Predictive model for the diagnosis of intraabdominal abscess.
OBJECTIVE
The authors investigated the use of an artificial neural network (ANN) to aid in the diagnosis of intraabdominal abscess.


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.


CONCLUSIONS
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.
Freed
K S
KS
Department of Radiology, Duke University Medical Center, Durham, NC 27705, USA.
Lo
J Y
JY
Baker
J A
JA
Floyd
C E
CE
Low
V H
VH
Seabourn
J T
JT
Nelson
R C
RC
D018784:Abdominal Abscess / Q000000981:diagnostic imaging*; D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D003198:Computer Simulation; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D014057:Tomography, X-Ray Computed