Background To assess the root angle characteristics of maxillary incisors, and to analyze the relationship between the root angle and other implant-related anatomical indices to use the sagittal root angle as an index for immediate implant evaluation and design. Methods A random sample consisting of 400 cone-beam computed tomography (CBCT) images and 65 maxillary plaster models were selected for the present study. CBCT and stereolithography (STL) scan images were imported as DICOM files into coDiagnostiX software for matching the hard and soft tissue. The angle between the long axis of the anterior tooth and the corresponding alveolar bone and implant-related hard and soft tissue indices were measured in the sagittal section. Descriptive statistics, frequency analysis, multi-level comparisons, and correlation analyses were performed. Results The average sagittal root angles were 15° at the central incisor and 19° at the lateral incisor. The root angle in males was significantly larger than that in females, and increased with age. The largest angle, 22.35°, was found in the lateral incisors of the oldest (> 50 years old) male group. The root angle was found to correlate with coronal buccal bone thickness, coronal palatal bone thickness, apical buccal bone thickness, palatal bone thickness, and the below apex bone thickness. Conclusions The sagittal root angle could reflect the distribution of other implant-related anatomical indices, which may provide additional reference for the evaluation of immediate implant placement.

Background: Immediate implant placement in the esthetic area requires comprehensive assessments with nearly 30 quantitative indexes. Most artificial intelligence (AI)-driven measurements of quantitative indexes depend on segmentation or landmark detection, which require extra labeling of images and contain possible intraclass errors. Methods: For the initial attempt, the method was tested on sagittal root inclination measurement. This study had developed an accurate and efficient end-to-end model incorporating a convolutional neural network (CNN) based on unlabeled cone-beam computed tomography (CBCT) images for immediate implant placement diagnosis and treatment. The model took pretrained ResNeXt101 as the backbone and was constructed based on 2,920 CBCT images with corresponding angles of the tooth axis and bone axis. The performance of our CNN model was evaluated on a separate test set.Results: Our model exhibited high prediction accuracy in sagittal root inclination measurements, as evidenced by the low mean average error of 2.16 • , the high correlation coefficient of 0.915 to manual measurement, and the narrow 95% confidence interval shown by Bland-Altman plots. The intraclass correlation coefficient further confirmed the measurement accuracy of our model was comparable with that of junior clinicians. The model took merely 0.001 seconds for each CBCT image, making it highly efficient. To better understand the model's quality, we visualized our end-to-end CNN model through Guided Backpropagation, Grad-CAM, and Guided Grad-CAM, and confirmed its effectiveness in region recognition. Conclusions: We succeeded in taking the first step in constructing the end-toend immediate implant placement AI tool through sagittal root inclination measurements without intermediate steps and extra labeling on images.

No abstract