High-accuracy detection of airway obstruction in asthma using machine learning algorithms and forced oscillation measurements

Amaral, Jorge; Lopes, Agnaldo José; Veiga, Juliana; Faria, Alvaro Camilo Dias; Melo, Pedro Lopes de

doi:10.1016/j.cmpb.2017.03.023

Cited by 44 publications

(25 citation statements)

References 55 publications

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“…This analysis was also adequate to identify respiratory changes in patients with abnormal spirometric exams ( Fig 6 ), which provides additional evidence of the usefulness of this analysis in diagnostic purposes. These results are in close agreement with recent studies in which the use of ML clinical decision support systems improved the diagnostic accuracy in chronic obstructive pulmonary disease [ 27 ] and asthma [ 29 ].…”

Section: Discussionsupporting

confidence: 92%

“…Many of these classifiers have been applied with a great deal of practical success in respiratory research [ 46 – 49 ]. A detailed description of them can be found in previous studies [ 26 – 29 , 50 ]. Because the data set size was relatively small, the performance evaluation was analyzed using k-fold cross-validation, which makes better use of the available dataset [ 26 – 29 ].…”

Section: Methodsmentioning

confidence: 99%

“…Machine learning (ML) methods are increasingly used in medicine [ 23 – 25 ], and may contribute to simplify the interpretation of FOT results and to enhance the diagnostic accuracy. Promising results were obtained in previous studies of our group in the detection of the early effects of smoking [ 26 ], the diagnosis [ 27 ] and classification of the severity [ 28 ] of chronic obstructive pulmonary disease, and airway obstruction in asthma [ 29 ]. To the best of our knowledge, however, there is no study evaluating the diagnostic potential of the FOT, and developing clinical decision support systems to facilitate the diagnostic use and to improve the accuracy of this method in patients with SCA.…”

Section: Introductionmentioning

confidence: 99%

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Respiratory resistance and reactance in adults with sickle cell anemia: Correlation with functional exercise capacity and diagnostic use

et al. 2017

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BackgroundThe improvement in sickle cell anemia (SCA) care resulted in the emergence of a large population of adults living with this disease. The mechanisms of lung injury in this new population are largely unknown. The forced oscillation technique (FOT) represents the current state-of-the-art in the assessment of lung function. The present work uses the FOT to improve our knowledge about the respiratory abnormalities in SCA, evaluates the associations of FOT with the functional exercise capacity and investigates the early detection of respiratory abnormalities.Methodology/Principal findingsSpirometric classification of restrictive abnormalities resulted in three categories: controls (n = 23), patients with a normal exam (n = 21) and presenting pulmonary restriction (n = 24). FOT analysis showed that, besides restrictive changes (reduced compliance; p<0.001), there is also an increase in respiratory resistance (p<0.001) and ventilation heterogeneity (p<0.01). FOT parameters are associated with functional exercise capacity (R = -0.38), pulmonary diffusion (R = 0.66), respiratory muscle performance (R = 0.41), pulmonary volumes (R = 0.56) and airway obstruction (R = 0.54). The diagnostic accuracy was evaluated by investigating the area under the receiver operating characteristic curve (AUC). A combination of FOT and machine learning (ML) classifiers showed adequate diagnostic accuracy in the detection of early respiratory abnormalities (AUC = 0.82).ConclusionsIn this study, the use of FOT showed that adults with SCA develop a mixed pattern of respiratory disease. Changes in FOT parameters are associated with functional exercise capacity decline, abnormal pulmonary mechanics and diffusion. FOT associated with ML methods accurately diagnosed early respiratory abnormalities. This suggested the potential utility of the FOT and ML clinical decision support systems in the identification of respiratory abnormalities in patients with SCA.

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Section: Discussionsupporting

confidence: 92%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Respiratory resistance and reactance in adults with sickle cell anemia: Correlation with functional exercise capacity and diagnostic use

et al. 2017

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“…13 XGBoost is a well-known ML technique that utilizes boosting decision trees algorithm to build a prediction model and has been utilized to predict the outcome of cystic fibrosis based on spirometry measures. 14,15 Amaral et al 16…”

Section: Machine Learningmentioning

confidence: 99%

“…XGBoost is a well‐known ML technique that utilizes boosting decision trees algorithm to build a prediction model and has been utilized to predict the outcome of cystic fibrosis based on spirometry measures . Amaral et al demonstrated that boosting decision trees, trained by IOS parameters, can be successfully used to diagnose OAD in patients with asthma. Neural network method is another commonly used ML technique.…”

Section: Introductionmentioning

confidence: 99%

Diagnostic value of spirometry vs impulse oscillometry: A comparative study in children with sickle cell disease

Mondal

Yirinec

Midya

et al. 2019

Pediatric Pulmonology

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Background Spirometry is conventionally used to diagnose airway diseases in children with sickle cell disease (C‐SCD). However, spirometry is difficult for younger children to perform, is effort dependent, and it provides limited information on respiratory mechanics. Impulse oscillometry (IOS) is an effort‐independent pulmonary function test (PFT), which measures total airway resistance (R5Hz) and reactance (AX). IOS could be advantageous without certain limitations of spirometry. Aim To compare the accuracy of IOS vs spirometry in making the diagnosis of asthma and assessing age‐related pulmonary changes in C‐SCD. Study design Retrospective chart review. Subject selection Fifty‐six C‐SCD and thirty‐six controls (asthmatics without SCD) followed at Penn State with PFTs obtained during the initial pulmonary evaluation. Methodology We grouped C‐SCD into asthmatics and non‐asthmatics based on pre‐referral diagnosis and compared PFTs between two groups. Receiver operating characteristic (ROC) curve analyses and machine learning tools (XGBoost and artificial neural network) were used to rank the spirometry and IOS measures based on their ability to predict a diagnosis of asthma. Robust linear regression was used to analyze association among height/age with various PFT measures. Results Both ROC and XGBoost indicated that FEF25‐75%, forced expiratory volume in 1 second (FEV1)/forced vital capacity, and R5Hz(%) were the top three predictors for asthma diagnosis. R5Hz(%) and AX had superior bronchodilator response (BDR) than FEV1. IOS parameters had significant association with height/age in C‐SCD (possibly due to the stiff lungs) but not in controls. Conclusion IOS had advantages over spirometry in C‐SCD because it is feasible in early childhood, provides insights into the pulmonary mechanics, and is more sensitive to detect BDR.

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AIM in Respiratory Disorders

Das

Topalović

Janssens

2021

Artificial Intelligence in Medicine

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High-accuracy detection of airway obstruction in asthma using machine learning algorithms and forced oscillation measurements

Cited by 44 publications

References 55 publications

Respiratory resistance and reactance in adults with sickle cell anemia: Correlation with functional exercise capacity and diagnostic use

Respiratory resistance and reactance in adults with sickle cell anemia: Correlation with functional exercise capacity and diagnostic use

Diagnostic value of spirometry vs impulse oscillometry: A comparative study in children with sickle cell disease

AIM in Respiratory Disorders

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