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Fig. 1 | Respiratory Research

Fig. 1

From: Deep learning parametric response mapping from inspiratory chest CT scans: a new approach for small airway disease screening

Fig. 1

Flowchart of participant inclusion and exclusion. Individuals with FEV1/FVC < 0.7 or FEV1pred < 80% were excluded from the study (n = 82). Those who showed poor cooperation during CT scans, insufficient expiration and inspiration, or motion artifacts on the image were not considered for the study (n = 89). Furthermore, individuals with a history of thoracic surgery (n = 19) and other conditions (n = 42) such as a huge thoracic mass, severe interstitial lung disease, etc., as observed on CT, were also not included in our study. Eligible participants were randomly divided into training, tuning, and test set using a random number generator. We used approximately 70% of the data for model training, 15% for model tuning, and the remaining 15% for performance testing. FEV1: forced expiratory volume in the first second; FVC: forced vital capacity

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