the best radiographic method for determining root canal morphology in mandibular first premolars: a study of chinese descendants in taiwan

the best radiographic method for determining root canal morphology in mandibular first premolars: a study of chinese descendants in taiwan

;Yu Sun;Tzu-Yi Lu;Yi- Chen Chen;Shue-Fen Yang
world academy of science, engineering and technology 2016 Vol. 11 pp. 175-181
317
sun2016journalthe

Abstract

Background/purpose: There is large variation in root canal morphology and undetected canals and incomplete instrumentation are reasons for root canal treatment failure. The purpose of this study was to determine the best radiographic method for determining root canal morphology in mandibular first premolars in Chinese descendants in Taiwan. Materials and methods: Mandibular first premolars extracted due to caries, periodontal diseases, trauma, or for orthodontic reasons were used. Four indices were examined: (1) root canal bifurcation observed in the buccolingual view; (2) root canal continuity in the buccolingual view; (3) double root outline in the buccolingual view; and (4) Vertucci canal classification in the mesiodistal view. Results: A total of 82 left and right mandibular first premolars were included, a complicated root canal was confirmed in 38 (46.3%) by cross-sectional imaging and a single root canal was found in 44 (53.7%). Bifurcation identified on the mesiodistal view exhibited the highest sensitivity (94.7%) and second highest specificity (88.6%) for identifying a complicated root canal; however, this view is not possible to obtain clinically. Canal bifurcation on the buccolingual view was the most specific (93.2%), but had the lowest sensitivity (73.7%). Canal continuity on the buccolingual view had a sensitivity of 94.7%, and specificity of 70.5%. Conclusion: Combined X-ray analyses, such as performing the buccolingual view for identification of canal bifurcation and canal continuity, may increase the accuracy of identifying complex root canal morphology.

Citation

ID: 244392
Ref Key: sun2016journalthe
Use this key to autocite in SciMatic or Thesis Manager

References

Blockchain Verification

Account:
NFT Contract Address:
0x95644003c57E6F55A65596E3D9Eac6813e3566dA
Article ID:
244392
Unique Identifier:
10.1016/j.jds.2016.01.003
Network:
Scimatic Chain (ID: 481)
Loading...
Blockchain Readiness Checklist
Authors
Abstract
Journal Name
Year
Title
5/5
Creates 1,000,000 NFT tokens for this article
Token Features:
  • ERC-1155 Standard NFT
  • 1 Million Supply per Article
  • Transferable via MetaMask
  • Permanent Blockchain Record
Blockchain QR Code
Scan with Saymatik Web3.0 Wallet

Saymatik Web3.0 Wallet