Literature DB >> 19852592

Automated cephalometry: system performance reliability using landmark-dependent criteria.

Chihiro Tanikawa1, Masakazu Yagi, Kenji Takada.   

Abstract

OBJECTIVE: The purpose of the present study was to evaluate reliability of a system that performs automatic recognition of anatomic landmarks and adjacent structures on lateral cephalograms using landmark-dependent criteria unique to each landmark.
MATERIALS AND METHODS: To evaluate the reliability of the system, the system was used to examine 65 lateral cephalograms. The area of each system-identified anatomic structure surrounding the landmark and the position of each system-identified landmark were compared with norms using confidence ellipses with alpha = .01, which were derived from the scattergrams of 100 estimates obtained according to the method reported by Baumrind and Frantz. When the system-identified area overlapped with the norm area, anatomic structure recognition was considered successful. In addition, when the system-identified point was located within the norm area, landmark identification was considered successful. Based on these judgment criteria, success rates were calculated for all landmarks.
RESULTS: The system successfully identified all specified anatomic structures in all the images and determined the positions of the landmarks with a mean success rate of 88% (range, 77%- 100%).
CONCLUSION: With the incorporation of the rational assessment criteria provided by confidence ellipses, the proposed system was confirmed to be reliable.

Entities:  

Mesh:

Year:  2009        PMID: 19852592     DOI: 10.2319/092908-508R.1

Source DB:  PubMed          Journal:  Angle Orthod        ISSN: 0003-3219            Impact factor:   2.079


  3 in total

Review 1.  Cephalometric Analysis in Orthodontics Using Artificial Intelligence-A Comprehensive Review.

Authors:  Aravind Kumar Subramanian; Yong Chen; Abdullah Almalki; Gautham Sivamurthy; Dashrath Kafle
Journal:  Biomed Res Int       Date:  2022-06-16       Impact factor: 3.246

2.  Automatic recognition of anatomic features on cephalograms of preadolescent children.

Authors:  Chihiro Tanikawa; Taku Yamamoto; Masakazu Yagi; Kenji Takada
Journal:  Angle Orthod       Date:  2010-09       Impact factor: 2.079

Review 3.  Applications of artificial intelligence and machine learning in orthodontics: a scoping review.

Authors:  Yashodhan M Bichu; Ismaeel Hansa; Aditi Y Bichu; Pratik Premjani; Carlos Flores-Mir; Nikhilesh R Vaid
Journal:  Prog Orthod       Date:  2021-07-05       Impact factor: 2.750

  3 in total

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