title: Prediction of forearm bone shape based on partial least squares regression from partial shape.
abstract: BACKGROUND
  Computer-assisted corrective osteotomy using a mirror image of the normal contralateral shape as reference is increasingly used. Instead, we propose to use the shape predicted by statistical learning to deal with cases demonstrating bilateral abnormality, such as bilateral trauma, congenital disease, and metabolic disease.
  
  
  METHODS
  Computed tomography (CT) scans of 100 normal forearms were used in this study. The whole bone shape was predicted from its partial shape based on statistical learning of the other 99 bones. Accuracy was evaluated by average symmetric surface distance (ASD), and translational and rotational errors.
  
  
  RESULTS
  ASDs for predicted shapes were 0.71-1.03 mm. Mean absolute translational and rotational errors were 0.48-1.76 mm and 0.99-6.08°, respectively.
  
  
  CONCLUSIONS
  Normal bone shape was predicted with an acceptable accuracy from its partial shape using statistical learning. Predicted shape can be an alternative to a mirror image, which may enable reduced radiation exposure and examination costs.
authors.affiliation: Department of Orthopaedic Surgery, Japan Community Health care Organization Osaka Hospital, Osaka, Japan.|Department of Orthopaedic Surgery, Osaka University Graduate School of Medicine, Osaka, Japan.|Graduate School of Information Science, Nara Institute of Science and Technology (NAIST), Nara, Japan.
authors.forename: Keiichiro
authors.initials: K
authors.lastname: Oura
authors.affiliation: Graduate School of Information Science, Nara Institute of Science and Technology (NAIST), Nara, Japan.
authors.forename: Yoshito
authors.initials: Y
authors.lastname: Otake
authors.affiliation: Department of Orthopaedic Surgery, Osaka University Graduate School of Medicine, Osaka, Japan.
authors.forename: Atsuo
authors.initials: A
authors.lastname: Shigi
authors.affiliation: Graduate School of Information Science, Nara Institute of Science and Technology (NAIST), Nara, Japan.
authors.forename: Futoshi
authors.initials: F
authors.lastname: Yokota
authors.affiliation: Department of Orthopaedic Surgery, Osaka University Graduate School of Medicine, Osaka, Japan.
authors.forename: Tsuyoshi
authors.initials: T
authors.lastname: Murase
authors.affiliation: Graduate School of Information Science, Nara Institute of Science and Technology (NAIST), Nara, Japan.
authors.forename: Yoshinobu
authors.initials: Y
authors.lastname: Sato
mesh_terms: D003198:Computer Simulation; D005542:Forearm / Q000033:anatomy & histology* / Q000000981:diagnostic imaging / Q000601:surgery; D006801:Humans; D021621:Imaging, Three-Dimensional; D016018:Least-Squares Analysis; D000069550:Machine Learning; D008953:Models, Anatomic; D010027:Osteotomy / Q000379:methods; D011884:Radius / Q000033:anatomy & histology / Q000000981:diagnostic imaging / Q000601:surgery; D025321:Surgery, Computer-Assisted / Q000379:methods; D014057:Tomography, X-Ray Computed; D014457:Ulna / Q000033:anatomy & histology / Q000000981:diagnostic imaging / Q000601:surgery