Deep learning-based segmentation of dental implants on cone-beam computed tomography images: A validation study.

Objectives: To train and validate a cloud-based convolutional neural network (CNN) model for automated segmentation (AS) of dental implant and attached prosthetic crown on cone-beam computed tomography (CBCT) images.

Methods: A total dataset of 280 maxillomandibular jawbone CBCT scans was acquired from patients who underwent implant placement with or without coronal restoration. The dataset was randomly divided into three subsets: training set (n = 225), validation set (n = 25) and testing set (n = 30).

Robust and efficient estimation of nonparametric generalized linear models.

Unlabelled: Generalized linear models are flexible tools for the analysis of diverse datasets, but the classical formulation requires that the parametric component is correctly specified and the data contain no atypical observations. To address these shortcomings, we introduce and study a family of nonparametric full-rank and lower-rank spline estimators that result from the minimization of a penalized density power divergence. The proposed class of estimators is easily implementable, offers high protection against outlying observations and can be tuned for arbitrarily high efficiency in the case of clean data.

RNA biomarkers from proximal liquid biopsy for diagnosis of ovarian cancer.

Background: Most ovarian cancer patients are diagnosed at an advanced stage and have a high mortality rate. Current screening strategies fail to improve prognosis because markers that are sensitive for early stage disease are lacking. This medical need justifies the search for novel approaches using utero-tubal lavage as a proximal liquid biopsy.

Pancreas Islet Cell-Specific Antibody Detection by ELISA.

Background: Islet cell-specific autoantibodies are useful to classify diabetes. The aim of this study was to evaluate the performance of commercially available ELISAs to detect autoantibodies to glutamic acid decarboxylase 65-kDa isoform (GADA), tyrosine phosphatase-related islet antigen 2 (IA-2A), zinc transporter protein 8 (ZnT8A), and insulin (IAA). The performance of ELISA was compared to the performance of RIA.

Development and validation of a novel artificial intelligence driven tool for accurate mandibular canal segmentation on CBCT.

Objectives: The objective of this study is the development and validation of a novel artificial intelligence driven tool for fast and accurate mandibular canal segmentation on cone beam computed tomography (CBCT).

Methods: A total of 235 CBCT scans from dentate subjects needing oral surgery were used in this study, allowing for development, training and validation of a deep learning algorithm for automated mandibular canal (MC) segmentation on CBCT. Shape, diameter and direction of the MC were adjusted on all CBCT slices using a voxel-wise approach.