title: Automated CT detection of intestinal abnormalities and ischemia for decision making in emergency medicine.
abstract: BACKGROUND
  Evaluation of computed tomography (CT) for the diagnosis of intestinal wall abnormalities and ischemia is important for clinical decision making in patients with acute abdominal pain to which if surgery should be performed in the emergency department. Interpretation of such information on CT is usually based on visual assessment by medical professionals and still remains a challenge in a variety of settings of the medical emergency care. This paper reports a pilot study in the implementation of image processing methods for automated detection of intestinal wall abnormalities and bowel ischemia, which can be of a potential application for CT-based detection of the intestinal disease.
  
  
  METHODS
  CT scans of 3 patients of ischemia, one benign and one control subjects were used in this study. Statistical and geometrical features of the CT scans were extracted for pattern classification using two distance measures and the k-nearest neighbor algorithm. The automated detection of intestinal abnormalities and ischemia was carried out using labeled data from the training process with various proportions of training and testing samples to validate the results.
  
  
  RESULTS
  Detection rates of intestinal ischemia and abnormalities are promising in terms of sensitivity and specificity, where the sensitivity is higher than the specificity in all test cases. The overall classification accuracy between the diseased and control subjects can be as high as 100% when all CT scans were included for measuring the difference between a cohort of three patients of ischemia and a single control subject.
  
  
  CONCLUSIONS
  The proposed approach can be utilized as a computer-aided tool for decision making in the emergency department, where the availability of expert knowledge of the radiologist and surgeon about this complex bowel disease is limited.
authors: Taichiro
authors: T
authors: Tsunoyama
authors: Tuan D
authors: TD
authors: Pham
authors: Takashi
authors: T
authors: Fujita
authors: Tetsuya
authors: T
authors: Sakamoto
mesh_terms: D015746:Abdominal Pain / Q000000981:diagnostic imaging / Q000601:surgery; D000293:Adolescent; D000368:Aged; D000369:Aged, 80 and over; D001331:Automation; D003657:Decision Making; D004635:Emergency Medicine; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007422:Intestines / Q000098:blood supply* / Q000000981:diagnostic imaging* / Q000601:surgery; D007511:Ischemia / Q000000981:diagnostic imaging*; D008297:Male; D010363:Pattern Recognition, Automated; D010865:Pilot Projects; D014057:Tomography, X-Ray Computed