title: Pulmonary perfusion patterns and pulmonary arterial pressure
abstract: 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 (PA) 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 (ANN) 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 (r=0.846, P<.001). 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
keyphrases: pulmonary perfusion patterns artificial intelligence methods quantitative parameters perfusion image angiography radionuclide perfusion images patients normal chest radiographs image parameters boundary characteristics leave-one-out cross-validation technique Pearson correlation coefficient accuracy pulmonary embolism pulmonary hypertension noninvasive radionuclide perfusion scanning image analysis physiologic information visual image inspection angiographic pulmonary arterial pressure measurement artificial neural network predictions statistical characteristics pulmonary arterial systolic pressure 29 Pa 48 Pa