Computer vision with smartphone microphotography for detection of carious lesions

Abstract

ObjectivesTo evaluate the similarities in microphotographic images across different smartphones and to establish whether computer vision can use microphotographs to successfully classify dental caries. MethodA universal clip-type microscope with 60x optical zoom was selected to perform in vitro microphotography of extracted teeth. For the first objective, areas of cariogenic interest were physically labelled by dentists and eight smartphones were used to capture images of tooth decays with the microscope fitted over the primary camera lens. For the second objective, 233 microphotography images were acquired and virtually augmented to produce 1631 images that were categorized digitally by an international caries classification system for computer vision-based object detection (YOLO.v4). Five practitioners independently labelled randomly selected images from the test dataset following the caries classification system which were subsequently used to evaluate the diagnostic test accuracy of the YOLO model. ResultA significant overall mean square error [F (df) = 4.03 (6); P < 0.05] was observed while Bhattacharya's distance evaluation produced no significant differences [F (df) = 1.60 (6); P > 0.05] across all eight smartphone derived datasets. Index and reference test comparisons determined an overall sensitivity of 0.99 and specificity of 0.94 for the trained YOLO.v4 and highly significant correlations (r > 0.9, P < 0.001) to the classifications labelled by the dental practitioners. ConclusionNon-standardized images of tooth caries captured by different smartphones generated an accurate diagnostic model for classifying carious lesions that was similar to the visual assessments performed by experienced dental practitioners.