2020
DOI: 10.1111/iej.13265
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Evaluation of artificial intelligence for detecting periapical pathosis on cone‐beam computed tomography scans

Abstract: Orhan K, Bayrakdar IS, Ezhov M, Kravtsov A, € Ozy€ urek T. Evaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans. ) methods were compared using Wilcoxon signed rank test and Bland-Altman analysis.Results The deep convolutional neural network system was successful in detecting teeth and numbering specific teeth. Only one tooth was incorrectly identified. The AI system was able to detect 142 of a total of 153 periapical lesions. The reliability of correctl… Show more

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Cited by 180 publications
(162 citation statements)
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References 53 publications
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“…Focusing on the binary presence or absence of lesions, DL identified proximal carious lesions from near-infrared transillumination images with an area under the receiver operating characteristic curve of 0.856 (Casalegno et al 2019). Volumetric measurement in CBCT, following DL-based segmentation, was reported to be comparable to the results obtained from manual segmentation of periapical lesions (Orhan et al 2020). However, this research failed to report outcomes such as the volume deviation of lesions and the Intersection over Union metric.…”
Section: Applications Of Ai In Dentistrymentioning
confidence: 63%
“…Focusing on the binary presence or absence of lesions, DL identified proximal carious lesions from near-infrared transillumination images with an area under the receiver operating characteristic curve of 0.856 (Casalegno et al 2019). Volumetric measurement in CBCT, following DL-based segmentation, was reported to be comparable to the results obtained from manual segmentation of periapical lesions (Orhan et al 2020). However, this research failed to report outcomes such as the volume deviation of lesions and the Intersection over Union metric.…”
Section: Applications Of Ai In Dentistrymentioning
confidence: 63%
“…Orhan et al verified the performance of a deep learning algorithm using CBCT images to detect and volumetrically measure periapical lesions [28]. A detection rate of 92.8% and a significant positive correlation between the automated and manual measurements were reported.…”
Section: Automated Diagnosis Of Dental and Maxillofacial Diseasesmentioning
confidence: 99%
“…Table 1 exhibits the included studies regarding the use of AI for 3D imaging in DMFR. These studies focused on three main applications, including automated diagnosis of dental and maxillofacial diseases [16][17][18][19][20][28][29][30][31][32], localization of anatomical landmarks for orthodontic and orthognathic treatment planning [21,22,[33][34][35], and improvement of image quality [23,36].…”
Section: Current Use Of Ai For 3d Imaging In Dmfrmentioning
confidence: 99%
“…It offers distinct advantages including lower radiation doses, compared to medical CT, and the potential of importing and exporting individualized, DICOM and overlap-free reconstructed data to and from other applications [4][5][6][7] . CBCT can supply high-resolution three-dimensional (3D) images without distortion and superimposition of bone and other dental structures that can be seen in conventional radiography [8][9] .…”
Section: Introductionmentioning
confidence: 99%