Kazimierczak Private Dental Practice, 85-009 Bydgoszcz, Poland.
Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, 85-067 Bydgoszcz, Poland.
Dentomaxillofac Radiol. 2024 Jan 11;53(1):52-59. doi: 10.1093/dmfr/twad006.
To compare artificial intelligence (AI)-driven web-based platform and manual measurements for analysing facial asymmetry in craniofacial CT examinations.
The study included 95 craniofacial CT scans from patients aged 18-30 years. The degree of asymmetry was measured based on AI platform-predefined anatomical landmarks: sella (S), condylion (Co), anterior nasal spine (ANS), and menton (Me). The concordance between the results of automatic asymmetry reports and manual linear 3D measurements was calculated. The asymmetry rate (AR) indicator was determined for both automatic and manual measurements, and the concordance between them was calculated. The repeatability of manual measurements in 20 randomly selected subjects was assessed. The concordance of measurements of quantitative variables was assessed with interclass correlation coefficient (ICC) according to the Shrout and Fleiss classification.
Erroneous AI tracings were found in 16.8% of cases, reducing the analysed cases to 79. The agreement between automatic and manual asymmetry measurements was very low (ICC < 0.3). A lack of agreement between AI and manual AR analysis (ICC type 3 = 0) was found. The repeatability of manual measurements and AR calculations showed excellent correlation (ICC type 2 > 0.947).
The results indicate that the rate of tracing errors and lack of agreement with manual AR analysis make it impossible to use the tested AI platform to assess the degree of facial asymmetry.
比较基于人工智能（AI）的网络平台与手动测量在颅面 CT 检查中分析面部不对称的差异。
本研究纳入了 95 例年龄在 18-30 岁的颅面 CT 扫描患者。采用 AI 平台预定义的解剖学标志（鞍结节 S、髁突 Co、前鼻棘 ANS 和颏部 Me）测量不对称程度。自动不对称报告的结果与手动线性 3D 测量的一致性通过计算来评估。为自动和手动测量确定了不对称率（AR）指标，并计算了它们之间的一致性。在 20 名随机选择的受试者中评估了手动测量的重复性。根据 Shrout 和 Fleiss 分类，采用组内相关系数（ICC）评估定量变量测量的一致性。
16.8%的病例中存在错误的 AI 描记，分析病例减少至 79 例。自动和手动不对称测量之间的一致性非常低（ICC < 0.3）。发现 AI 和手动 AR 分析之间缺乏一致性（ICC 类型 3 = 0）。手动测量和 AR 计算的重复性显示出极好的相关性（ICC 类型 2 > 0.947）。
结果表明，追踪错误的发生率以及与手动 AR 分析的不一致性使得该测试的 AI 平台无法用于评估面部不对称程度。
