Literature DB >> 20578850

Automatic recognition of anatomic features on cephalograms of preadolescent children.

Chihiro Tanikawa1, Taku Yamamoto, Masakazu Yagi, Kenji Takada.   

Abstract

OBJECTIVE: To develop a system that automatically recognizes the dentoskeletal traits on cephalograms recorded for preadolescent children and to examine performance reliability.
MATERIALS AND METHODS: We obtained 859 lateral cephalograms and divided them into group P (400 films taken from orthodontic patients having permanent dentition) and group M (459 films taken from those having mixed dentition). Fifty-nine cephalograms in group M were reserved for system test, and the remaining cephalograms in groups M and P were used for system development. Using a previously reported method (Yagi and Shibata, 2003), systems S(M) and S(P+M) were developed with the knowledge generated from groups M and P+M (combined sample of groups M and P), respectively. The system S(P) that had been developed for cephalograms of permanent dentition in our previous report was also employed for comparison. To evaluate performance reliability, the systems examined the 59 reserved cephalograms. The areas of each system-identified anatomic structure surrounding the anatomic landmarks and the positions of each system-identified landmark were compared with the norms in the form of confidence ellipses. The success rates were calculated for S(P), S(M), and S(P+M).
RESULTS: The systems successfully identified all of the specified anatomic structures in all of the images. The systems S(P), S(M), and S(P+M) determined the landmark positions with a mean success rate of 69% (range, 38-98%), 82% (range, 50-100%), and 82% (range, 58-100%), respectively.
CONCLUSIONS: Systems S(M) and S(P+M) were confirmed to be accurate and reliable in recognizing the anatomic features on the cephalograms of preadolescent children.

Entities:  

Mesh:

Year:  2010        PMID: 20578850      PMCID: PMC8939022          DOI: 10.2319/092909-474.1

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


  6 in total

1.  The Saskatchewan Pediatric Bone Mineral Accrual Study: bone mineral acquisition during the growing years.

Authors:  D A Bailey
Journal:  Int J Sports Med       Date:  1997-07       Impact factor: 3.118

2.  Validity and reliability of a new edge-based computerized method for identification of cephalometric landmarks.

Authors:  Serge Kazandjian; Stavros Kiliaridis; Anestis Mavropoulos
Journal:  Angle Orthod       Date:  2006-07       Impact factor: 2.079

3.  An image representation algorithm compatible with neural-associative-processor-based hardware recognition systems.

Authors:  M Yagi; T Shibata
Journal:  IEEE Trans Neural Netw       Date:  2003

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

Authors:  Chihiro Tanikawa; Masakazu Yagi; Kenji Takada
Journal:  Angle Orthod       Date:  2009-11       Impact factor: 2.079

5.  Variability of cephalometric landmarks used for face growth studies.

Authors:  T Sekiguchi; B S Savara
Journal:  Am J Orthod       Date:  1972-06

6.  A study of roentgenocephalometric bony landmarks.

Authors:  F P van der Linden
Journal:  Am J Orthod       Date:  1971-02
  6 in total
  2 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

Review 2.  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

  2 in total

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