Eva Evander, Holger Holst, Andreas Järund, Mattias Ohlsson, Per Wollmer, Karl Åström and Lars Edenbrandt Role of Ventilation Scintigraphy in Diagnosis of Acute Pulmonary Embolism. An Evaluation Using Artificial Neural Networks European Journal of Nuclear Medicine 30, 961-965 (2003) Abstract: The purpose of this study was to assess the value of the ventilation study in the diagnosis of acute pulmonary embolism using a new automated method. Either perfusion scintigrams alone or two different combinations of ventilation/perfusion scintigrams were used as the only source of information regarding pulmonary embolism. A completely automated method based on computerised image processing and artificial neural networks was used for the interpretation. Methods: Three artificial neural networks were trained for the diagnosis of pulmonary embolism. Each network was trained with 18 automatically obtained features. Three different sets of features originating from three sets of scintigrams, were used. One network was trained using features obtained from each set of perfusion scintigrams, including six projections. The second network was trained using features from each set of (joint) ventilation and perfusion studies in six projections. A third network was trained using features from the perfusion study in six projections combined with a single ventilation image from the posterior view. A total of 1087 scintigrams from patients with suspected pulmonary embolism were used for network training. The test group consisted of 102 patients who had performed both a scintigram as well as pulmonary angiography. Performances in the test group were measured as area under the receiver operation characteristic curve. Results: The performance of the neural network interpreting perfusion scintigrams alone was 0.79 (95% confidence limits 0.71-0.86). When one ventilation image (posterior view) was added to the perfusion study, the performance was 0.84 (0.77-0.90). The increase was statistically significant (p<0.01). The performance increased to 0.87 (0.81-0.93) when both the perfusion and ventilation studies were used and also this increase in performance was statistically significant (p<0.01). Conclusions: The automated method presented here for the interpretation of lung scintigrams shows a significant increase in performance when one or all ventilation images are added to the six perfusion images. Thus, the ventilation study has a significant role in the diagnosis of acute lung embolism. LU TP 02-37