Accuracy of dental calculus detection using digital radiography and image manipulation.

The aim of this study was to determine if image enhancement improves a clinician's ability to identify the presence of calculus on digital radiographs. Seventy-one hopeless teeth were collected from 34 patients. Teeth were stained with 1% methylene blue, the largest interproximal calculus deposit was scored, and photographs of each interproximal root surface were taken. The surface area of calculus deposit was determined as a percentage of the total interproximal root surface area. Digital radiographs of teeth taken before extraction were modified using the following enhancements: auto-contrast, emboss, invert, and sharpen. Radiographic presence of calculus was determined by two examiners. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for each examiner and enhancement. A receiver operating characteristic curve was used to compare differences between the image enhancements in the detection of dental calculus. The kappa statistic was used to compare ratings between examiners. None of the enhanced images were statistically superior to original images in identifying radiographic calculus (P > 0.05). The average sensitivity of digital radiography was 50%, average specificity was 82.2%, PPV was 94%, and NPV 23.2%. A threshold of >30% of interproximal root surface covered with calculus and increasing size of deposits were associated with improved detection (P < 0.05). Digital enhancements do not significantly improve radiographic detection of dental calculus. As area of calculus on the root surface and size of calculus deposits increased, sensitivity of detection also increased. © 2020 American Academy of Periodontology.

Citation

Jenna C Hyer, David E Deas, A Archontia Palaiologou, Marcel E Noujeim, Michael J Mader, Brian L Mealey. Accuracy of dental calculus detection using digital radiography and image manipulation. Journal of periodontology. 2021 Mar;92(3):419-427

Mesh Tags

PMID: 32786147

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