The purpose of this study was to classify 10 cone-beam computed tomographic (CBCT) devices using a ranking model according to the detection of fine endodontic structures.
A dedicated dentate anthropomorphic phantom was scanned 2 times using 10 CBCT devices without any metal (metal-free condition) and with an endodontically treated tooth containing a metallic post (metal condition). A reference image acquired on an industrial micro-CT scanner was used to register all CBCT images, yielding corresponding anatomic slices. Afterward, 3 experienced observers assessed all acquired CBCT images for their ability to assess a narrow canal, isthmus, and apical delta ramification using a categoric rank from 1 (best) to 10 (worst). Fleiss kappa statistics were used to calculate intra- and interobserver agreements for each CBCT device separately. Based on the observers’ scores, general linear mixed models were applied to compare image quality among different CBCT devices for performing endodontic diagnostic tasks (α = .05).
The 10 CBCT devices performed differently for the evaluated endodontic tasks (P < .05), with 3 devices performing better for endodontic feature detection. Yet, in the presence of metal, only 2 devices were able to keep a high level of endodontic feature detection.
The evaluated endodontic tasks were CBCT device dependent, and their detection was influenced by the presence of metal.
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Published online: March 07, 2021
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