document: Pulmonary perfusion patterns and pulmonary arterial pressure Uses artificial intelligence methods to determine whether quantitative parameters describing the perfusion image can be synthesized to make a reasonable estimate of the pulmonary arterial -LRB- PA -RRB- pressure measured at angiography . Radionuclide perfusion images were obtained in 120 patients with normal chest radiographs who also underwent angiographic PA pressure measurement within 3 days of the radionuclide study . An artificial neural network -LRB- ANN -RRB- was constructed from several image parameters describing statistical and boundary characteristics of the perfusion images . With use of a leave-one-out cross-validation technique , this method was used to predict the PA systolic pressure in cases on which the ANN had not been trained . A Pearson correlation coefficient was determined between the predicted and measured PA systolic pressures . ANN predictions correlated with measured pulmonary systolic pressures -LRB- r = 0.846 , P < .001 -RRB- . The accuracy of the predictions was not influenced by the presence of pulmonary embolism . None of the 51 patients with predicted PA pressures of less than 29 mm Hg had pulmonary hypertension at angiography . All 13 patients with predicted PA pressures greater than 48 mm Hg had pulmonary hypertension at angiography . Meaningful information regarding PA pressure can be derived from noninvasive radionuclide perfusion scanning . The use of image analysis in concert with artificial intelligence methods helps to reveal physiologic information not readily apparent at visual image inspection
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