Use of artificial intelligence software in dental education: A study on assisted proximal caries assessment in bitewing radiographs

Abstract

AbstractIntroductionTeaching 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.Materials and MethodsThe 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.ResultsAt 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.ConclusionTraining 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.