Predictive model for the diagnosis of intraabdominal abscess.
Acad Radiol
; 5(7): 473-9, 1998 Jul.
Article
en En
| MEDLINE
| ID: mdl-9653463
ABSTRACT
RATIONALE AND OBJECTIVES:
The authors investigated the use of an artificial neural network (ANN) to aid in the diagnosis of intraabdominal abscess. MATERIALS ANDMETHODS:
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.
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Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Simulación por Computador
/
Interpretación de Imagen Radiográfica Asistida por Computador
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Redes Neurales de la Computación
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Absceso Abdominal
Tipo de estudio:
Diagnostic_studies
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Prognostic_studies
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Risk_factors_studies
Límite:
Adolescent
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Adult
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Aged
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Aged80
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
Acad Radiol
Asunto de la revista:
RADIOLOGIA
Año:
1998
Tipo del documento:
Article
País de afiliación:
Estados Unidos