Insights into Predicting Tooth Extraction from Panoramic Dental Images: Artificial Intelligence vs. Dentists.

OBJECTIVES: Tooth extraction is one of the most frequently performed medical procedures. The indication is based on the combination of clinical and radiological examination and individual patient parameters and should be made with great care. However, determining whether a tooth should be extracted is not always a straightforward decision. Moreover, visual and cognitive pitfalls in the analysis of radiographs may lead to incorrect decisions. Artificial intelligence (AI) could be used as a decision support tool to provide a score of tooth extractability. MATERIAL AND METHODS: Using 26,956 single teeth images from 1,184 panoramic radiographs (PANs), we trained a ResNet50 network to classify teeth as either extraction-worthy or preservable. For this purpose, teeth were cropped with different margins from PANs and annotated. The usefulness of the AI-based classification as well that of dentists was evaluated on a test dataset. In addition, the explainability of the best AI model was visualized via a class activation mapping using CAMERAS. RESULTS: The ROC-AUC for the best AI model to discriminate teeth worthy of preservation was 0.901 with 2% margin on dental images. In contrast, the average ROC-AUC for dentists was only 0.797. With a 19.1% tooth extractions prevalence, the AI model's PR-AUC was 0.749, while the dentist evaluation only reached 0.589. CONCLUSION: AI models outperform dentists/specialists in predicting tooth extraction based solely on X-ray images, while the AI performance improves with increasing contextual information. CLINICAL RELEVANCE: AI could help monitor at-risk teeth and reduce errors in indications for extractions.

Marginal quality and wear of extended posterior resin composite restorations: Eight-year results in vivo.

AIM: To evaluate wear and marginal quality of resin composite restorations over eight years of clinical service in vivo. METHODS: 30 patients received 68 resin composite restorations (36 Grandio, 32 Tetric Ceram) in the course of a prospective clinical trial. 3-D evaluation of 36 selected teeth involving 144 epoxy replicas was carried out using a special 3-D scanning device with an accuracy of U1=2.5+L/350 µm; U3=3+L/300 µm. In vivo replicas were sputter-coated with gold and examined under a SEM at 200× magnification. Marginal integrity between resin composite and enamel was expressed as a percentage of the entire judgeable margin length. RESULTS: During the clinical 8-year observation period, wear significantly increased in the restored areas as well as in OCAs. RBCs under investigation showed no significant differences regarding wear (p>0.05). Localization of the restorations (premolar vs. molar or upper vs. lower) did not show a significant influence on wear rates (p>0.05). Clinically, by SEM, and by 3-D scanning distinct changes of worn contours on enamel and RBC were visible. Quantitative margin analysis revealed a change of perfect margins (58% at baseline vs. 14% at 8 years), positive step formations (15% at baseline vs. 10% at 8 years), and negative step formations (20% at baseline vs. 71% at 8 years) over time (p<0.001). Regarding the portion of gap-free margins (baseline vs. 2 years) and total margin length (baseline to 8 years), Grandio showed lower values (Mann-Whitney U-test; p<0.05). The portion of negative step formations was lower for Tetric Ceram at baseline (Mann-Whitney-U test; p<0.05). CONCLUSIONS: After eight years of clinical service, neither wear nor marginal quality was a critical factor for estimation and survival of extended posterior resin composite restorations.