Mattias Ohlsson
Professor
Role of ventilation scintigraphy in diagnosis of acute pulmonary embolism: an evaluation using artificial neural networks.
Author
Summary, in English
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.
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.
Department/s
- Clinical Physiology (Lund)
- Computational Biology and Biological Physics - Undergoing reorganization
- Clinical Physiology and Nuclear Medicine, Malmö
- Mathematics (Faculty of Engineering)
- Nuclear medicine, Malmö
- Mathematical Imaging Group
Publishing year
2003
Language
English
Pages
961-965
Publication/Series
European Journal of Nuclear Medicine and Molecular Imaging
Volume
30
Issue
7
Links
Document type
Journal article
Publisher
Springer
Topic
- Radiology, Nuclear Medicine and Medical Imaging
Status
Published
Research group
- Clinical Physiology, Malmö
- Nuclear medicine, Malmö
- Mathematical Imaging Group
ISBN/ISSN/Other
- ISSN: 1619-7070