Lee, Dong-Woon; Kim, Sung-Yong; Jeong, Seong-Nyum; Lee, Jae-Hong
Diagnostics (Basel, Switzerland)
2021Feb ; 11 ( 2 ) :.
PMID : 33546446
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Lee, Dong-Woon - Department of Periodontology, Veterans Health Service Medical Center, Seoul 05368, Korea.
Kim, Sung-Yong - Department of Prosthodontics, Veterans Health Service Medical Center, Seoul 05368,
Jeong, Seong-Nyum - Department of Periodontology, Daejeon Dental Hospital, Institute of Wonkwang Dental
Lee, Jae-Hong - Department of Periodontology, Daejeon Dental Hospital, Institute of Wonkwang Dental
ABSTRACT
Fracture of a dental implant (DI) is a rare mechanical complication that is a critical cause of DI failure and explantation. The purpose of this study was to evaluate the reliability and validity of a three different deep convolutional neural network (DCNN) architectures (VGGNet-19, GoogLeNet Inception-v3, and automated DCNN) for the detection and classification of fractured DI using panoramic and periapical radiographic images. A total of 21,398 DIs were reviewed at two dental hospitals, and 251 intact and 194 fractured DI radiographic images were identified and included as the dataset in this study. All three DCNN architectures achieved a fractured DI detection and classification accuracy of over 0.80 AUC. In particular, automated DCNN architecture using periapical images showed the highest and most reliable detection (AUC = 0.984, 95% CI = 0.900-1.000) and classification (AUC = 0.869, 95% CI = 0.778-0.929) accuracy performance compared to fine-tuned and pre-trained VGGNet-19 and GoogLeNet Inception-v3 architectures. The three DCNN architectures showed acceptable accuracy in the detection and classification of fractured DIs, with the best accuracy performance achieved by the automated DCNN architecture using only periapical images.
keyword
artificial intelligence; deep learning; dental implants; supervised machine learning
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