Artificial intelligence web-based cephalometric analysis platform: comparison with the computer assisted cephalometric method

IF 0.3 Q4 DENTISTRY, ORAL SURGERY & MEDICINE
Hikmetnur Danisman
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Abstract

ABSTRACTPurpose The aim of this investigation was to evaluate the reliability and accuracy of cephalometric measurements of the web-based artificial intelligence cephalometric analysis platform in comparison with the computer assisted cephalometric analysis method.Materials and Methods 60 patients’ pretreatment lateral cephalograms were randomly selected. A total of 21 landmarks were identified by one operator and a total of 20 parameters were measured both AI based platform WebCeph® and Dolphin Imaging®. Measurements of AI landmarking were recorded. Then, the landmarks placed automatically by the AI (AI landmarking) were corrected manually (manual landmarking). All the measurements were recorded and performed once more after 4-weeks. Correlation between repeated measurements was evaluated by using the Pearson correlation coefficient. Paired t-test was used for comparisons between groups.Results Most of the measurements showed statistically significant differences between AI landmarking and manual landmarking 1, except for the angular measurements of the U1-SNº (P = 0.717), interinsizal angle (P = 0.410), and L1-NBº (P = 0,295). Most of the measurements were found to be statistically similar between manual landmarking 1 and manual landmarking 2, except for the angular measurement of the SN-GoGnº, IMPAº, linear measurements ANS-Me. The Pearson correlation coefficients of all cephalometric measurements were above 0.80.Conclusions All mean differences between the manual landmarking 1 and AI landmarking measurements were less than 2 degrees/2 mm, except for the nasolabial angle. Although WebCeph’s artificial intelligence algorithm is not sufficient to accurately determine the position of soft tissue landmarks, it becomes more suitable for clinical use with the control and manual correction of landmarks by observers.KEYWORDS: Artificial intelligenceautomatic landmarkingcephalometricWebCeph AcknowledgmentsWe thank Hatice Cansu Kış, PhD, from Gaziosmanpasa University (Tokat, Turkiye) for technical support and advice on statistical analyses.Disclosure statementNo potential conflict of interest was reported by the author.Authors contribution“HD made the cephalometric tracings, analysed and interpreted the cephalometric data, standardized the cephalograms and selected them according to the including criteria.Ethical approvalThe study was conducted according to the Declaration of Helsinki principles and was approved by the Scientific Research and Publication Ethics Committee at Nuh Naci Yazgan University (Approval No:1/393).
基于web的人工智能头颅测量分析平台:与计算机辅助头颅测量方法的比较
【摘要】目的评价基于网络的人工智能头测量分析平台与计算机辅助头测量分析方法测量结果的可靠性和准确性。材料与方法随机选取60例前处理侧位脑电图患者。一名操作员共识别了21个地标,并通过基于AI的平台WebCeph®和Dolphin Imaging®测量了总共20个参数。记录人工智能地标测量值。然后,对人工智能自动放置的地标(人工地标)进行人工校正(人工地标)。记录所有测量数据,4周后再次进行测量。使用Pearson相关系数评估重复测量之间的相关性。组间比较采用配对t检验。结果人工智能标尺与人工标尺除U1-SNº(P = 0.717)、内径角(P = 0.410)和L1-NBº(P = 0.295)的角度测量值外,其余测量值差异均有统计学意义。除sn - gonº、IMPAº的角度测量值、ANS-Me的线性测量值外,人工地标1与人工地标2的测量值在统计学上基本一致。所有头颅测量的Pearson相关系数均在0.80以上。结论除鼻唇角外,人工标记1与人工标记测量的平均差异均小于2度/ 2mm。虽然WebCeph的人工智能算法不足以准确确定软组织地标的位置,但通过观察者对地标的控制和人工校正,WebCeph的人工智能算法更适合临床使用。我们感谢Gaziosmanpasa大学(Tokat, Turkiye)的Hatice Cansu博士在统计分析方面的技术支持和建议。披露声明作者未报告潜在的利益冲突。作者贡献:HD制作了头颅影像,分析和解释了头颅影像资料,对头颅影像进行了标准化处理,并根据纳入标准进行了选择。伦理批准本研究根据赫尔辛基宣言原则进行,并得到Nuh Naci Yazgan大学科学研究和出版伦理委员会的批准(批准号:1/393)。
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