Choledocholithiasis: diagnosis with oral-contrast-enhanced CT cholangiography

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.