Dentomaxillofac Radiol
November 2024
Objective: To develop an accurate method for converting dose-area product (DAP) to patient dose for dental cone-beam computed tomography (CBCT) using deep learning.
Methods: 24,384 CBCT exposures of an adult phantom were simulated with PCXMC 2.0, using permutations of tube voltage, filtration, source-isocenter distance, beam width/height and isocenter position.
Aims: Low cardiorespiratory fitness (CRF) is associated with functional disability, heart failure and mortality. Left ventricular (LV) end-diastolic volume (LVEDV) has been linked with CRF, but its utility as a diagnostic marker of low CRF has not been tested.
Methods: This multi-center international cohort examined the relationship between LV size on echocardiography and CRF (peak oxygen uptake [peak VO2] from cardiopulmonary exercise testing) in individuals with LV ejection fraction ≥50%.
Background: To support dentists with limited experience, this study trained and compared six convolutional neural networks to detect crossbites and classify non-crossbite, frontal, and lateral crossbites using 2D intraoral photographs.
Methods: Based on 676 photographs from 311 orthodontic patients, six convolutional neural network models were trained and compared to classify (1) non-crossbite vs. crossbite and (2) non-crossbite vs.
Objectives: The aim of this study was to identify cone-beam computed tomography (CBCT) protocols that offer an optimal balance between effective dose (ED) and 3D model for orthognathic virtual surgery planning, using CT as a reference, and to assess whether such protocols can be defined based on technical image quality metrics.
Methods: Eleven CBCT (VISO G7, Planmeca Oy, Helsinki, Finland) scan protocols were selected out of 32 candidate protocols, based on ED and technical image quality measurements. Next, an anthropomorphic RANDO SK150 phantom was scanned using these 11 CBCT protocols and 2 CT scanners for bone quantity assessments.