A deep learning approach to automatic detection and profiling of dental caries in bitewing and periapical radiographs

Abstract

<h3>Objective</h3> Dental caries is the most prevalent noncommunicable disease among humans, but routine misdiagnosis remains an underappreciated challenge. This study aimed to answer the challenge with the latest advances in artificial intelligence (AI), machine/deep learning, and computer vision. This study was conducted by an interdisciplinary team of academic clinicians and a dental AI startup. <h3>Study Design</h3> We curated and methodically annotated an imbalanced database of bitewing and periapical radiographs using a caries ontology derived from the ICDAS framework. Using this dataset, we leveraged machine learning algorithms to train predictive models to perform segmentation and classification of dental caries on novel radiographs. Given the trained model, we designed a comparison study to measure the qualitative and quantitative performance of the trained neural networks vs a set of experts. The objective of the study was to assess the expected effectiveness of model performance, and validate model behavior in direct comparison with humans on the same tasks. The study consisted of 3 dentists interacting with a web-based platform for collecting annotations of de-identified radiographs. Fifteen radiographs were used for the pilot study. <h3>Results</h3> In the human-human comparisons, there was 4% strong disagreement, 48% minor disagreement, and 48% agreement. In the majority of cases (69%), the neural network model was either perfectly correct or close with some nuance. In no case was the model completely incorrect (0%); it either missed one or more caries or produced a false positive (the remaining 31%). In general, it matched or exceeded the expert evaluation of radiographs. <h3>Conclusion</h3> We contend that these results show how neural network-based machine learning models hold considerable promise in providing assistance to dentists. We intend to pursue larger-scale human-machine comparison studies in subsequent efforts. <b>Statement of Ethical Review</b> Ethical review was sought and study was exempted from ethical review