Skeletal facial asymmetry: reliability of manual and artificial intelligence-driven analysis.

IF 2.9 2区医学 Q1 DENTISTRY, ORAL SURGERY & MEDICINE Dento maxillo facial radiology Pub Date : 2024-01-11 DOI:10.1093/dmfr/twad006

Natalia Kazimierczak, Wojciech Kazimierczak, Zbigniew Serafin, Paweł Nowicki, Tomasz Jankowski, Agnieszka Jankowska, Joanna Janiszewska-Olszowska

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Abstract

Objectives: To compare artificial intelligence (AI)-driven web-based platform and manual measurements for analysing facial asymmetry in craniofacial CT examinations.

Methods: 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.

Results: 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).

Conclusions: 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.

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骨骼面部不对称：人工和人工智能分析的可靠性。

目的比较人工智能（AI）驱动的网络平台和人工测量方法，以分析颅面部 CT 检查中的面部不对称情况：研究包括 95 例 18-30 岁患者的颅面部 CT 扫描。不对称程度根据 AI 平台预先定义的解剖地标进行测量：蝶鞍 (S)、髁突 (Co)、前鼻骨棘 (ANS) 和耳廓 (Me)。计算了自动不对称报告结果与手动线性三维测量结果之间的一致性。确定了自动和手动测量的不对称率（AR）指标，并计算了两者之间的一致性。对随机抽取的 20 名受试者的手动测量重复性进行了评估。根据 Shrout 和 Fleiss 分类法，使用类间相关系数（ICC）评估定量变量测量的一致性：自动和人工不对称测量的一致性非常低（ICC < 0.3）。人工智能和手动 AR 分析之间缺乏一致性（ICC 类型 3 = 0）。人工测量和 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，由于追踪错误率和与人工 AR 分析缺乏一致性，因此无法使用测试的人工智能平台来评估面部不对称程度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Dento maxillo facial radiology 医学-核医学

CiteScore

5.60

自引率

9.10%

发文量

审稿时长

4-8 weeks

期刊介绍： Dentomaxillofacial Radiology (DMFR) is the journal of the International Association of Dentomaxillofacial Radiology (IADMFR) and covers the closely related fields of oral radiology and head and neck imaging. Established in 1972, DMFR is a key resource keeping dentists, radiologists and clinicians and scientists with an interest in Head and Neck imaging abreast of important research and developments in oral and maxillofacial radiology. The DMFR editorial board features a panel of international experts including Editor-in-Chief Professor Ralf Schulze. Our editorial board provide their expertise and guidance in shaping the content and direction of the journal. Quick Facts: - 2015 Impact Factor - 1.919 - Receipt to first decision - average of 3 weeks - Acceptance to online publication - average of 3 weeks - Open access option - ISSN: 0250-832X - eISSN: 1476-542X