Flow-Volume Curve Analysis for Predicting Recurrence After Endoscopic Dilation of Airway Stenosis

et al. 2021

BMC Pulm Med

Background Small plateau (SP) on the flow-volume curve was found in parts of patients with suspected asthma or upper airway abnormalities, but it lacks clear scientific proof. Therefore, we aimed to characterize its clinical features. Methods We involved patients by reviewing the bronchoprovocation test (BPT) and bronchodilator test (BDT) completed between October 2017 and October 2020 to assess the characteristics of the sign. Patients who underwent laryngoscopy were assigned to perform spirometry to analyze the relationship of the sign and upper airway abnormalities. SP-Network was developed to recognition of the sign using flow-volume curves. Results Of 13,661 BPTs and 8,168 BDTs completed, we labeled 2,123 (15.5%) and 219 (2.7%) patients with the sign, respectively. Among them, there were 1,782 (83.9%) with the negative-BPT and 194 (88.6%) with the negative-BDT. Patients with SP sign had higher median FVC and FEV1% predicted (both P < .0001). Of 48 patients (16 with and 32 without the sign) who performed laryngoscopy and spirometry, the rate of laryngoscopy-diagnosis upper airway abnormalities in patients with the sign (63%) was higher than those without the sign (31%) (P = 0.038). SP-Network achieved an accuracy of 95.2% in the task of automatic recognition of the sign. Conclusions SP sign is featured on the flow-volume curve and recognized by the SP-Network model. Patients with the sign are less likely to have airway hyperresponsiveness, automatic visualizing of this sign is helpful for primary care centers where BPT cannot available.

Section: Discussionmentioning

confidence: 99%

Clinical analysis of the “small plateau” sign on the flow-volume curve followed by deep learning automated recognition

et al. 2021

BMC Pulm Med

“…They found an accuracy of 92% at identifying UAO. However, previous studies focused on detecting UAO but did not classify its characteristic patterns and distinguished them from other classic patterns [15,29]. The deep learning model our study used provided good performance while processing various UAO patterns and other classic patterns, it could help to resolve more complicated clinical problems of UAO classification.…”

Section: Discussionmentioning

confidence: 98%

“…Quantitative criteria: on the basis of previous findings, criteria were as follows: FEF 50% /FIF 50% < 0.3 or >1.0 [10], FEV 1 /FEV 0.5 > 1.5 [7], FEV 1 /PEF > 10 mL/L/min [12], FIF 50% < 100 L/min [7], PEF/PIF > 1.8 [13], and A exp/ A inp > 1.3 [13]. Aggregate criteria: the aggregate criteria were used based on a mathematical model from Boolean algebra [24], which has been used to predict recurrence in patients after endoscopic dilation treatment of upper airway stenosis [15]: F = A × B + C × (A + B). A is the visual criteria, B is the FEF 50% /FIF 50% , C is the other quantitative criteria.…”

Section: Methodsmentioning

confidence: 99%

“…UAO can severely affect peak expiratory flow (PEF); the FEV 1 /PEF ratio >50% [11] or the FEV 1 /PEF >10 mL/L/min [12] suggests UAO may be present. Other quantitative criteria, such as FEF 50% /FIF 50% < 0.3 or > 1.0, FEV 1 /FEV 0.5 > 1.5, FIF 50% < 100 L/min, PEF/PIF > 1.8, and the ratio of the area under the expiratory and inspiratory curves (A exp /A inp ) > 1.3 also play critical roles in detecting UAO [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning for Automatic Upper Airway Obstruction Detection by Analysis of Flow-Volume Curve

et al. 2022

Respiration

Background: Due to the similar symptoms of upper airway obstruction to asthma, misdiagnosis is common. Spirometry is a cost-effective screening test for upper airway obstruction and its characteristic patterns involving fixed, variable intrathoracic and extrathoracic lesions. We aimed to develop a deep learning model to detect upper airway obstruction patterns and compared its performance with that of lung function clinicians. Methods: Spirometry records were reviewed to detect the possible condition of airway stenosis. Then they were confirmed by the gold standard (e.g., computed tomography, endoscopy, or clinic diagnosis of upper airway obstruction). Images and indices derived from flow-volume curves were used for training and testing the model. Clinicians determined cases using spirometry records from the test set. The deep learning model evaluated the same data. Results: Of 45,831 patients’ spirometry records, 564 subjects with curves suggesting upper airway obstruction, after verified by the gold standard, 351 patients were confirmed. These cases and another 200 cases without airway stenosis were used as the training and testing sets. 432 clinicians evaluated 20 cases of each of the three patterns and 20 no airway stenosis cases (n = 80). They assigned an accuracy of 41.2% (±15.4) (interquartile range: 27.5–52.5%), with poor agreements (κ = 0.12). For the same cases, the model generated a correct detection of 81.3% (p < 0.0001). Conclusions: Deep learning could detect upper airway obstruction patterns from other classic patterns of ventilatory defects with high accuracy, whereas clinicians presented marked errors and variabilities. The model may serve as a support tool to enhance clinicians’ correct diagnosis of upper airway obstruction using spirometry.

“…Additionally, we only explored the conventional patterns. Specific patterns, such as upper airway obstruction (Fiorelli et al, 2019), "saw-tooth sign" (Bourne et al, 2017), and the "small-plateau sign" (Wang et al, 2021), require the recognition of flow-volume curves, including inspiratory and expiratory phases.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

Front. Physiol.

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.