Role of ventilation scintigraphy in diagnosis of acute pulmonary embolism: an evaluation using artificial neural networks.
Evander, Eva LU ; Holst, Holger ; Järund, Andreas ; Ohlsson, Mattias LU
; Wollmer, Per
LU
; Åström, Karl
LU
and Edenbrandt, Lars
LU
(2003)
In European Journal of Nuclear Medicine and Molecular Imaging
30(7).
p.961-965
- 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... (More) - 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/114036
- author
- Evander, Eva
LU
; Holst, Holger
; Järund, Andreas
; Ohlsson, Mattias
LU
; Wollmer, Per
LU
; Åström, Karl
LU
and Edenbrandt, Lars
LU
- organization
- publishing date
- 2003
- type
- Contribution to journal
- publication status
- published
- subject
- in
- European Journal of Nuclear Medicine and Molecular Imaging
- volume
- 30
- issue
- 7
- pages
- 961 - 965
- publisher
- Springer
- external identifiers
-
- wos:000184644800004
- pmid:12748832
- scopus:0042192115
- ISSN
- 1619-7070
- DOI
- 10.1007/s00259-003-1182-5
- language
- English
- LU publication?
- yes
- additional info
- The information about affiliations in this record was updated in December 2015. The record was previously connected to the following departments: Clinical Physiology (013242300), Department of Clinical Physiology (Lund) (013013000), Computational biology and biological physics (000006113), Mathematics (Faculty of Technology) (011015005), Clinical Physiology and Nuclear Medicine Unit (013242320), Lund University Research Program in Medical Informatics (013242310)
- id
- 1e802720-4449-4afa-a920-104b224b4e09 (old id 114036)
- alternative location
- http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=Retrieve&db=PubMed&list_uids=12748832&dopt=Abstract
- date added to LUP
- 2016-04-01 12:30:58
- date last changed
- 2024-01-08 23:10:00
@article{1e802720-4449-4afa-a920-104b224b4e09,
abstract = {{The purpose of this study was to assess the<br/><br>
value of the ventilation study in the diagnosis of acute<br/><br>
pulmonary embolism using a new automated method.<br/><br>
Either perfusion scintigrams alone or two different combinations<br/><br>
of ventilation/perfusion scintigrams were used<br/><br>
as the only source of information regarding pulmonary<br/><br>
embolism. A completely automated method based on<br/><br>
computerised image processing and artificial neural networks<br/><br>
was used for the interpretation. Three artificial<br/><br>
neural networks were trained for the diagnosis of pulmonary<br/><br>
embolism. Each network was trained with 18 automatically<br/><br>
obtained features. Three different sets of features<br/><br>
originating from three sets of scintigrams were<br/><br>
used. One network was trained using features obtained<br/><br>
from each set of perfusion scintigrams, including six<br/><br>
projections. The second network was trained using features<br/><br>
from each set of (joint) ventilation and perfusion<br/><br>
studies in six projections. A third network was trained<br/><br>
using features from the perfusion study in six projections<br/><br>
combined with a single ventilation image from the posterior<br/><br>
view. A total of 1,087 scintigrams from patients with<br/><br>
suspected pulmonary embolism were used for network<br/><br>
training. The test group consisted of 102 patients who<br/><br>
had undergone both scintigraphy and pulmonary angiography.<br/><br>
Performances in the test group were measured as<br/><br>
area under the receiver operation characteristic curve.<br/><br>
The performance of the neural network in interpreting<br/><br>
perfusion scintigrams alone was 0.79 (95% confidence<br/><br>
limits 0.71–0.86). When one ventilation image (posterior<br/><br>
view) was added to the perfusion study, the performance<br/><br>
was 0.84 (0.77–0.90). This increase was statistically significant<br/><br>
(P=0.022). The performance increased to 0.87<br/><br>
(0.81–0.93) when all perfusion and ventilation images<br/><br>
were used, and the increase in performance from 0.79 to<br/><br>
0.87 was also statistically significant (P=0.016). The automated<br/><br>
method presented here for the interpretation of<br/><br>
lung scintigrams shows a significant increase in performance<br/><br>
when one or all ventilation images are added to<br/><br>
the six perfusion images. Thus, the ventilation study has<br/><br>
a significant role in the diagnosis of acute lung embolism.}},
author = {{Evander, Eva and Holst, Holger and Järund, Andreas and Ohlsson, Mattias and Wollmer, Per and Åström, Karl and Edenbrandt, Lars}},
issn = {{1619-7070}},
language = {{eng}},
number = {{7}},
pages = {{961--965}},
publisher = {{Springer}},
series = {{European Journal of Nuclear Medicine and Molecular Imaging}},
title = {{Role of ventilation scintigraphy in diagnosis of acute pulmonary embolism: an evaluation using artificial neural networks.}},
url = {{http://dx.doi.org/10.1007/s00259-003-1182-5}},
doi = {{10.1007/s00259-003-1182-5}},
volume = {{30}},
year = {{2003}},
}