Purpose-One critical step in routine orthognathic surgery is to reestablish a desired final dental occlusion. Traditionally, the final occlusion is established by hand articulating stone dental models. To date, there are still no effective solutions to establish the final occlusion in computeraided surgical simulation. In this study, we consider the most common one-piece maxillary orthognathic surgery and propose a three-stage approach to digitally and automatically establish the desired final dental occlusion.Methods-The process includes three stages: (1) extraction of points of interest and teeth landmarks from a pair of upper and lower dental models; (2) establishment of Midline-Canine-Molar (M-C-M) relationship following the clinical criteria on these three regions; and (3) fine alignment of upper and lower teeth with maximum contacts without breaking the established M-C-M relationship. Our method has been quantitatively and qualitatively validated using 18 pairs of dental models.Results-Qualitatively, experienced orthodontists assess the algorithm-articulated and handarticulated occlusions while being blind to the methods used. They agreed that occlusion results of the two methods are equally good. Quantitatively, we measure and compare the distances between selected landmarks on upper and lower teeth for both algorithm-articulated and hand-articulated James J. Xia,

Purpose: Patient information leaflets are designed to provide easy to follow information summaries and first point of contact information about treatment options. This survey reviewed the content of dental implant patient information leaflets, produced by implant companies and available within the UK in 2011.Methods: Dental implant companies in the UK were asked to provide samples of their patient information leaflets. The information within the leaflets was then summarised as well as the quantity, the types of images used to illustrate the leaflets and whether the source of the information was given. Quantitative data was obtained on the amount of information provided, size of images and number of references.Results: A response rate of 71% was obtained, and 23 leaflets were studied. Great variation was found between the leaflets, with the word counts ranging from 88 to 5434, and 44 different topics were identified. The majority of the images used were decorative, and none of the leaflets gave any reference to the sources of their information. Implant treatment was generally described in a positive way, concentrating on describing the treatment and giving the advantages. Much less information was given about the potential disadvantages and risks of complications or failure, including the relevance of periodontal disease or smoking. Conclusion:Implant patient information leaflets provided by dental implant companies should not be solely relied upon to provide patients with all the information they need to give informed consent to treatment.

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Federated learning (FL) can collaboratively train deep learning models using isolated patient data owned by different hospitals for various clinical applications, including medical image segmentation. However, a major problem of FL is its performance degradation when dealing with the data that are not independently and identically distributed (non-iid), which is often the case in medical images. In this paper, we first conduct a theoretical analysis on the FL algorithm to reveal the problem of model aggregation during training on non-iid data. With the insights gained through the analysis, we propose a simple and yet effective method, federated cross learning (FedCross), to tackle this challenging problem. Unlike the conventional FL methods that combine multiple individually trained local models on a server node, our FedCross sequentially trains the global model across different clients in a roundrobin manner, and thus the entire training procedure does not involve any model aggregation steps. To further improve its performance to be comparable with the centralized learning method, we combine the FedCross with an ensemble learning mechanism to compose a federated cross ensemble learning (FedCrossEns) method. Finally, we conduct extensive experiments using a set of public datasets. The experimental results show that the proposed FedCross training strategy outperforms the mainstream FL methods on non-iid data. In addition to improving the segmentation performance, our FedCrossEns can further provide a quantitative estimation of the model uncertainty, demonstrating the effectiveness and clinical significance of our designs. Source code will be made publicly available after paper publication.