Role of ventilation scintigraphy in diagnosis of acute pulmonary embolism: an evaluation using artificial neural networks.

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. 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 1,087 scintigrams from patients with

suspected pulmonary embolism were used for network

training. The test group consisted of 102 patients who

had undergone both scintigraphy and pulmonary angiography.

Performances in the test group were measured as

area under the receiver operation characteristic curve.

The performance of the neural network in 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). This increase was statistically significant

(P=0.022). The performance increased to 0.87

(0.81–0.93) when all perfusion and ventilation images

were used, and the increase in performance from 0.79 to

0.87 was also statistically significant (P=0.016). 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.