PURPOSE: The objective of this study was to develop a deep convolutional neural network (CNN) that would identify the brand and model of a dental implant from a radiograph. MATERIALS AND METHODS: A data augmentation procedure provided a total of 1,206 dental implant radiographic images of three different brands for six models (Nobel Biocare NobelActive [NNA] and Br.nemark System [NBS], Straumann Bone Level [SBL] and Tissue Level [STL], and Zimmer Biomet Dental Tapered Screw-Vent [ZTSV] and SwissPlus [ZSP]). They were divided into a test group (n = 241; 19.9%) and a training and validation group (n = 965; 80%). Preprocessing and transfer learning were applied to a pretrained GoogLeNet Inception CNN network. The accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC) of the CNN model were evaluated. RESULTS: The diagnostic accuracy was 93.8% (95% CI: 87.2% to 99.4%), the sensitivity was 93.5% (95% CI: 84.2% to 99.3%), the specificity was 94.2% (95% CI: 83.5% to 99.4%), the positive predictive value was 92% (95% CI: 83.9% to 97.2%), and the negative predictive value was 91.5% (95% CI: 80.2% to 97.1%). The deep CNN algorithm achieved an AUC of 0.918 (95% CI: 0.826 to 0.973) on NNA, 0.922 (95% CI: 0.831 to 0.964) on NBS, 0.909 (95% CI: 0.844 to 0.982) on SBL, 0.890 (95% CI: 0.783 to 0.945) on STL, 0.931 (95% CI: 0.867 to 0.979) on ZTSV, and 0.911 (95% CI: 0.811 to 0.957) on ZSP. CONCLUSION: The deep CNN model had a very good performance in identifying a dental implant from a radiograph. A huge and varied database of radiographs would have to be built up to be able to identify any dental implant.
PURPOSE: The objective of this study was to develop a deep convolutional neural network (CNN) that would identify the brand and model of a dental implant from a radiograph. MATERIALS AND METHODS: A data augmentation procedure provided a total of 1,206 dental implant radiographic images of three different brands for six models (Nobel Biocare NobelActive [NNA] and Br.nemark System [NBS], Straumann Bone Level [SBL] and Tissue Level [STL], and Zimmer Biomet Dental Tapered Screw-Vent [ZTSV] and SwissPlus [ZSP]). They were divided into a test group (n = 241; 19.9%) and a training and validation group (n = 965; 80%). Preprocessing and transfer learning were applied to a pretrained GoogLeNet Inception CNN network. The accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC) of the CNN model were evaluated. RESULTS: The diagnostic accuracy was 93.8% (95% CI: 87.2% to 99.4%), the sensitivity was 93.5% (95% CI: 84.2% to 99.3%), the specificity was 94.2% (95% CI: 83.5% to 99.4%), the positive predictive value was 92% (95% CI: 83.9% to 97.2%), and the negative predictive value was 91.5% (95% CI: 80.2% to 97.1%). The deep CNN algorithm achieved an AUC of 0.918 (95% CI: 0.826 to 0.973) on NNA, 0.922 (95% CI: 0.831 to 0.964) on NBS, 0.909 (95% CI: 0.844 to 0.982) on SBL, 0.890 (95% CI: 0.783 to 0.945) on STL, 0.931 (95% CI: 0.867 to 0.979) on ZTSV, and 0.911 (95% CI: 0.811 to 0.957) on ZSP. CONCLUSION: The deep CNN model had a very good performance in identifying a dental implant from a radiograph. A huge and varied database of radiographs would have to be built up to be able to identify any dental implant.