title: Development of a clinical workflow tool to enhance the detection of vertebral fractures: accuracy and precision evaluation.
abstract: METHODS
  Image analysis model development.
  
  
  OBJECTIVE
  The objective of this study was to develop a novel clinical workflow tool that uses model-based shape recognition technology to allow efficient, semiautomated detailed annotation of each vertebra between T4 and L4 on plain lateral radiographs.
  
  
  BACKGROUND
  Identification of prevalent vertebral fractures, especially when not symptomatic, has been problematic despite their importance. There is a recognized need to increase the opportunities to detect vertebral fractures so that clinically beneficial therapeutic interventions can be initiated.
  
  
  METHODS
  Radiographs obtained from 165 subjects in the Canadian Multicenter Osteoporosis Study (CaMos) were used to construct a vertebral shape model of the vertebral column from T4 to L4 using a statistical learning technique, as well as to estimate the accuracy and precision of this automated software tool for vertebral shape analysis. Radiographs showing scoliosis greater than 15 degrees were excluded.
  
  
  RESULTS
  Vertebral contours defined by 95 points per vertebra, represented by 79,895 points in total, were assessed on 841 individual vertebrae. The mean absolute accuracy error calculated over each vertebra in each test image was 1.06 +/- 1.2 mm. This value corresponded to an average 3.4% of vertebral height. The mean precision error, reflecting interobserver variability, per vertebra of the resulting annotations was 0.61 +/- 0.73 mm. This value corresponded to an average 2.3% of vertebral height. Accuracy and precision error estimates did not differ notably by vertebral level.
  
  
  CONCLUSIONS
  The results of the current study indicate that statistical modeling can provide a robust tool for the accurate and precise semiautomated annotation of vertebral body shape from T4 to L4 in patients who do not have scoliosis greater than 15 degrees . This method may prove useful as a clinical workflow tool to aid the physician in vertebral fracture assessment and might contribute to decision-making about pharmacologic treatment of osteoporosis.
authors.affiliation: Optasia Medical Ltd, Cheadle, Cheshire, United Kingdom.
authors.forename: Alan
authors.initials: A
authors.lastname: Brett
authors.forename: Colin G
authors.initials: CG
authors.lastname: Miller
authors.forename: Curtis W
authors.initials: CW
authors.lastname: Hayes
authors.forename: Joel
authors.initials: J
authors.lastname: Krasnow
authors.forename: Takouhi
authors.initials: T
authors.lastname: Ozanian
authors.forename: Ken
authors.initials: K
authors.lastname: Abrams
authors.forename: Jon E
authors.initials: JE
authors.lastname: Block
authors.forename: Cornelis
authors.initials: C
authors.lastname: van Kuijk
mesh_terms: D001185:Artificial Intelligence; D003198:Computer Simulation; D003661:Decision Support Techniques; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008159:Lumbar Vertebrae / Q000000981:diagnostic imaging / Q000473:pathology; D015233:Models, Statistical; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods*; D011237:Predictive Value of Tests; D011859:Radiography / Q000379:methods*; D012680:Sensitivity and Specificity; D012984:Software; D016103:Spinal Fractures / Q000000981:diagnostic imaging* / Q000473:pathology; D013904:Thoracic Vertebrae / Q000000981:diagnostic imaging / Q000473:pathology; D057188:Workflow