Literature DB >> 12748832

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

Eva Evander1, Holger Holst, Andreas Järund, Mattias Ohlsson, Per Wollmer, Karl Aström, Lars Edenbrandt.   

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.

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Year:  2003        PMID: 12748832     DOI: 10.1007/s00259-003-1182-5

Source DB:  PubMed          Journal:  Eur J Nucl Med Mol Imaging        ISSN: 1619-7070            Impact factor:   9.236


  15 in total

1.  Using artificial neural network analysis of global ventilation-perfusion scan morphometry as a diagnostic tool.

Authors:  J A Scott
Journal:  AJR Am J Roentgenol       Date:  1999-10       Impact factor: 3.959

2.  Efficient lung scintigraphy.

Authors:  K Tägil; E Evander; P Wollmer; J Palmer; B Jonson
Journal:  Clin Physiol       Date:  2000-03

3.  Automated interpretation of ventilation-perfusion lung scintigrams for the diagnosis of pulmonary embolism using artificial neural networks.

Authors:  H Holst; K Aström; A Järund; J Palmer; A Heyden; F Kahl; K Tägil; E Evander; G Sparr; L Edenbrandt
Journal:  Eur J Nucl Med       Date:  2000-04

4.  An independent evaluation of a new method for automated interpretation of lung scintigrams using artificial neural networks.

Authors:  H Holst; K Måre; A Järund; K Aström; E Evander; K Tägil; M Ohlsson; L Edenbrandt
Journal:  Eur J Nucl Med       Date:  2001-01

5.  Multifractal texture analysis of perfusion lung scans as a potential diagnostic tool for acute pulmonary embolism.

Authors:  G D Tourassi; E D Frederick; C E Floyd; R E Coleman
Journal:  Comput Biol Med       Date:  2001-01       Impact factor: 4.589

6.  The role of 133Xe ventilation studies in the scintigraphic detection of pulmonary embolism.

Authors:  P O Alderson; N Rujanavech; R H Sicker-Walker; R C McKnight
Journal:  Radiology       Date:  1976-09       Impact factor: 11.105

7.  Effect of ventilation images on observer interpretation of lung perfusion examinations.

Authors:  G L McLaughlin; R W Burt; B DePalma; R Gubler
Journal:  AJR Am J Roentgenol       Date:  1977-06       Impact factor: 3.959

8.  Introduction to neural networks.

Authors:  S S Cross; R F Harrison; R L Kennedy
Journal:  Lancet       Date:  1995-10-21       Impact factor: 79.321

9.  Krypton-81m ventilation scintigraphy for the diagnosis of pulmonary emboli.

Authors:  M L Goris; S G Daspit
Journal:  Clin Nucl Med       Date:  1981-05       Impact factor: 7.794

10.  How well can radiologists using neural network software diagnose pulmonary embolism?

Authors:  J A Scott; E L Palmer; A J Fischman
Journal:  AJR Am J Roentgenol       Date:  2000-08       Impact factor: 3.959

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  4 in total

Review 1.  Pulmonary thromboembolism in children.

Authors:  Paul S Babyn; Harpal K Gahunia; Patricia Massicotte
Journal:  Pediatr Radiol       Date:  2005-01-06

2.  A study on the value of computer-assisted assessment for SPECT/CT-scans in sentinel lymph node diagnostics of penile cancer as well as clinical reliability and morbidity of this procedure.

Authors:  Ulf Lützen; Carsten Maik Naumann; Marlies Marx; Yi Zhao; Michael Jüptner; René Baumann; László Papp; Norbert Zsótér; Alexey Aksenov; Klaus-Peter Jünemann; Maaz Zuhayra
Journal:  Cancer Imaging       Date:  2016-09-07       Impact factor: 3.909

3.  Neural hypernetwork approach for pulmonary embolism diagnosis.

Authors:  Matteo Rucco; David Sousa-Rodrigues; Emanuela Merelli; Jeffrey H Johnson; Lorenzo Falsetti; Cinzia Nitti; Aldo Salvi
Journal:  BMC Res Notes       Date:  2015-10-29

4.  Application of Artificial Neural Networks to Identify Alzheimer's Disease Using Cerebral Perfusion SPECT Data.

Authors:  Dariusz Świetlik; Jacek Białowąs
Journal:  Int J Environ Res Public Health       Date:  2019-04-11       Impact factor: 3.390

  4 in total

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