Oral Radiology, Department of Dentistry and Oral Health, Aarhus University, Aarhus C, Denmark.
Private practice, Tandlægerne Sydcentret, Kolding, Denmark.
Eur J Dent Educ. 2024 May;28(2):490-496. doi: 10.1111/eje.12973. Epub 2023 Nov 14.
Teaching of dental caries diagnostics is an essential part of dental education. Diagnosing proximal caries is a challenging task, and automated systems applying artificial intelligence (AI) have been introduced to assist in this respect. Thus, the implementation of AI for teaching purposes may be considered. The aim of this study was to assess the impact of an AI software on students' ability to detect enamel-only proximal caries in bitewing radiographs (BWs) and to assess whether proximal tooth overlap interferes with caries detection.
The study included 74 dental students randomly allocated to either a test or control group. At two sessions, both groups assessed proximal enamel caries in BWs. At the first session, the test group registered caries in 25 BWs using AI software (AssistDent®) and the control group without using AI. One month later, both groups detected caries in another 25 BWs in a clinical setup without using the software. The student's registrations were compared with a reference standard. Positive agreement (caries) and negative agreement (no caries) were calculated, and t-tests were applied to assess whether the test and control groups performed differently. Moreover, t-tests were applied to test whether proximal overlap interfered with caries registration.
At the first and second sessions, 56 and 52 tooth surfaces, respectively, were detected with enamel-only caries according to the reference standard. At session 1, no significant difference between the control (48%) and the test (42%) group was found for positive agreement (p = .08), whereas the negative agreement was higher for the test group (86% vs. 80%; p = .02). At session 2, there was no significant difference between the groups. The test group improved for positive agreement from session 1 to session 2 (p < .001), while the control group improved for negative agreement (p < .001). Thirty-eight per cent of the tooth surfaces overlapped, and the mean positive agreement and negative agreement were significantly lower for overlapping surfaces than non-overlapping surfaces (p < .001) in both groups.
Training with the AI software did not impact on dental students' ability to detect proximal enamel caries in bitewing radiographs although the positive agreement improved over time. It was revealed that proximal tooth overlap interfered with caries detection.
牙病诊断教学是口腔医学教育的重要组成部分。诊断邻面龋是一项具有挑战性的任务，因此引入了应用人工智能（AI）的自动化系统来辅助诊断。因此，可以考虑将 AI 应用于教学目的。本研究的目的是评估 AI 软件对学生在咬合翼片（BW）中检测釉质内邻面龋能力的影响，并评估邻面牙齿重叠是否会干扰龋病检测。
本研究纳入了 74 名牙科学生，随机分为实验组和对照组。两组学生在两个阶段均对 BW 中的邻面釉质龋进行评估。在第一阶段，实验组使用 AI 软件（AssistDent®）在 25 张 BW 中登记龋病，对照组不使用 AI。一个月后，两组学生在临床环境中不使用软件对另外 25 张 BW 中的龋病进行检测。学生的登记结果与参考标准进行比较。计算阳性一致率（有龋）和阴性一致率（无龋），并应用 t 检验评估实验组和对照组的表现是否存在差异。此外，还应用 t 检验测试邻面重叠是否会干扰龋病登记。
根据参考标准，在第一和第二阶段，分别有 56 和 52 个牙面被检测出仅有釉质龋。在第一阶段，实验组（42%）和对照组（48%）的阳性一致率无显著差异（p=0.08），而实验组的阴性一致率更高（86% vs. 80%；p=0.02）。在第二阶段，两组之间无显著差异。实验组的阳性一致率从第一阶段到第二阶段有所提高（p<0.001），而对照组的阴性一致率有所提高（p<0.001）。38%的牙面重叠，在两组中，重叠牙面的阳性一致率和阴性一致率均显著低于非重叠牙面（p<0.001）。
尽管实验组的阳性一致率随着时间的推移而提高，但使用 AI 软件进行培训并未影响学生在 BW 中检测邻面釉质龋的能力。研究结果表明，邻面牙齿重叠会干扰龋病检测。
