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

Abstract

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.

Keywords: Airway responsiveness; Deep learning; Flow-volume curve; Pulmonary function test; Small plateau sign.

Fig. 1

Representative examples of the SP sign in BPTs and BDTs. a SP sign in positive BPT. Curve 1 = pre-BPT, SP sign (+); Curve 5 = post-BPT, SP (−). b SP sign in negative BPT. Curve 1 = pre-BPT, SP sign (+); Curve 6 = post-BPT, SP sign (+). c SP sign in positive BDT; Curve 1 = pre-BDT, SP sign (−); Curve 4 = post-BDT, SP (+). d SP sign in negative BDT; Curve 1 = pre-BDT, SP sign (+); Curve 4 = post-BDT, SP sign (+). Point A = the start point of the SP sign; Point B = the end point of the SP sign. SP = small plateau; BPTs = bronchoprovocation tests; BDTs = bronchodilator tests

Fig. 2

A couple of examples of pdf and input images. Inputs to the model were images extracted from collected PFT reports in pdf format. a Flow-volume curves of a woman (aged 64 years, height 162.8 cm) with pdf on the left, after pre-processing of curves by the model were used as input images on the right; b Flow-volume curves of a man (aged 25 years, height 165.5 cm). PFT = pulmonary function test

Fig. 3

Architecture of SP-Net. When given an input image, the classic ResNet convolutional neural network was utilized to extract the feature map. Then a region proposal network would generate object bounds and objectness scores. Next, a RoI pooling layer would extract a feature vector from the feature map for each of the proposals. Each feature vector was fed into a series of fully connected layers that finally branch into a classifier and a regressor which output: (1) Whether an SP sign was detected; (2) Bounding box positions. RoI = region of interest; SP = small plateau; SP-Net = SP-network

Fig. 4

Prevalence of SP sign of all patients in BPTs. BPT (−) SP sign (+) was defined as patients with SP sign and had negative-BPTs; BPT (−) SP sign (−) was defined as patients without SP sign and had negative-BPTs; BPT (+) SP sign (+) was defined as patients with SP sign and had positive-BPTs; BPT (+) SP sign (−) was defined as patients without SP sign and had positive-BPTs. Classes were defined as follows: class 1 = (Vol A − Vol B) × 100/FVC ratio ≤ 10% in BPT (−); class 2 = (Vol A − Vol B) × 100/FVC ratio > 10% to ≤ 20% in BPT (−); class 3 = (Vol A − Vol B) × 100/FVC ratio > 20% to ≤ 30% in BPT (−); class 4 = (Vol A − Vol B) × 100/FVC ratio > 30% in BPT (−). BPTs = bronchoprovocation tests; Vol = volume; SP = small plateau

Fig. 5

Prevalence of SP sign of all patients in BDTs. BDT (−) SP sign (+) was defined as patients with SP sign and had negative-BDTs; BDT (−) SP sign (−) was defined as patients without SP sign and had negative-BDTs; BDT (+) SP sign (+) was defined as patients with SP sign and had positive-BDTs; BDT (+) SP sign (−) was defined as patients without SP sign and had positive-BDTs. BDTs = bronchodilator tests; SP = small plateau

Fig. 6

Examples of the laryngoscopy findings. a Epiglottic cyst; b vocal cord nodule; c tonsil mass, d hypertrophy of tonsils/adenoids; e supraglottic laryngeal cancer. The location indicated by the black arrow is the lesion

Fig. 7

Model output. The SP-Net drew a red bounding box to represent the detected position of the SP sign and also generated corresponding annotations. SP = small plateau; SP-Net = SP-network

Fig. 8

Examples of the laryngoscopy findings and corresponding model outputs of the SP sign. a Normal; b vocal cord polyp. SP = small plateau

Fig. 9

Confusion matrix. 0: subjects without SP sign, 1: subjects with SP sign. SP = small plateau