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American Journal of Roentgenology, Vol 173, 943-948, Copyright © 1999 by American Roentgen Ray Society

ARTICLES

Using artificial neural network analysis of global ventilation- perfusion scan morphometry as a diagnostic tool

JA Scott
Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston 02114, USA.

OBJECTIVE: The purpose of this study was to determine whether global statistical data from radionuclide ventilation-perfusion scans could predict the likelihood of pulmonary embolism. MATERIALS AND METHODS: Digital data were obtained from 161 patients undergoing both radionuclide ventilation-perfusion scanning and subsequent pulmonary angiography. Morphometric data characterizing whole-lung perfusion and ventilation parameters were input into artificial neural networks in an attempt to predict the likelihood of pulmonary embolism. RESULTS: The performance of artificial neural networks using only automated global region of interest-based data was superior to that of clinicians in predicting the likelihood of acute pulmonary embolism in patients with normal findings on chest radiographs with segmental or larger emboli (p < .005) and in patients with normal findings on chest radiographs and emboli of any size (p < .01). Network performance did not significantly differ from clinician performance in patients with abnormal findings on chest radiographs. CONCLUSION: The adjunctive use of artificial neural networks using only user-independent, standard image statistics can significantly improve accuracy in the diagnosis of pulmonary embolism in patients with normal findings on chest radiographs.
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Copyright © 1999 by the American Roentgen Ray Society.