Maxillofacial Plastic and Reconstructive Surgery

  • 제37권
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  • Pages.30.1-30.7
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  • 2015
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  • 2288-8101(pISSN)
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  • 2288-8586(eISSN)
대한악안면성형재건외과학회 (Korean Association of Maxillofacial Plastic and Reconstructive Surgeons)
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Assessment of the proximity between the mandibular third molar and inferior alveolar canal using preoperative 3D-CT to prevent inferior alveolar nerve damage

  • Lee, Byeongmin (Department of Oral and Maxillofacial Surgery, Kangnam Sacred Heart Hospital, College of Medicine, Hallym University) ;
  • Park, Youngju (Department of Oral and Maxillofacial Surgery, Kangnam Sacred Heart Hospital, College of Medicine, Hallym University) ;
  • Ahn, Janghoon (Department of Orthodontics, Kangnam Sacred Heart Hospital, College of Medicine, Hallym University) ;
  • Chun, Jihyun (Department of Oral and Maxillofacial Surgery, Kangnam Sacred Heart Hospital, College of Medicine, Hallym University) ;
  • Park, Suhyun (Department of Oral and Maxillofacial Surgery, Kangnam Sacred Heart Hospital, College of Medicine, Hallym University) ;
  • Kim, Minjin (Department of Oral and Maxillofacial Surgery, Kangnam Sacred Heart Hospital, College of Medicine, Hallym University) ;
  • Jo, Youngserk (Department of Oral and Maxillofacial Surgery, Kangnam Sacred Heart Hospital, College of Medicine, Hallym University) ;
  • Ahn, Somi (Department of Oral and Maxillofacial Surgery, Kangnam Sacred Heart Hospital, College of Medicine, Hallym University) ;
  • Kim, Beulha (Department of Oral and Maxillofacial Surgery, Kangnam Sacred Heart Hospital, College of Medicine, Hallym University) ;
  • Choi, Sungbae (Department of Oral and Maxillofacial Surgery, Kangnam Sacred Heart Hospital, College of Medicine, Hallym University)
  • 투고 : 2015.08.26
  • 심사 : 2015.09.01
  • 발행 : 2015.12.31
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초록

Background: The inferior alveolar nerve (IAN) may be injured during extraction of the mandibular third molar, causing severe postoperative complications. Many methods have been described for evaluating the relative position between the mandibular third molar and the inferior alveolar canal (IAC) on panoramic radiography and computed tomography, but conventional radiography provides limited information on the proximity of these two structures. The present study assessed the benefits of three-dimensional computed tomography (3D-CT) prior to surgical extraction of the mandibular third molar, to prevent IAN damage. Methods: This retrospective study included 4917 extractions in 3555 patients who presented for extraction of the mandibular third molars. The cases were classified into three groups, according to anatomical relationship between the mandibular third molars and the IAC on panoramic radiography and whether 3D-CT was performed. Symptoms of IAN damage were assessed using the touch-recognition test. Data were compared using the chi-square test and Fisher's exact test. Results: Among the 32 cases of IAN damage, 6 cases were included in group I (0.35 %, n = 1735 cases), 23 cases in group II (1.1 %, n = 2063 cases), and 3 cases in group III (0.27 %, n = 1119 cases). The chi-square test showed a significant difference in the incidence of IAN damage between groups I and II. No significant difference was observed between groups I and III using Fisher's exact test. In the 6 cases of IAN damage in group I, the mandibular third molar roots were located lingual relative to the IAC in 3 cases and middle relative to the IAC in 3 cases. The overlap was ${\geq}2mm$ in 3 of 6 cases and 0-2 mm in the remaining 3 cases. The mean distance between the mandibular third molar and IAC was 2.2 mm, the maximum distance 12 mm, and the minimum distance 0.5 mm. Greater than 80 % recovery was observed in 15 of 32 (46.8 %) cases of IAN damage. Conclusions: 3D-CT may be a useful tool for assessing the three-dimensional anatomical relationship and proximity between the mandibular third molar and IAC in order to prevent IAN damage during extraction of mandibular third molars.

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참고문헌

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피인용 문헌

  1. Сross-sectional anatomic study of direct positional relationships between mandibular canal and roots of posterior teeth using cone beam computed tomography. vol.7, pp.8, 2018, https://doi.org/10.17126/joralres.2018.079
  2. Deep semi-supervised learning for automatic segmentation of inferior alveolar nerve using a convolutional neural network vol.21, pp.1, 2015, https://doi.org/10.1186/s12903-021-01983-5