title: New approach for the diagnosis of extractions with neural network machine learning.
abstract: BACKGROUND
  The decision to extract teeth for orthodontic treatment is important and difficult because it tends to be based on the practitioner's experiences. The purposes of this study were to construct an artificial intelligence expert system for the diagnosis of extractions using neural network machine learning and to evaluate the performance of this model.
  
  
  METHODS
  The subjects included 156 patients. Input data consisted of 12 cephalometric variables and an additional 6 indexes. Output data consisted of 3 bits to divide the extraction patterns. Four neural network machine learning models for the diagnosis of extractions were constructed using a back-propagation algorithm and were evaluated.
  
  
  RESULTS
  The success rates of the models were 93% for the diagnosis of extraction vs nonextraction and 84% for the detailed diagnosis of the extraction patterns.
  
  
  CONCLUSIONS
  This study suggests that artificial intelligence expert systems with neural network machine learning could be useful in orthodontics. Improved performance was achieved by components such as proper selection of the input data, appropriate organization of the modeling, and preferable generalization.
authors.affiliation: Clinical instructor, Department of Orthodontics, Korea University Ansan Hospital, Ansan, Korea.
authors.forename: Seok-Ki
authors.initials: SK
authors.lastname: Jung
authors.affiliation: Professor, Department of Orthodontics, School of Dentistry, Dental Research Institute, Seoul National University, Seoul, Korea. Electronic address: taewoo@snu.ac.kr.
authors.forename: Tae-Woo
authors.initials: TW
authors.lastname: Kim
mesh_terms: D000328:Adult; D000465:Algorithms; D059925:Anatomic Landmarks / Q000473:pathology; D001185:Artificial Intelligence; D002508:Cephalometry / Q000379:methods; D003658:Decision Making, Computer-Assisted; D005103:Expert Systems; D005260:Female; D006801:Humans; D007180:Incisor / Q000473:pathology; D008046:Lip / Q000473:pathology; D000069550:Machine Learning; D008297:Male; D008334:Mandible / Q000473:pathology; D008437:Maxilla / Q000473:pathology; D009295:Nasal Bone / Q000473:pathology; D016571:Neural Networks, Computer; D009666:Nose / Q000473:pathology; D009971:Orthodontics, Corrective; D010347:Patient Care Planning; D014081:Tooth Extraction; D055815:Young Adult