Breathe out the Secret of the Lung: Video Classification of Exhaled Flows from Normal and Asthmatic Lung Models Using CNN-Long Short-Term Memory Networks

Abstract

In this study, we present a novel approach to differentiate normal and diseased lungs based on exhaled flows from 3D-printed lung models simulating normal and asthmatic conditions. By leveraging the sequential learning capacity of the Long Short-Term Memory (LSTM) network and the automatic feature extraction of convolutional neural networks (CNN), we evaluated the feasibility of the automatic detection and staging of asthmatic airway constrictions. Two asthmatic lung models (D1, D2) with increasing levels of severity were generated by decreasing the bronchiolar calibers in the right upper lobe of a normal lung (D0). Expiratory flows were recorded in the mid-sagittal plane using a high-speed camera at 1500 fps. In addition to the baseline flow rate (20 L/min) with which the networks were trained and verified, two additional flow rates (15 L/min and 10 L/min) were considered to evaluate the network’s robustness to flow deviations. Distinct flow patterns and vortex dynamics were observed among the three disease states (D0, D1, D2) and across the three flow rates. The AlexNet-LSTM network proved to be robust, maintaining perfect performance in the three-class classification when the flow deviated from the recommendation by 25%, and still performed reasonably (72.8% accuracy) despite a 50% flow deviation. The GoogleNet-LSTM network also showed satisfactory performance (91.5% accuracy) at a 25% flow deviation but exhibited low performance (57.7% accuracy) when the deviation was 50%. Considering the sequential learning effects in this classification task, video classifications only slightly outperformed those using still images (i.e., 3–6%). The occlusion sensitivity analyses showed distinct heat maps specific to the disease state.