Inconsistent positioning of patients and region of interest (ROI) is known to influence the precision of bone mineral density (BMD) measurements in the spine and hip. However, it is unknown whether minor shifts in the positioning of the ROI along the shaft of the radius affect the measurement of forearm BMD and its subregions. The ultradistal (UD-), mid-, one-third, and total radius BMDs of 50 consecutive clinical densitometry patients were acquired. At baseline the distal end of the ROI was placed at the tip of the ulnar styloid as usual, and then the forearm was reanalyzed 10 more times, each time shifting the ROI 1 mm proximally. No corrections for multiple comparisons were necessary since the differences that were significant were significant at p < 0.001. The UD-radius BMD increased as the ROI was shifted proximally; the increase was significant when shifted even 1 mm proximally (p < 0.001). These same findings held true for the mid- and total radius bone density, though the percent increase with moving proximally was significantly greater for the UD radius than for the other subregions. However, there was no significant change in the one-third radius BMD when shifted proximally 1-10 mm. Minor proximal shifts of the forearm ROI substantially affect the BMD of the UD-, mid- and total radius, while having no effect on the one-third radius BMD. Since the one-third radius is the only forearm region usually reported, minor proximal shifts of the ROI should not influence forearm BMD results significantly.
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http://dx.doi.org/10.1016/j.jocd.2017.12.005 | DOI Listing |
Background: Transcranial Electrical Stimulation (TES), Temporal Interference Stimulation (TIS), Electroconvulsive Therapy (ECT) and Tumor Treating Fields (TTFields) are based on the application of electric current patterns to the brain.
Objective: The optimal electrode positions, shapes and alignments for generating a desired current pattern in the brain vary between persons due to anatomical variability. The aim is to develop a flexible and efficient computational approach to determine individually optimal montages based on electric field simulations.
Imaging Sci Dent
December 2024
Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Naples, Italy.
Purpose: This study aimed to evaluate the impact of a metal artifact reduction (MAR) algorithm on cone-beam computed tomography (CBCT) scans of titanium and zirconia implants, both within and outside the field of view (FOV).
Materials And Methods: In this study, a dry human mandible was positioned in a CBCT scanner with only its left quadrant included in the FOV. Each type of implant (titanium and zirconia) was placed once in the right second premolar extraction socket and once in the left second premolar extraction socket of the mandible.
Front Physiol
December 2024
Department of Oral & Maxillofacial Surgery, Shenzhen Stomatology Hospital, Affiliated to Shenzhen University, Shenzhen, Guangdong Province, China.
Introduction: This study aimed to develop a deep learning-based method for interpreting magnetic resonance imaging (MRI) scans of temporomandibular joint (TMJ) anterior disc displacement (ADD) and to formulate an automated diagnostic system for clinical practice.
Methods: The deep learning models were utilized to identify regions of interest (ROI), segment TMJ structures including the articular disc, condyle, glenoid fossa, and articular tubercle, and classify TMJ ADD. The models employed Grad-CAM heatmaps and segmentation annotation diagrams for visual diagnostic predictions and were deployed for clinical application.
Oral Radiol
December 2024
Department of Oral, Dental and Maxillofacial Radiology, Faculty of Dentistry, Ataturk University, Erzurum, 25240, Turkey.
Objective: The aim of this study is to determine the contact relationship and position of impacted mandibular third molar teeth (IMM) with the mandibular canal (MC) in panoramic radiography (PR) images using deep learning (DL) models trained with the help of cone beam computed tomography (CBCT) and DL to compare the performances of the architectures.
Methods: In this study, a total of 546 IMMs from 290 patients with CBCT and PR images were included. The performances of SqueezeNet, GoogLeNet, and Inception-v3 architectures in solving four problems on two different regions of interest (RoI) were evaluated.
Biomed Eng Online
December 2024
Department of Clinical Physiology, Motion Analysis Center, University Hospital of Toulouse, Hôpital de Purpan, Toulouse, France.
Background: Stroke is the leading cause of acquired motor deficiencies in adults. Restoring prehension abilities is challenging for individuals who have not recovered active hand opening capacities after their rehabilitation. Self-triggered functional electrical stimulation applied to finger extensor muscles to restore grasping abilities in daily life is called grasp neuroprosthesis (GNP) and remains poorly accessible to the post-stroke population.
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