Literature DB >> 31476533

Determining the risk relationship associated with inferior alveolar nerve injury following removal of mandibular third molar teeth: A systematic review.

F Kang1, M K Sah2, G Fei2.   

Abstract

PURPOSE: This study analyzes the risk factors associated with the incidences of inferior alveolar nerve (IAN) injury after surgical removal of impacted mandibular third molar (IMTM) and to evaluate the contribution of these risk factors to postoperative neurosensory deficits.
MATERIALS AND METHODS: An exhaustive literature search has been carried out in the COCHRANE library and PubMed electronic databases from January 1990 to March 2019 supplemented by manual searching to identify the related studies. Twenty-three studies out of 693 articles from the initial search were finally included, which summed up a total of 26,427 patients (44,171 teeth).
RESULTS: Our results have been compared with other current available papers in the literature review that obtained similar outcomes. Among 44,171 IMTM extractions performed by various grades of operators, 1.20% developed transient IAN deficit and 0.28% developed permanent IAN deficit respectively. Depth of impaction (P<0.001), contact between mandibular canal (MC) and IMTM (P<0.001), surgical technique (P<0.001), intra-operative nerve exposure (P<0.001), and surgeon's experience (P<0.001) were statistically significant as contributing risk factors of IAN deficits.
CONCLUSION: Radiographic findings, such as depth of impaction, proximity of the tooth to the mandibular canal, surgical technique, intra-operative nerve exposure, and surgeon's experience were high risk factors of IAN deficit after surgical removal of IMTMs.
Copyright © 2019 Elsevier Masson SAS. All rights reserved.

Entities:  

Keywords:  Impacted mandibular third molar; Inferior alveolar nerve; Mandibular canal; Neurosensory deficit

Year:  2019        PMID: 31476533     DOI: 10.1016/j.jormas.2019.06.010

Source DB:  PubMed          Journal:  J Stomatol Oral Maxillofac Surg        ISSN: 2468-7855            Impact factor:   1.569


  2 in total

1.  Evaluation of multi-task learning in deep learning-based positioning classification of mandibular third molars.

Authors:  Shintaro Sukegawa; Tamamo Matsuyama; Futa Tanaka; Takeshi Hara; Kazumasa Yoshii; Katsusuke Yamashita; Keisuke Nakano; Kiyofumi Takabatake; Hotaka Kawai; Hitoshi Nagatsuka; Yoshihiko Furuki
Journal:  Sci Rep       Date:  2022-01-13       Impact factor: 4.379

2.  A Fused Deep Learning Architecture for the Detection of the Relationship between the Mandibular Third Molar and the Mandibular Canal.

Authors:  Cansu Buyuk; Nurullah Akkaya; Belde Arsan; Gurkan Unsal; Secil Aksoy; Kaan Orhan
Journal:  Diagnostics (Basel)       Date:  2022-08-20
  2 in total

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