05).</p><p><strong>Conclusions: </strong>AI unicortical screw trajectory planning for lumbar surgery in patients with BMD of 40-120 mg/cm3 can significantly improve screw POF and reduce the risk of screw pullout.</p>","PeriodicalId":10457,"journal":{"name":"Clinical Spine Surgery","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Effectiveness of Artificial Intelligence-based Pedicle Screw Trajectory Planning in Patients With Different Levels of Bone Mineral Density.\",\"authors\":\"Xu Xiong, Jia-Ming Liu, William Weijia Lu, Ke-Di Yang, Huan Qi, Zhi-Li Liu, Ning Zhang, Shan-Hu Huang\",\"doi\":\"10.1097/BSD.0000000000001687\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Study design: </strong>Retrospective cohort study.</p><p><strong>Objective: </strong>To evaluate the effectiveness of pedicle screw trajectory planning based on artificial intelligence (AI) software in patients with different levels of bone mineral density (BMD).</p><p><strong>Summary of background data: </strong>AI-based pedicle screw trajectory planning has potential to improve pullout force (POF) of screws. However, there is currently no literature investigating the efficacy of AI-based pedicle screw trajectory planning in patients with different levels of BMD.</p><p><strong>Methods: </strong>The patients were divided into 5 groups (group A-E) according to their BMD. The AI software utilizes lumbar spine CT data to perform screw trajectory planning and simulate AO screw trajectories for bilateral L3-5 vertebral bodies. Both screw trajectories were subdivided into unicortical and bicortical modes. The AI software automatically calculating the POF and pullout risk of every screw trajectory. The POF and risk of screw pullout for AI-planned screw trajectories and AO standard trajectories were compared and analyzed.</p><p><strong>Results: </strong>Forty-three patients were included. For the screw sizes, AI-planned screws were greater in diameter and length than those of AO screws (P<0.05). 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AI unicortical trajectories in groups B-E had a lower risk of screw pullout compared with that of AO unicortical trajectories (P<0.05).</p><p><strong>Conclusions: </strong>AI unicortical screw trajectory planning for lumbar surgery in patients with BMD of 40-120 mg/cm3 can significantly improve screw POF and reduce the risk of screw pullout.</p>\",\"PeriodicalId\":10457,\"journal\":{\"name\":\"Clinical Spine Surgery\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2024-09-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical Spine Surgery\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1097/BSD.0000000000001687\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical Spine Surgery","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/BSD.0000000000001687","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
引用次数: 0
摘要
研究设计回顾性队列研究：评估基于人工智能（AI）软件的椎弓根螺钉轨迹规划对不同骨矿密度（BMD）水平患者的有效性：基于人工智能的椎弓根螺钉轨迹规划具有改善螺钉拔出力（POF）的潜力。然而，目前还没有文献研究基于人工智能的椎弓根螺钉轨迹规划对不同骨密度水平患者的疗效：根据患者的 BMD 分成 5 组（A-E 组）。人工智能软件利用腰椎 CT 数据进行螺钉轨迹规划，模拟双侧 L3-5 椎体的 AO 螺钉轨迹。两种螺钉轨迹又分为单皮质模式和双皮质模式。人工智能软件自动计算每个螺钉轨迹的POF和拔出风险。比较并分析了 AI 规划的螺钉轨迹和 AO 标准轨迹的 POF 和螺钉拔出风险：结果：共纳入 43 例患者。就螺钉尺寸而言，AI 计划螺钉的直径和长度均大于 AO 螺钉（PC 结论：AI 单皮质螺钉轨迹的直径和长度均大于 AO 螺钉）：对 BMD 为 40-120 mg/cm3 的患者进行腰椎手术的 AI 单皮质螺钉轨迹规划可显著改善螺钉 POF 并降低螺钉脱出的风险。
The Effectiveness of Artificial Intelligence-based Pedicle Screw Trajectory Planning in Patients With Different Levels of Bone Mineral Density.
Study design: Retrospective cohort study.
Objective: To evaluate the effectiveness of pedicle screw trajectory planning based on artificial intelligence (AI) software in patients with different levels of bone mineral density (BMD).
Summary of background data: AI-based pedicle screw trajectory planning has potential to improve pullout force (POF) of screws. However, there is currently no literature investigating the efficacy of AI-based pedicle screw trajectory planning in patients with different levels of BMD.
Methods: The patients were divided into 5 groups (group A-E) according to their BMD. The AI software utilizes lumbar spine CT data to perform screw trajectory planning and simulate AO screw trajectories for bilateral L3-5 vertebral bodies. Both screw trajectories were subdivided into unicortical and bicortical modes. The AI software automatically calculating the POF and pullout risk of every screw trajectory. The POF and risk of screw pullout for AI-planned screw trajectories and AO standard trajectories were compared and analyzed.
Results: Forty-three patients were included. For the screw sizes, AI-planned screws were greater in diameter and length than those of AO screws (P<0.05). In groups B-E, the AI unicortical trajectories had a POF of over 200N higher than that of AO unicortical trajectories. POF was higher in all groups for the AI bicortical screw trajectories compared with the AO bicortical screw trajectories (P<0.05). AI unicortical trajectories in groups B-E had a lower risk of screw pullout compared with that of AO unicortical trajectories (P<0.05).
Conclusions: AI unicortical screw trajectory planning for lumbar surgery in patients with BMD of 40-120 mg/cm3 can significantly improve screw POF and reduce the risk of screw pullout.
期刊介绍：
Clinical Spine Surgery is the ideal journal for the busy practicing spine surgeon or trainee, as it is the only journal necessary to keep up to date with new clinical research and surgical techniques. Readers get to watch leaders in the field debate controversial topics in a new controversies section, and gain access to evidence-based reviews of important pathologies in the systematic reviews section. The journal features a surgical technique complete with a video, and a tips and tricks section that allows surgeons to review the important steps prior to a complex procedure.
Clinical Spine Surgery provides readers with primary research studies, specifically level 1, 2 and 3 studies, ensuring that articles that may actually change a surgeon’s practice will be read and published. Each issue includes a brief article that will help a surgeon better understand the business of healthcare, as well as an article that will help a surgeon understand how to interpret increasingly complex research methodology. Clinical Spine Surgery is your single source for up-to-date, evidence-based recommendations for spine care.
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