title: Automatic recognition of anatomic features on cephalograms of preadolescent children.
abstract: OBJECTIVE
  To develop a system that automatically recognizes the dentoskeletal traits on cephalograms recorded for preadolescent children and to examine performance reliability.
  
  
  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.
authors.affiliation: Department of Orthodontics and Dentofacial Orthopedics, Osaka University, Osaka, Japan.
authors.forename: Chihiro
authors.initials: C
authors.lastname: Tanikawa
authors.forename: Taku
authors.initials: T
authors.lastname: Yamamoto
authors.forename: Masakazu
authors.initials: M
authors.lastname: Yagi
authors.forename: Kenji
authors.initials: K
authors.lastname: Takada
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D002508:Cephalometry; D002648:Child; D002675:Child, Preschool; D002680:Chin / Q000033:anatomy & histology; D003818:Dentition, Mixed; D019229:Dentition, Permanent; D004424:Ear Canal / Q000033:anatomy & histology; D005147:Facial Bones / Q000033:anatomy & histology*; D005260:Female; D006801:Humans; D007180:Incisor / Q000033:anatomy & histology; D051188:Knowledge Bases; D008297:Male; D008334:Mandible / Q000033:anatomy & histology; D008437:Maxilla / Q000033:anatomy & histology; D008875:Middle Aged; D009295:Nasal Bone / Q000033:anatomy & histology; D016571:Neural Networks, Computer; D010159:Palate / Q000033:anatomy & histology; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012658:Sella Turcica / Q000033:anatomy & histology; D019291:Skull Base / Q000033:anatomy & histology; D055815:Young Adult