Department of Oral Health Sciences, Faculty of Medicine, KU Leuven, 3000 Leuven, Belgium.
OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, 3000 Leuven, Belgium.
Eur J Orthod. 2024 Aug 1;46(4). doi: 10.1093/ejo/cjae029.
OBJECTIVES: This systematic review and meta-analysis aimed to investigate the accuracy and efficiency of artificial intelligence (AI)-driven automated landmark detection for cephalometric analysis on two-dimensional (2D) lateral cephalograms and three-dimensional (3D) cone-beam computed tomographic (CBCT) images. SEARCH METHODS: An electronic search was conducted in the following databases: PubMed, Web of Science, Embase, and grey literature with search timeline extending up to January 2024. SELECTION CRITERIA: Studies that employed AI for 2D or 3D cephalometric landmark detection were included. DATA COLLECTION AND ANALYSIS: The selection of studies, data extraction, and quality assessment of the included studies were performed independently by two reviewers. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A meta-analysis was conducted to evaluate the accuracy of the 2D landmarks identification based on both mean radial error and standard error. RESULTS: Following the removal of duplicates, title and abstract screening, and full-text reading, 34 publications were selected. Amongst these, 27 studies evaluated the accuracy of AI-driven automated landmarking on 2D lateral cephalograms, while 7 studies involved 3D-CBCT images. A meta-analysis, based on the success detection rate of landmark placement on 2D images, revealed that the error was below the clinically acceptable threshold of 2 mm (1.39 mm; 95% confidence interval: 0.85-1.92 mm). For 3D images, meta-analysis could not be conducted due to significant heterogeneity amongst the study designs. However, qualitative synthesis indicated that the mean error of landmark detection on 3D images ranged from 1.0 to 5.8 mm. Both automated 2D and 3D landmarking proved to be time-efficient, taking less than 1 min. Most studies exhibited a high risk of bias in data selection (n = 27) and reference standard (n = 29). CONCLUSION: The performance of AI-driven cephalometric landmark detection on both 2D cephalograms and 3D-CBCT images showed potential in terms of accuracy and time efficiency. However, the generalizability and robustness of these AI systems could benefit from further improvement. REGISTRATION: PROSPERO: CRD42022328800.
目的：本系统评价和荟萃分析旨在研究人工智能（AI）驱动的自动标志检测在二维（2D）侧位头颅侧位片和三维（3D）锥形束 CT（CBCT）图像上进行头影测量分析的准确性和效率。
检索方法：在以下数据库中进行电子检索：PubMed、Web of Science、Embase 和灰色文献，检索时间截至 2024 年 1 月。
选择标准：纳入使用 AI 进行 2D 或 3D 头影测量标志检测的研究。
数据收集和分析：由两名评审员独立进行研究选择、数据提取和纳入研究的质量评估。使用诊断准确性研究质量评估工具 2 版评估偏倚风险。进行荟萃分析以评估基于平均径向误差和标准误差的 2D 标志识别准确性。
结果：在去除重复项、标题和摘要筛选以及全文阅读后，选择了 34 篇出版物。其中，27 项研究评估了 AI 驱动的自动标志定位在 2D 侧位头颅侧位片上的准确性，而 7 项研究涉及 3D-CBCT 图像。基于 2D 图像上标志放置的成功检测率的荟萃分析显示，误差低于临床可接受的 2mm 阈值（1.39mm；95%置信区间：0.85-1.92mm）。对于 3D 图像，由于研究设计之间存在显著异质性，因此无法进行荟萃分析。然而，定性综合表明，3D 图像上标志检测的平均误差范围为 1.0 至 5.8mm。自动 2D 和 3D 标志定位均证明在时间效率方面具有优势，用时不到 1 分钟。大多数研究在数据选择（n=27）和参考标准（n=29）方面存在高偏倚风险。
结论：在二维头颅侧位片和三维 CBCT 图像上，人工智能驱动的头影测量标志检测在准确性和时间效率方面表现出潜力。然而，这些 AI 系统的可推广性和稳健性可以通过进一步改进来提高。
注册：PROSPERO：CRD42022328800。
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