The potential for facial artery injury during mandibular third molar extraction. An anatomical study using contrast-enhanced computed tomography.
Yohei TakeshitaSoichiro IbaragiHirokazu YutoriJingo KusukawaRichard Shane TubbsToshiyuki KawazuJunichi AsaumiJoe IwanagaPublished in: Clinical anatomy (New York, N.Y.) (2021)
The purpose of this study was to evaluate the risk of injury to the facial (FA) and related arteries during mandibular third molar (MTM) extraction using contrast-enhanced computed tomography (CE-CT). CE-CT images of the MTM region were retrospectively reviewed. The area of the MTM was equally divided into three zones in the coronal images from mesial to distal, that is, zone 1, zone 2, and zone 3. The FA, submental artery (SMA), and sublingual artery (SLA) were identified. The distance from the mandible to FA, SMA, and SLA and the diameter of the FA, SMA, and SLA was measured in three zones, respectively. The thickness of the facial soft tissues and width of the mandible were measured at their maximum. The mean distance from the FA to the buccal cortical bone in zone 1, zone 2 and zone 3 was 2.24 mm, 2.39 mm and 1.67 mm, respectively. The SMA and SLA were found to be distal to the mandible. The mean diameter of the FA was 1.26 mm in males and 1.04 mm in females, respectively (p < 0.0001). The distance between the FA and buccal cortical bone of the mandible, and the patients' weight showed moderate correlation in zones 1 and 2. Based on our findings, the FA can be damaged if the surgical invasion reaches the facial soft tissues during MTM surgery. The patients' weight might be a good predictor for FA injury when CE-CT is not available.
Keyphrases
- contrast enhanced
- computed tomography
- magnetic resonance imaging
- diffusion weighted
- dual energy
- magnetic resonance
- positron emission tomography
- end stage renal disease
- diffusion weighted imaging
- soft tissue
- minimally invasive
- newly diagnosed
- ejection fraction
- optical coherence tomography
- physical activity
- peritoneal dialysis
- prognostic factors
- deep learning
- body mass index
- bone mineral density
- gene expression
- chronic kidney disease
- weight loss
- postmenopausal women
- coronary artery disease
- acute coronary syndrome
- machine learning
- patient reported outcomes
- energy transfer