Segmentation of anatomical structures on dento-maxillo-facial (DMF) computed tomography (CT) or cone beam computed tomography (CBCT) scans is increasingly needed in digital dentistry. The main aim of this research was to propose and evaluate a novel open source tool called DentalSegmentator for fully automatic segmentation of five anatomical structures on DMF CT and CBCT scans: maxilla/upper skull, mandible, upper teeth, lower teeth, and the mandibular canal. A retrospective sample of 470 CT and CBCT scans was used as a training/validation set. The performance and generalizability of the tool was evaluated by comparing segmentations provided by experts and automatic segmentations in two hold-out test datasets: an internal dataset of 133 CT and CBCT scans acquired before orthognathic surgery and an external dataset of 123 CBCT scans randomly sampled from routine examinations in 5 institutions. The mean overall results in the internal test dataset (n = 133) were a Dice similarity coefficient (DSC) of 92.2 ± 6.3 % and a normalised surface distance (NSD) of 98.2 ± 2.2 %. The mean overall results on the external test dataset (n = 123) were a DSC of 94.2 ± 7.4 % and a NSD of 98.4 ± 3.6 %. The results obtained from this highly diverse dataset demonstrate that this tool can provide fully automatic and robust multiclass segmentation for DMF CT and CBCT scans. To encourage the clinical deployment of DentalSegmentator, the pre-trained nnU-Net model has been made publicly available along with an extension for the 3D Slicer software. DentalSegmentator open source 3D Slicer extension provides a free, robust, and easy-to-use approach to obtaining patient-specific three-dimensional models from CT and CBCT scans. These models serve various purposes in a digital dentistry workflow, such as visualization, treatment planning, intervention, and follow-up.

Background: Opioid analgesics are commonly used along with propofol during general anesthesia. Due to the dearth of data on the quality of anesthesia achieved with this combination, the present meta-analysis was carried out. Methods: Electronic databases were searched for appropriate studies using a suitable search strategy. Randomized clinical trials comparing the combination of remifentanil/sufentanil/alfentanil with propofol with fentanyl and propofol, were included. The outcome measures were as follows: total propofol dose to achieve the desired general anesthesia; time of onset and duration of general anesthesia; depth of general anesthesia; and recovery time (time for eye-opening and time taken for extubation). Risk of bias was assessed and Forest plots were generated for eligible outcomes. The weighted mean difference [95% confidence intervals] was used as the effect estimate. Results: Fourteen studies were included in the systematic review and 13 were included in the metaanalysis. Statistically significant differences were observed for remifentanil in comparison to fentanyl when combined with propofol: Propofol dose (in mg) -76.18 [-94.72, -57.64]; time of onset of anesthesia (min) -0.44 [-0.74, -0.15]; time taken for eye-opening (min) -3.95 [-4.8, -3.1]; and time for extubation (min) -3.53 [-4.37, -2.7]. No significant differences were observed for either sufentanil or alfentanil about the dose of propofol required and due to scanty data, pooling of the data could not be attempted for other outcome measures for either sufentanil or alfentanil. Conclusion: To conclude, we found that remifentanil has a statistically significant anesthetic profile than fentanyl when combined with propofol. Scanty evidence for both alfentanil and sufentanil precludes any such confirmation.