TY - JOUR
T1 - Automatic Detection of Periapical Osteolytic Lesions on Cone-beam Computed Tomography Using Deep Convolutional Neuronal Networks
AU - Kirnbauer, Barbara
AU - Hadzic, Arnela
AU - Jakse, Norbert
AU - Bischof, Horst
AU - Stern, Darko
N1 - Funding Information:
All authors gave their final approval and agree to be accountable for all aspects of the work. The authors would like to thank Enago (www.enago.com/) for the English language review. This study did not receive any funding. The authors deny any conflicts of interest related to this study
Publisher Copyright:
© 2022 The Authors
PY - 2022/11
Y1 - 2022/11
N2 - Introduction: Cone-beam computed tomography (CBCT) is an essential diagnostic tool in oral radiology. Radiolucent periapical lesions (PALs) represent the most frequent jaw lesions. However, the description, interpretation, and documentation of radiological findings, especially incidental findings, are time-consuming and resource-intensive, requiring a high degree of expertise. To improve quality, dentists may use artificial intelligence in the form of deep learning tools. This study was conducted to develop and validate a deep convolutional neuronal network for the automated detection of osteolytic PALs in CBCT data sets. Methods: CBCT data sets from routine clinical operations (maxilla, mandible, or both) performed from January to October 2020 were retrospectively screened and selected. A 2-step approach was used for automatic PAL detection. First, tooth localization and identification were performed using the SpatialConfiguration-Net based on heatmap regression. Second, binary segmentation of lesions was performed using a modified U-Net architecture. A total of 144 CBCT images were used to train and test the networks. The method was evaluated using the 4-fold cross-validation technique. Results: The success detection rate of the tooth localization network ranged between 72.6% and 97.3%, whereas the sensitivity and specificity values of lesion detection were 97.1% and 88.0%, respectively. Conclusions: Although PALs showed variations in appearance, size, and shape in the CBCT data set and a high imbalance existed between teeth with and without PALs, the proposed fully automated method provided excellent results compared with related literature.
AB - Introduction: Cone-beam computed tomography (CBCT) is an essential diagnostic tool in oral radiology. Radiolucent periapical lesions (PALs) represent the most frequent jaw lesions. However, the description, interpretation, and documentation of radiological findings, especially incidental findings, are time-consuming and resource-intensive, requiring a high degree of expertise. To improve quality, dentists may use artificial intelligence in the form of deep learning tools. This study was conducted to develop and validate a deep convolutional neuronal network for the automated detection of osteolytic PALs in CBCT data sets. Methods: CBCT data sets from routine clinical operations (maxilla, mandible, or both) performed from January to October 2020 were retrospectively screened and selected. A 2-step approach was used for automatic PAL detection. First, tooth localization and identification were performed using the SpatialConfiguration-Net based on heatmap regression. Second, binary segmentation of lesions was performed using a modified U-Net architecture. A total of 144 CBCT images were used to train and test the networks. The method was evaluated using the 4-fold cross-validation technique. Results: The success detection rate of the tooth localization network ranged between 72.6% and 97.3%, whereas the sensitivity and specificity values of lesion detection were 97.1% and 88.0%, respectively. Conclusions: Although PALs showed variations in appearance, size, and shape in the CBCT data set and a high imbalance existed between teeth with and without PALs, the proposed fully automated method provided excellent results compared with related literature.
KW - Artificial intelligence
KW - deep learning/machine learning
KW - digital imaging/radiology
KW - inflammation
KW - oral diagnosis
UR - http://www.scopus.com/inward/record.url?scp=85137711782&partnerID=8YFLogxK
U2 - 10.1016/j.joen.2022.07.013
DO - 10.1016/j.joen.2022.07.013
M3 - Article
C2 - 35952897
AN - SCOPUS:85137711782
VL - 48
SP - 1434
EP - 1440
JO - Journal of Endodontics
JF - Journal of Endodontics
SN - 0099-2399
IS - 11
ER -