title: Compression fracture diagnosis in lumbar: a clinical CAD system.
abstract: OBJECTIVE
  Lower back pain affects 80-90 % of all people at some point during their life time, and it is considered as the second most neurological ailment after headache. It is caused by defects in the discs, vertebrae, or the soft tissues. Radiologists perform diagnosis mainly from X-ray radiographs, MRI, or CT depending on the target organ. Vertebra fracture is usually diagnosed from X-ray radiographs or CT depending on the available technology. In this paper, we propose a fully automated Computer-Aided Diagnosis System (CAD) for the diagnosis of vertebra wedge compression fracture from CT images that integrates within the clinical routine.
  
  
  METHODS
  We perform vertebrae localization and labeling, segment the vertebrae, and then diagnose each vertebra. We perform labeling and segmentation via coordinated system that consists of an Active Shape Model and a Gradient Vector Flow Active Contours (GVF-Snake). We propose a set of clinically motivated features that distinguish the fractured vertebra. We provide two machine learning solutions that utilize our features including a supervised learner (Neural Networks (NN)) and an unsupervised learner (K-Means).
  
  
  RESULTS
  We validate our method on a set of fifty (thirty abnormal) Computed Tomography (CT) cases obtained from our collaborating radiology center. Our diagnosis detection accuracy using NN is 93.2 % on average while we obtained 98 % diagnosis accuracy using K-Means. Our K-Means resulted in a specificity of 87.5 % and sensitivity over 99 %.
  
  
  CONCLUSIONS
  We presented a fully automated CAD system that seamlessly integrates within the clinical work flow of the radiologist. Our clinically motivated features resulted in a great performance of both the supervised and unsupervised learners that we utilize to validate our CAD system. Our CAD system results are promising to serve in clinical applications after extensive validation.
authors.affiliation: University of Jordan, Amman 11942, Jordan.
authors.forename: Samah
authors.initials: S
authors.lastname: Al-Helo
authors.forename: Raja S
authors.initials: RS
authors.lastname: Alomari
authors.forename: Subarna
authors.initials: S
authors.lastname: Ghosh
authors.forename: Vipin
authors.initials: V
authors.lastname: Chaudhary
authors.forename: Gurmeet
authors.initials: G
authors.lastname: Dhillon
authors.forename: Moh'd B
authors.initials: MB
authors.lastname: Al-Zoubi
authors.forename: Hazem
authors.initials: H
authors.lastname: Hiary
authors.forename: Thair M
authors.initials: TM
authors.lastname: Hamtini
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D015331:Cohort Studies; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation*; D050815:Fractures, Compression / Q000175:diagnosis*; D006801:Humans; D008159:Lumbar Vertebrae / Q000293:injuries*; D012680:Sensitivity and Specificity; D016103:Spinal Fractures / Q000175:diagnosis*; D014057:Tomography, X-Ray Computed