3D morphometric quantification of maxillae and defects for patients with unilateral cleft palate via deep learning-based CBCT image auto-segmentation.

Objective: This study aimed to quantify the 3D asymmetry of the maxilla in patients with unilateral cleft lip and palate (UCP) and investigate the defect factors responsible for the variability of the maxilla on the cleft side using a deep-learning-based CBCT image segmentation protocol.

Setting And Sample Population: Cone beam computed tomography (CBCT) images of 60 patients with UCP were acquired. The samples in this study consisted of 39 males and 21 females, with a mean age of 11.

Machine learning in orthodontics: .

Objectives: To (1) introduce a novel machine learning method and (2) assess maxillary structure variation in unilateral canine impaction for advancing clinically viable information.

Materials And Methods: A machine learning algorithm utilizing Learning-based multi-source IntegratioN frameworK for Segmentation (LINKS) was used with cone-beam computed tomography (CBCT) images to quantify volumetric skeletal maxilla discrepancies of 30 study group (SG) patients with unilaterally impacted maxillary canines and 30 healthy control group (CG) subjects. Fully automatic segmentation was implemented for maxilla isolation, and maxillary volumetric and linear measurements were performed.

REDOSER project: optimising biological therapy dose for rheumatoid arthritis and spondyloarthritis patients.

Background: Reducing the dose of biological therapy (BT) when patients with immune-mediated arthritis achieve a sustained therapeutic goal may help to decrease costs for national health services and reduce the risk of serious infection. However, there is little information about whether such a decision can be applied universally. Therefore, the objective of this study was to develop appropriateness criteria for reducing the dose of BT in patients with rheumatoid arthritis (RA), axial spondyloarthritis (axSpA), and peripheral spondyloarthritis (pSpA).

Stock management in hospital pharmacy using chance-constrained model predictive control.

One of the most important problems in the pharmacy department of a hospital is stock management. The clinical need for drugs must be satisfied with limited work labor while minimizing the use of economic resources. The complexity of the problem resides in the random nature of the drug demand and the multiple constraints that must be taken into account in every decision.