Convolutional Neural Network Analysis to Predict Expiratory Flow Limitation During Exercise in Adults with Asthma

OBJECTIVE AND HYPOTHESIS: The objective of this study was to assess the potential for using well-known convolutional neural network (CNN) designs to accurately identify the presence of expiratory flow-limitation (EFL) during exercise in adult asthmatics. We hypothesized that a CNN model would accurately identify spontaneous exercise tidal flow-volume loops (eFVL) that exceed maximal airflow from the maximal forced expiration (i.e., EFL). METHODS AND RESULTS: The expiratory portion of spontaneous eFVLs were placed within the pre-exercise maximal volitional flow-volume envelope. Inspiratory capacity maneuvers were performed to determine the correct lung volume for placement of the eFVLs. We assembled a collection of spontaneous eFVLs from one asthmatic subject consisting of an equal number of eFVLs that were flow-limited and non-flow-limited. Subsequently, our overall approach followed standard deep learning practices. The eFVLs were manually labelled as EFL and non-EFL. The sci-kit learn train_test_split() method was used to split the eFVLs randomly into training and testing sets using a 67%/33% split. We trained both a one- (expired airflow only) and a two-channel (expired volume and airflow) CNN model to predict the presence or absence of EFL as a response to an eFVL. The models were instantiated as sequential models using the TensorFlow 2.6.0 python API. After 100 training epochs on the training cases, the 1-channel model was challenged to predict the labels of the previously unseen testing cases. The 1-channel model achieved 93.3% accuracy at predicting the EFL/non-EFL labels as a response to the eFVLs despite having not been trained on these data. Additionally, the 2-channel model was 100% accurate on the unseen test cases. We then challenged the 2-channel model on 45 unseen eFVLs from a different asthmatic subject; the eFVLs consisted of a balance of EFL and non-EFL. The model performed poorly, accurately identifying only 57% of the eFVLs as EFL or non-EFL. We then trained an additional 2-channel model on a random sample of the combined data from the two subjects and challenged the model on the unseen eFVLs from the same two subjects. At 25 epochs and 100 epochs, the CNN was challenged with unseen testing subjects and achieved 88 and 92% accuracy, respectively, at distinguishing between EFL and non-EFL. CONCLUSIONS: These proof-of-concept findings provide strong evidence that neural network analyses hold promise as a novel method for identifying either the presence or absence of EFL. Given the current, tedious and nuanced methods for determining EFL, such artificial intelligence analyses hold promise for automating the analysis and identification of exercise ventilatory constraints. © FASEB.