Showing results (11-20 of 38) with videos related to

Sort By:
Pageof 4
Diagnostics (Basel, Switzerland)|February 26, 2025
An Assessment of Deep Learning's Impact on General Dentists' Ability to Detect Alveolar Bone Loss in 2D Intraoral RadiographsAmjad AlGhaihab, Antonio J Moretti, Jonathan Reside, et al.
Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodontics|April 2, 2002
Evaluation of tuned-aperture computed tomography in the detection of simulated periodontal defectsAruna Ramesh, John B Ludlow, Richard L Webber, et al.
Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology|December 28, 2023
Evaluation of deep learning for detecting intraosseous jaw lesions in cone beam computed tomography volumesYiing-Shiuan Huang, Pavel Iakubovskii, Li Zhen Lim, et al.
Journal of Periodontal Research|May 15, 2002
Comparison of conventional and TACT (Tuned Aperture Computed Tomography) digital subtraction radiography in detection of pericrestal bone-gainOnanong Chai-U-Dom, John B Ludlow, Donald A Tyndall, et al.
Journal of Dental Hygiene : JDH|October 4, 2005
Interactive computer-assisted instruction vs. lecture format in dental educationW Bruce Howerton, Platin R T Enrique, John B Ludlow, et al.
Journal of Dental Hygiene : JDH|April 9, 2002
Pre-clinical performance comparing intraoral film and CCD-based systemsTracey Malarkey Sommers, Sally M Mauriello, John B Ludlow, et al.
Journal of Prosthodontics : Official Journal of the American College of Prosthodontists|September 26, 2003
Tuned aperture computed tomography (TACT) for cross-sectional implant site assessment in the posterior mandibleBehnoush Rashedi, Donald A Tyndall, John B Ludlow, et al.
Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology|January 28, 2019
Comparing the diagnostic efficacy of intraoral radiography and cone beam computed tomography volume registration in the detection of mandibular alveolar bone defectsPeter T Green, André Mol, Antonio J Moretti, et al.
Dento Maxillo Facial Radiology|August 18, 2022
The effect of a deep-learning tool on dentists' performances in detecting apical radiolucencies on periapical radiographsManal H Hamdan, Lyudmila Tuzova, André Mol, et al.
Pageof 4