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Predicting the Presence of Acute Pulmonary Embolism: A Comparative Analysis of the Artificial Neural Network, Logistic Regression, and Threshold Models

John Eng1

1 Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, Central Radiology Viewing Area, Rm. 117, 600 N. Wolfe St., Baltimore, MD 21287.



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  		Fig. 1. —Schematic diagram illustrates structure of artificial neural network chosen for this study. Each lung was divided into three regions (upper, middle, and lower), resulting in 18 input units. Additional units were number of subsegmental perfusion mismatches, presence or absence of symmetry in the number of mismatches, and size of largest pleural perfusion. Links were defined for every possible pairing of input units between adjacent layers. Vent = ventilation scan, perf = perfusion scan, CXR = chest radiograph, Asymm = asymmetry.

 


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  		Fig. 2. —Graph depicts empiric (nonparametric) receiver operating characteristic curves representing accuracy of three data models in predicting presence or absence of pulmonary embolism. Curves are similar for each. Thick black line = neural network, thick gray line = logistic regression, thin black line = threshold.

 


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  		Fig. 3. —Graph shows comparison of areas under receiver operating characteristic (ROC) curves for four data models and two groups of physicians who were unaided by any modeling. Only diagnosis of PIOPED (Prospective Investigation of Pulmonary Embolism Diagnosis) study's referring clinician was statistically significantly different from neural network model. SD = standard deviation, CI = confidence interval.

 


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  		Fig. 4. —Bar graph illustrates mean value of 21 input variables used in this study. Variable 19, representing total number of subsegmental ventilation—perfusion scanning mismatches, is dominant. {square} = pulmonary embolism absent, {blacksquare} = pulmonary embolism present.

 

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