Classification of Spirometry Using Stacked Autoencoder based Neural Network

Trivedy, Sudipto; Goyal, Manish; Mishra, Madhusudhan; Verma, Narsingh; Mukherjee, Anirban

doi:10.1109/i2mtc.2019.8827087

Cited by 2 publications

(1 citation statement)

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“…Several software applications and algorithms established for interpreting PFTs have been investigated in healthcare research (Giri et al, 2021). A stacked autoencoder-based neural network has been used to detect abnormalities using spirometric parameters such as the forced expiratory volume in the first second (FEV 1 ), forced vital capacity (FVC), FEV 1 /FVC, and flow-volume curves (Trivedy et al, 2019). Ventilatory patterns have a characteristic configuration in the flow-volume curves (Pellegrino et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Analytic Models Based on Flow-Volume Curves for Identifying Ventilatory Patterns

Wang

Chen

et al. 2022

Front. Physiol.

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IntroductionSpirometry, a pulmonary function test, is being increasingly applied across healthcare tiers, particularly in primary care settings. According to the guidelines set by the American Thoracic Society (ATS) and the European Respiratory Society (ERS), identifying normal, obstructive, restrictive, and mixed ventilatory patterns requires spirometry and lung volume assessments. The aim of the present study was to explore the accuracy of deep learning-based analytic models based on flow–volume curves in identifying the ventilatory patterns. Further, the performance of the best model was compared with that of physicians working in lung function laboratories.MethodsThe gold standard for identifying ventilatory patterns was the rules of ATS/ERS guidelines. One physician chosen from each hospital evaluated the ventilatory patterns according to the international guidelines. Ten deep learning models (ResNet18, ResNet34, ResNet18_vd, ResNet34_vd, ResNet50_vd, ResNet50_vc, SE_ResNet18_vd, VGG11, VGG13, and VGG16) were developed to identify patterns from the flow–volume curves. The patterns obtained by the best-performing model were cross-checked with those obtained by the physicians.ResultsA total of 18,909 subjects were used to develop the models. The ratio of the training, validation, and test sets of the models was 7:2:1. On the test set, the best-performing model VGG13 exhibited an accuracy of 95.6%. Ninety physicians independently interpreted 100 other cases. The average accuracy achieved by the physicians was 76.9 ± 18.4% (interquartile range: 70.5–88.5%) with a moderate agreement (κ = 0.46), physicians from primary care settings achieved a lower accuracy (56.2%), while the VGG13 model accurately identified the ventilatory pattern in 92.0% of the 100 cases (P < 0.0001).ConclusionsThe VGG13 model identified ventilatory patterns with a high accuracy using the flow–volume curves without requiring any other parameter. The model can assist physicians, particularly those in primary care settings, in minimizing errors and variations in ventilatory patterns.

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Section: Introductionmentioning

confidence: 99%