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

OBJECTIVE. The purpose of this study was to determine whether global statis tical data from radionuclide ventilation-perfusion scans could predict the likelihood of pulmonary embolism. MATERIALS AND METHODS. Digital data were obtained from 161 patients undergo ing both radionuclide ventilation-perfusion scanning and subsequent pulmona ry angiography. Morphometric data characterizing whole-lung perfusion and v entilation parameters were input into artificial neural networks in an atte mpt 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 nor mal findings on chest radiographs with segmental or larger emboli (p < .005 ) and in patients with normal findings on chest radiographs and emboli of a ny size (p < .01). Network performance did not significantly differ from cl inician performance in patients with abnormal findings on chest radiographs . CONCLUSION. The adjunctive use of artificial neural networks using only use r-independent, standard image statistics can significantly improve accuracy in the diagnosis of pulmonary embolism in patients with normal findings on chest radiographs.