Skip to main content
Journal cover image

Predictive model for the diagnosis of intraabdominal abscess.

Publication ,  Journal Article
Freed, KS; Lo, JY; Baker, JA; Floyd, CE; Low, VH; Seabourn, JT; Nelson, RC
Published in: Acad Radiol
July 1998

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.

Duke Scholars

Altmetric Attention Stats
Dimensions Citation Stats

Published In

Acad Radiol

DOI

ISSN

1076-6332

Publication Date

July 1998

Volume

5

Issue

7

Start / End Page

473 / 479

Location

United States

Related Subject Headings

  • Tomography, X-Ray Computed
  • Radiographic Image Interpretation, Computer-Assisted
  • ROC Curve
  • Predictive Value of Tests
  • Nuclear Medicine & Medical Imaging
  • Neural Networks, Computer
  • Middle Aged
  • Male
  • Humans
  • Female
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Freed, K. S., Lo, J. Y., Baker, J. A., Floyd, C. E., Low, V. H., Seabourn, J. T., & Nelson, R. C. (1998). Predictive model for the diagnosis of intraabdominal abscess. Acad Radiol, 5(7), 473–479. https://doi.org/10.1016/s1076-6332(98)80187-6
Freed, K. S., J. Y. Lo, J. A. Baker, C. E. Floyd, V. H. Low, J. T. Seabourn, and R. C. Nelson. “Predictive model for the diagnosis of intraabdominal abscess.Acad Radiol 5, no. 7 (July 1998): 473–79. https://doi.org/10.1016/s1076-6332(98)80187-6.
Freed KS, Lo JY, Baker JA, Floyd CE, Low VH, Seabourn JT, et al. Predictive model for the diagnosis of intraabdominal abscess. Acad Radiol. 1998 Jul;5(7):473–9.
Freed, K. S., et al. “Predictive model for the diagnosis of intraabdominal abscess.Acad Radiol, vol. 5, no. 7, July 1998, pp. 473–79. Pubmed, doi:10.1016/s1076-6332(98)80187-6.
Freed KS, Lo JY, Baker JA, Floyd CE, Low VH, Seabourn JT, Nelson RC. Predictive model for the diagnosis of intraabdominal abscess. Acad Radiol. 1998 Jul;5(7):473–479.
Journal cover image

Published In

Acad Radiol

DOI

ISSN

1076-6332

Publication Date

July 1998

Volume

5

Issue

7

Start / End Page

473 / 479

Location

United States

Related Subject Headings

  • Tomography, X-Ray Computed
  • Radiographic Image Interpretation, Computer-Assisted
  • ROC Curve
  • Predictive Value of Tests
  • Nuclear Medicine & Medical Imaging
  • Neural Networks, Computer
  • Middle Aged
  • Male
  • Humans
  • Female