Document Detail

Predictive model for the diagnosis of intraabdominal abscess.
MedLine Citation:
PMID:  9653463     Owner:  NLM     Status:  MEDLINE    
RATIONALE AND OBJECTIVES: The authors investigated the use of an artificial neural network (ANN) to aid in the diagnosis of intraabdominal abscess. MATERIALS AND METHODS: An ANN was constructed based on data from 140 patients who underwent abdominal and pelvic computed tomography (CT) between January and December 1995. Input nodes included data from clinical history, physical examination, laboratory investigation, and radiographic study. The ANN was trained and tested on data from all 140 cases by using a round-robin method and was compared with linear discriminate analysis. A receiver operating characteristic curve was generated to evaluate both predictive models. RESULTS: CT examinations in 50 cases were positive for abscess. This finding was confirmed by means of laboratory culture of aspirations from CT-guided percutaneous drainage in 38 patients, ultrasound-guided percutaneous drainage in five patients, surgery in five patients, and characteristic appearance on CT scans without aspiration in two patients. CT scans in 90 cases were negative for abscess. The sensitivity and specificity of the ANN in predicting the presence of intraabdominal abscess were 90% and 51%, respectively. Receiver operating characteristic analysis showed no statistically significant difference in performance between the two predictive models. CONCLUSION: The ANN is a useful tool for determining whether an intraabdominal abscess is present. It can be used to set priorities for CT examinations in order to expedite treatment in patients believed to be more likely to have an abscess.
K S Freed; J Y Lo; J A Baker; C E Floyd; V H Low; J T Seabourn; R C Nelson
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Publication Detail:
Type:  Comparative Study; Journal Article    
Journal Detail:
Title:  Academic radiology     Volume:  5     ISSN:  1076-6332     ISO Abbreviation:  Acad Radiol     Publication Date:  1998 Jul 
Date Detail:
Created Date:  1998-09-15     Completed Date:  1998-09-15     Revised Date:  2006-11-15    
Medline Journal Info:
Nlm Unique ID:  9440159     Medline TA:  Acad Radiol     Country:  UNITED STATES    
Other Details:
Languages:  eng     Pagination:  473-9     Citation Subset:  IM    
Department of Radiology, Duke University Medical Center, Durham, NC 27705, USA.
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MeSH Terms
Abdominal Abscess / radiography*
Aged, 80 and over
Computer Simulation*
Middle Aged
Neural Networks (Computer)*
Predictive Value of Tests
ROC Curve
Radiographic Image Interpretation, Computer-Assisted / methods*
Tomography, X-Ray Computed

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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