Literature DB >> 12070519

Radiographic identification of nonthreaded endosseous dental implants.

Indira G Sahiwal1, Ronald D Woody, Byron W Benson, Guillermo E Guillen.   

Abstract

STATEMENT OF PROBLEM: The identification of dental implant bodies in patients without available records is a considerable problem due to increased patient mobility and to the large number of implant systems with different designs.
PURPOSE: The purpose of this study was to document features that would help dentists identify non-threaded implant bodies from their radiographic images.
MATERIAL AND METHODS: More than 50 implant manufacturers were contacted and asked to provide implants with dimensions as close as possible to 3.75 mm (diameter) x 10 mm (length). Forty-four implants were donated, 16 of which were identified as non-threaded. Radiographs were made of these implants at 0 degrees, 30 degrees, 60 degrees, and 90 degrees horizontal rotation combined with -20 degrees, -10 degrees, 0 degrees, +10 degrees, and +20 degrees vertical inclination relative to the radiographic beam and film. A total of 20 images per implant were taken and examined to identify consistent, unique features that would aid in implant identification. At a 20 degrees vertical inclination, vital features of implants were distorted enough to be deemed unrecognizable. Therefore, only those observations made from radiographs between -10 degrees and +10 degrees vertical inclination were used for implant identification purposes.
RESULTS: All implants could be recognized from radiographs made between -10 degrees and +10 degrees vertical inclination. A series of tables and flowcharts describe the implants according to their identifying features.
CONCLUSION: Information from this study should help dentists identify non-threaded endosseous implants from their radiographic images.

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Year:  2002        PMID: 12070519     DOI: 10.1067/mpr.2002.124431

Source DB:  PubMed          Journal:  J Prosthet Dent        ISSN: 0022-3913            Impact factor:   3.426


  3 in total

1.  A Performance Comparison between Automated Deep Learning and Dental Professionals in Classification of Dental Implant Systems from Dental Imaging: A Multi-Center Study.

Authors:  Jae-Hong Lee; Young-Taek Kim; Jong-Bin Lee; Seong-Nyum Jeong
Journal:  Diagnostics (Basel)       Date:  2020-11-07

2.  Machine learning for identification of dental implant systems based on shape - A descriptive study.

Authors:  Veena Basappa Benakatti; Ramesh P Nayakar; Mallikarjun Anandhalli
Journal:  J Indian Prosthodont Soc       Date:  2021 Oct-Dec

3.  Efficacy of deep convolutional neural network algorithm for the identification and classification of dental implant systems, using panoramic and periapical radiographs: A pilot study.

Authors:  Jae-Hong Lee; Seong-Nyum Jeong
Journal:  Medicine (Baltimore)       Date:  2020-06-26       Impact factor: 1.817

  3 in total

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