Mandibular Landmarks in Dentistry and Radiology

Mandibular Landmarks

Mandibular landmarks are important anatomical reference points used in various fields, including dentistry, radiology, and facial growth studies. These landmarks provide a stable framework for measuring changes and evaluating the development of the mandible over time.

Stable Mandibular Landmarks

A study by Chen et al. identified the most stable mandibular landmarks in growing patients using three-dimensional cone-beam computed tomography (CBCT) scans. The landmarks include:

• Point C: The internal symphysis
• Points D and E: Two additional landmarks on the inferior part of the internal symphysis
• Pog: The chin
• Mental foramina (MF) and Mandibular foramina (MFA): The openings of the mental and mandibular canals

The study found that the total distance changes between Point C and these landmarks were all less than 1.0 mm from T1 to T2, indicating stability through time.

Landmark Visibility

The visibility of mandibular landmarks on panoramic radiographs and CBCT scan images varies. A study by the authors evaluated the visibility of anatomical landmarks on panoramic images and CBCT scan images. The findings were as follows:

• Panoramic radiographs: The mandibular canal could be observed in 92.5% of cases, with good visibility in 12.0%. The mental foramen could be observed in 44.5% of cases, with no cases having good visibility. Anterior looping of the mental nerve was present in 16.5% of cases, but no cases had good visibility.
• CBCT scan images: The mandibular canal, the mental foramen, and the lingual foramen could be observed in 100% of cases, with good visibility in 51.0%, 98.5%, and 45.0% of cases, respectively. Anterior looping of the mental nerve was present in 26% of cases, with 2% having good visibility. An incisive canal could be identified in 49.5% of the cases, with only 7.5% showing good visibility.

Automated Localization of Mandibular Landmarks

Deep learning has been used to segment the mandible and identify three-dimensional (3D) anatomical landmarks from cone-beam computed tomography (CBCT) images. The PointRend deep learning mechanism can segment the mandible in 10 ± 1.5 seconds with a Dice similarity coefficient of 0.98. This study also found that the most accurate mandibular median sagittal plane (MMSP) was the B-Gn-F plane, which involves the supra-mentale, gnathion, and mandibular foramen.

Conclusion

Mandibular landmarks play a crucial role in understanding the development and changes in the mandible over time. With advancements in technology and techniques, the identification and localization of these landmarks have become more accurate and efficient, providing valuable insights for various applications in healthcare and research.