Interactive Network Exploration with<b>Orange</b>

Štajdohar, Miha; Demšar, Janez

doi:10.18637/jss.v053.i06

Cited by 7 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The software programs included Mathcad (version 2001; Mathsoft), SPSS/PC WIN 11.5.1 (SPSS, Chicago, IL), C++ programming language, and Orange [22]. Values of p lower than 0.05 indicated statistical significance.…”

Section: Methodsmentioning

confidence: 99%

Spirometric assessment of emphysema presence and severity as measured by quantitative CT and CT-based radiomics in COPD

et al. 2019

View full text Add to dashboard Cite

Background The mechanisms underlying airflow obstruction in COPD cannot be distinguished by standard spirometry. We ascertain whether mathematical modeling of airway biomechanical properties, as assessed from spirometry, could provide estimates of emphysema presence and severity, as quantified by computed tomography (CT) metrics and CT-based radiomics. Methods We quantified presence and severity of emphysema by standard CT metrics (VIDA) and co-registration analysis (ImbioLDA) of inspiratory-expiratory CT in 194 COPD patients who underwent pulmonary function testing. According to percentages of low attenuation area below − 950 Hounsfield Units (%LAA -950insp ) patients were classified as having no emphysema (NE) with %LAA -950insp < 6, moderate emphysema (ME) with %LAA -950insp ≥ 6 and < 14, and severe emphysema (SE) with %LAA -950insp ≥ 14. We also obtained stratified clusters of emphysema CT features by an automated unsupervised radiomics approach (CALIPER). An emphysema severity index (ESI), derived from mathematical modeling of the maximum expiratory flow-volume curve descending limb, was compared with pulmonary function data and the three CT classifications of emphysema presence and severity as derived from CT metrics and radiomics. Results ESI mean values and pulmonary function data differed significantly in the subgroups with different emphysema degree classified by VIDA, ImbioLDA and CALIPER ( p < 0.001 by ANOVA). ESI differentiated NE from ME/SE CT-classified patients (sensitivity 0.80, specificity 0.85, AUC 0.86) and SE from ME CT-classified patients (sensitivity 0.82, specificity 0.87, AUC 0.88). Conclusions Presence and severity of emphysema in patients with COPD, as quantified by CT metrics and radiomics can be estimated by mathematical modeling of airway function as derived from standard spirometry. Electronic supplementary material The online version of this article (10.1186/s12931-019-1049-3) contains supplementary material, which is available to authorized users.

show abstract

Section: Methodsmentioning

confidence: 99%

Spirometric assessment of emphysema presence and severity as measured by quantitative CT and CT-based radiomics in COPD

et al. 2019

View full text Add to dashboard Cite

show abstract

“…All analyses were performed using SPSS (PCWIN 11.5.1, Chicago, IL, USA) and Orange software [23]. Two-sided alpha 0.05 was considered significant.…”

Section: Discussionmentioning

confidence: 99%

Validation of a method to assess emphysema severity by spirometry in the COPDGene study

et al. 2020

View full text Add to dashboard Cite

Background Standard spirometry cannot identify the predominant mechanism underlying airflow obstruction in COPD, namely emphysema or airway disease. We aimed at validating a previously developed methodology to detect emphysema by mathematical analysis of the maximal expiratory flow-volume (MEFV) curve in standard spirometry. Methods From the COPDGene population we selected those 5930 subjects with MEFV curve and inspiratory-expiratory CT obtained on the same day. The MEFV curve descending limb was fit real-time using forced vital capacity (FVC), peak expiratory flow, and forced expiratory flows at 25, 50 and 75% of FVC to derive an emphysema severity index (ESI), expressed as a continuous positive numeric parameter ranging from 0 to 10. According to inspiratory CT percent lung attenuation area below − 950 HU we defined three emphysema severity subgroups (%LAA-950insp < 6, 6–14, ≥14). By co-registration of inspiratory-expiratory CT we quantified persistent (%pLDA) and functional (%fLDA) low-density areas as CT metrics of emphysema and airway disease, respectively. Results ESI differentiated CT emphysema severity subgroups increasing in parallel with GOLD stages (p < .001), but with high variability within each stage. ESI had significantly higher correlations (p < .001) with emphysema than with airway disease CT metrics, explaining 67% of %pLDA variability. Conversely, standard spirometric variables (FEV1, FEV1/FVC) had significantly lower correlations than ESI with emphysema CT metrics and did not differentiate between emphysema and airways CT metrics. Conclusions ESI adds to standard spirometry the power to discriminate whether emphysema is the predominant mechanism of airway obstruction. ESI methodology has been validated in the large multiethnic population of smokers of the COPDGene study and therefore it could be applied for clinical and research purposes in the general population of smokers, using a readily available online website.

show abstract

“…Most developments for the tool were for Python script layers, to avoid the core layer which is written in C++ [55]. Python is one of the most popular high-level languages, it contains a wide-ranging library that can be used in machine learning, data mining, and other scientific applications [56]. The ANN remains under active development by the authors, as many researchers have been able to improve this tool by adding new functions to suit their work [55].…”

Section: Overview Of the Artificial Neural Networkmentioning

confidence: 99%

Artificial Intelligence to Enhance Aerodynamic Shape Optimisation of the Aegis UAV

Azabi

Chai

Kipouros

2019

MAKE

View full text Add to dashboard Cite

This article presents an optimisation framework that uses stochastic multi-objective optimisation, combined with an Artificial Neural Network (ANN), and describes its application to the aerodynamic design of aircraft shapes. The framework uses the Multi-Objective Particle Swarm Optimisation (MOPSO) algorithm and the obtained results confirm that the proposed technique provides highly optimal solutions in less computational time than other approaches to the same design problem. The main idea was to focus computational effort on worthwhile design solutions rather than exploring and evaluating all possible solutions in the design space. It is shown that the number of valid solutions obtained using ANN-MOPSO compared to MOPSO for 3000 evaluations grew from 529 to 1006 (90% improvement) with a penalty of only 8.3% (11 min) in computational time. It is demonstrated that including an ANN, the ANN-MOPSO with 3000 evaluations produced a larger number of valid solutions than the MOPSO with 5500 evaluations, and in 33% less computational time (64 min). This is taken as confirmation of the potential power of ANNs when applied to this type of design problem.

show abstract

Interactive Network Exploration withOrange

Cited by 7 publications

References 11 publications

Spirometric assessment of emphysema presence and severity as measured by quantitative CT and CT-based radiomics in COPD

Spirometric assessment of emphysema presence and severity as measured by quantitative CT and CT-based radiomics in COPD

Validation of a method to assess emphysema severity by spirometry in the COPDGene study

Artificial Intelligence to Enhance Aerodynamic Shape Optimisation of the Aegis UAV

Contact Info

Product

Resources

About