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Abstract：
Background: Cephalometric analysis (CA) is an indispensable diagnostic tool in orthodontics for treatment planning and outcome assessment. Manual CA is time-consuming and prone to variability. Methods: This study aims to compare the accuracy and repeatability of CA results among three commercial AI-driven programs: CephX, WebCeph, and AudaxCeph. This study involved a retrospective analysis of lateral cephalograms from a single orthodontic center. Automated CA was performed using the AI programs, focusing on common parameters defined by Downs, Ricketts, and Steiner. Repeatability was tested through 50 randomly reanalyzed cases by each software. Statistical analyses included intraclass correlation coefficients (ICC3) for agreement and the Friedman test for concordance. Results: One hundred twenty-four cephalograms were analyzed. High agreement between the AI systems was noted for most parameters (ICC3 > 0.9). Notable differences were found in the measurements of angle convexity and the occlusal plane, where discrepancies suggested different methodologies among the programs. Some analyses presented high variability in the results, indicating errors. Repeatability analysis revealed perfect agreement within each program. Conclusions: AI-driven cephalometric analysis tools demonstrate a high potential for reliable and efficient orthodontic assessments, with substantial agreement in repeated analyses. Despite this, the observed discrepancies and high variability in part of analyses underscore the need for standardization across AI platforms and the critical evaluation of automated results by clinicians, particularly in parameters with significant treatment implications.
摘要：
背景：头影测量分析（CA）是正畸治疗计划和结果评估中不可或缺的诊断工具。手动CA耗时且易于变化。方法：本研究旨在比较三个商业AI驱动程序中CA结果的准确性和可重复性：CephX，WebCeph,还有AudaxCeph.这项研究涉及对单个正畸中心的侧位头颅图的回顾性分析。自动化CA是使用AI程序执行的，专注于由Downs定义的公共参数，Ricketts,还有Steiner.每个软件通过50个随机重新分析的案例来测试可重复性。统计分析包括用于一致性的组内相关系数(ICC3)和用于一致性的弗里德曼检验。结果：分析了124张头颅图。对于大多数参数(ICC3>0.9)，人工智能系统之间的高度一致。在角度凸度和咬合平面的测量中发现了明显的差异，其中差异表明程序之间存在不同的方法。一些分析在结果中表现出很高的变异性，指示错误。可重复性分析表明，每个程序都有完美的一致性。结论：AI驱动的头颅测量分析工具显示出可靠和有效的正畸评估的高潜力，在重复分析中具有实质性的一致性。尽管如此,在部分分析中观察到的差异和高度变异性强调了跨AI平台标准化的必要性，以及临床医生对自动化结果的关键评估。特别是在具有重大治疗意义的参数中。
