title: Semiautomated digital analysis of knee joint space width using MR images.
abstract: OBJECTIVE
  The goal of this study was to (a) develop a semiautomated computer algorithm to measure knee joint space width (JSW) from magnetic resonance (MR) images using standard imaging techniques and (b) evaluate the reproducibility of the algorithm.
  
  
  METHODS
  Using a standard clinical imaging protocol, bilateral knee MR images were obtained twice within a 2-week period from 17 asymptomatic research participants. Images were analyzed to determine the variability of the measurements performed by the program compared with the variability of manual measurements.
  
  
  RESULTS
  Measurement variability of the computer algorithm was considerably smaller than the variability of manual measurements. The average difference between two measurements of the same slice performed with the computer algorithm by the same user was 0.004 +/- 0.07 mm for the tibiofemoral joint (TF) and 0.009 +/- 0.11 mm for the patellofemoral joint (PF) compared with an average of 0.12 +/- 0.22 mm TF and 0.13 +/- 0.29 mm PF, respectively, for the manual method. Interuser variability of the computer algorithm was also considerably smaller, with an average difference of 0.004 +/- 0.1 mm TF and 0.0006 +/- 0.1 mm PF compared with 0.38 +/- 0.59 mm TF and 0.31 +/- 0.66 mm PF obtained using a manual method. The between-day reproducibility was larger but still within acceptable limits at 0.09 +/- 0.39 mm TF and 0.09 +/- 0.51 mm PF. This technique has proven consistently reproducible on a same slice base,while the reproducibility comparing different acquisitions of the same subject was larger. Longitudinal reproducibility improvement needs to be addressed through acquisition protocol improvements.
  
  
  CONCLUSIONS
  A semiautomated method for measuring knee JSW from MR images has been successfully developed.
authors.affiliation: Motion Analysis Laboratory, Division of Orthopedic Research, Mayo Clinic, Rochester, MN 55905, USA.
authors.forename: Filippo
authors.initials: F
authors.lastname: Agnesi
authors.forename: Kimberly K
authors.initials: KK
authors.lastname: Amrami
authors.forename: Carlo A
authors.initials: CA
authors.lastname: Frigo
authors.forename: Kenton R
authors.initials: KR
authors.lastname: Kaufman
mesh_terms: D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007593:Joint Instability / Q000175:diagnosis; D007719:Knee Joint / Q000033:anatomy & histology*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted