FVC-NET: An Automated Diagnosis of Pulmonary Fibrosis Progression Prediction Using Honeycombing and Deep Learning

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.

Adrenomedullin Expression Is Associated With the Severity and Poor Prognosis of Interstitial Lung Disease in Dermatomyositis Patients

“…We found that PBMC adrenomedullin levels in DM patients with ILD associated with pulmonary function test results, higher adrenomedullin mRNA levels correlated with worse lung function in DM patients with ILD, which may help assess the severity of ILD ( 33 , 34 ). In addition, deterioration of lung function, especially forced vital capacity (FVC) and DLco, is known to indicate disease progression and to be corelated with prognosis in DM patients with ILD ( 34 ).…”

Artificial Intelligence and Interstitial Lung Disease: Diagnosis and Prognosis

“…The authors also used the GSInquire method 35 to make the model more explainable concerning its predictions. Yadav et al 36 designed FVC-Net, a deep learning-based architecture, to predict the progression of the disease from the patient's CT scans and the patient's metadata. The proposed method performs lung segmentation and returns a score for the degree of honeycombing.…”

Retracted: FVC-NET: An Automated Diagnosis of Pulmonary Fibrosis Progression Prediction Using Honeycombing and Deep Learning

“…Tis article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of systematic manipulation of the publication and peer-review process.…”