title: Quantitative assessment of new MRI-based measurements to differentiate low and high stages of pelvic organ prolapse using support vector machines.
abstract: OBJECTIVE
  The objective of this study was to quantitatively assess the ability of new MRI-based measurements to differentiate low and high stages of pelvic organ prolapse. New measurements representing pelvic structural characteristics are proposed and analyzed using support vector machines (SVM).
  
  
  METHODS
  This retrospective study used data from 207 women with different types and stages of prolapse. Their demographic information, clinical history, and dynamic MRI data were obtained from the database. New MRI measurements were extracted and analyzed based on these reference lines: pubococcygeal line (PCL), mid-pubic line (MPL), true conjugate line (TCL), obstetric conjugate line (OCL), and diagonal conjugate line (DCL). A classification model using SVM was designed to assess the impact of the features (variables) in classifying prolapse into low or high stage.
  
  
  RESULTS
  The classification model using SVM can accurately identified anterior prolapse with very high accuracy (>0.90), and apical and posterior prolapse with good accuracy (0.80 - 0.90). Two newly proposed MRI-based features were found to be significant in the identification of anterior and posterior prolapse: the angle between TCL and MPL for anterior prolapse, and the angle between DCL and PCL for posterior prolapse. The overall accuracy of posterior prolapse identification increased from 47% to 80% when the newly proposed MRI-based features were taken into consideration.
  
  
  CONCLUSIONS
  The proposed MRI-based measurements are effective in differentiating low and high stages of pelvic organ prolapse, particularly for posterior prolapse.
authors.affiliation: Department of Mechanical and Industrial Engineering, Southern Illinois University-Edwardsville, Edwardsville, IL, USA.
authors.forename: S
authors.initials: S
authors.lastname: Onal
authors.forename: S
authors.initials: S
authors.lastname: Lai-Yuen
authors.forename: P
authors.initials: P
authors.lastname: Bao
authors.forename: A
authors.initials: A
authors.lastname: Weitzenfeld
authors.forename: D
authors.initials: D
authors.lastname: Hogue
authors.forename: S
authors.initials: S
authors.lastname: Hart
mesh_terms: D000328:Adult; D000368:Aged; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging; D008875:Middle Aged; D008962:Models, Theoretical; D056887:Pelvic Organ Prolapse / Q000145:classification* / Q000175:diagnosis*; D012189:Retrospective Studies; D012720:Severity of Illness Index; D060388:Support Vector Machine