Reproducibility of Electrical Caries Measurements: A Technical Problem?

Promises and perils of artificial intelligence in dentistry

Artificial intelligence (AI) is a subdiscipline of computer science that has made substantial progress in medicine and there is a growing body of AI research in dentistry. Dentists should have an understanding of the foundational concepts and the ability to critically evaluate dental research in AI. Machine learning (ML) is a subfield of AI that most dental AI research is dedicated to. The most prolific area of ML research is automated interpretation of dental imaging. Other areas include providing treatment recommendations, predicting future disease and treatment outcomes. The research impact is limited by small datasets that do not harness the positive correlation between very large datasets and ML performance. There is also a need to standardize research methodologies and utilize performance metrics that are appropriate for the clinical context. In addition to research challenges, this article discusses the ethical, legal and logistical considerations associated with implementation in clinical practice. This includes explainable AI, model bias, data privacy and security. The future implications of AI in dentistry involve a promise for a novel form of practicing dentistry however, the effect of AI on patient outcomes is yet to be determined.

An in vitro comparison of different diagnostic methods in detection of residual dentinal caries

The aim of this study was to investigate the efficiency of different diagnostic methods in detection of residual dentinal caries in excavated cavities. Fifty extracted molar with deep dentinal carious lesions were excavated using a slow-speed handpiece. All cavities were assessed by laser fluorescence(LF) device, electronic caries monitor(ECM), and caries detector dye(CDD) by three independent observers blindly. The measurements were repeated after two weeks. Specimens containing dentin slices 150 μm in thickness were prepared for histological analyses. The existence and absence of carious dentin was determined using a lightmicroscope. The average intraobserver accuracy was 1.00 (perfect agreement) for CDD, 0.86 (excellent agreement) for ECM, and 0.50 (good agreement) for LF. The average interobserver accuracy values were 0.92 (excellent agreement), (0.36 marginal agreement) and 0.48 (good agreement), for CDD, ECM, and LF, respectively. The average specificity was 0.60 for CDD, 73% for ECM, and 0.50 for LF. The average sensitivity was 0.55 for CDD, 0.85 for LF, and 0.47 for ECM. The average accuracy values were 0.53, 0.51, and 0.81 for CDD, ECM, and LF, respectively. LF had the greatest sensitivity and accuracy values of any of the methods tested. As a conclusion, LF device is appeared to most reliable method in detection of remain caries in cavity. However, because of its technical sensitivity it may susceptible to variations in measurements. To pay attention to the rule of usage and repeated measurements can minimize such variations in clinical practice. It was concluded that LF is an improvement on the currently available aids for residual caries detection.