Abstract
Pulmonary fibrosis is a severe chronic lung disease that causes irreversible scarring in the tissues of the lungs, which results in the loss of lung capacity. The Forced Vital Capacity (FVC) of the patient is an interesting measure to investigate this disease to have the prognosis of the disease. This paper proposes a deep learning-based FVC-Net architecture to predict the progression of the disease from the patient's computed tomography (CT) scan and the patient's metadata. The input to the model combines the image score generated based on the degree of honeycombing for a patient identified based on segmented lung images and the metadata. This input is then fed to a 3-layer net to obtain the final output. The performance of the proposed FVC-Net model is compared with various contemporary state-of-the-art deep learning-based models, which are available on a cohort from the pulmonary fibrosis progression dataset. The model showcased significant improvement in the performance over other models for modified Laplace Log-Likelihood (−6.64). Finally, the paper concludes with some prospects to be explored in the proposed study.
Highlights
Interstitial lung disease (ILD) is a term for a cluster of conditions comprising Idiopathic Pulmonary Fibrosis (IPF) [1]
Fibrotic ILD such as IPF is exemplified by fibrotic destruction of the lung parenchyma concerning medical performance and prediction
IPF is an intensifying fibrotic lung disease linked with a desolate prognosis and an average survival of around three years [2]
Summary
Interstitial lung disease (ILD) is a term for a cluster of conditions comprising Idiopathic Pulmonary Fibrosis (IPF) [1]. Metadata is formed by concatenating demographic data of each patient and their degree of honeycombing (i.e., image score) as feature set M ∈ M1, M2, M3, . E degree of honeycombing is the number of edge pixels in the segmented lung scan after the edge detection step [16]. Where the baseline FVC is represented by FVCbase and l as the index of the week
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