Fibrosis-Net: A Tailored Deep Convolutional Neural Network Design for Prediction of Pulmonary Fibrosis Progression From Chest CT Images

Abstract

Pulmonary fibrosis is a devastating chronic lung disease that causes irreparable lung tissue scarring and damage, resulting in progressive loss in lung capacity and has no known cure. A critical step in the treatment and management of pulmonary fibrosis is the assessment of lung function decline, with computed tomography (CT) imaging being a particularly effective method for determining the extent of lung damage caused by pulmonary fibrosis. Motivated by this, we introduce Fibrosis-Net, a deep convolutional neural network design tailored for the prediction of pulmonary fibrosis progression from chest CT images. More specifically, machine-driven design exploration was leveraged to determine a strong architectural design for CT lung analysis, upon which we build a customized network design tailored for predicting forced vital capacity (FVC) based on a patient’s CT scan, initial spirometry measurement, and clinical metadata. Finally, we leverage an explainability-driven performance validation strategy to study the decision-making behavior of Fibrosis-Net as to verify that predictions are based on relevant visual indicators in CT images. Experiments using a patient cohort from the OSIC Pulmonary Fibrosis Progression Challenge showed that the proposed Fibrosis-Net is able to achieve a significantly higher modified Laplace Log Likelihood score than the winning solutions on the challenge. Furthermore, explainability-driven performance validation demonstrated that the proposed Fibrosis-Net exhibits correct decision-making behavior by leveraging clinically-relevant visual indicators in CT images when making predictions on pulmonary fibrosis progress. Fibrosis-Net is able to achieve a significantly higher modified Laplace Log Likelihood score than the winning solutions on the OSIC Pulmonary Fibrosis Progression Challenge, and has been shown to exhibit correct decision-making behavior when making predictions. Fibrosis-Net is available to the general public in an open-source and open access manner as part of the OpenMedAI initiative. While Fibrosis-Net is not yet a production-ready clinical assessment solution, we hope that its release will encourage researchers, clinicians, and citizen data scientists alike to leverage and build upon it.

MEDUSA: Multi-Scale Encoder-Decoder Self-Attention Deep Neural Network Architecture for Medical Image Analysis

“…Finally, while in this study, we mainly focused on chest X-ray analysis of COVID-19, our proposed MEDUSA framework has broader potential in medical image analysis beyond the studied clinical workflow tasks and modality. In this regard, MEDUSA can be used for a wide range of applications ranging from disease detection, risk stratification, and treatment planning for a wide range of diseases such as tuberculosis ( 8 , 9 ), pulmonary fibrosis ( 10 , 11 ), prostate cancer ( 50 – 53 ), breast cancer ( 17 ), and lung cancer ( 12 – 14 ) using different modalities ranging from ultrasound to MRI to CT to PET imaging.…”

“…For example, in the area of lung related complications, deep neural networks have been explored to great effect for aiding clinicians in the detection of tuberculosis ( 8 , 9 ), pulmonary fibrosis ( 10 , 11 ), and lung cancer ( 12 – 14 ). Similar works have been done for prostate cancer ( 15 , 15 , 16 ) and breast cancer ( 17 , 18 ).…”

Recent Advancements in Computed Tomography Assessment of Fibrotic Interstitial Lung Diseases

“…36 This has enabled a number of researchers to develop AI models for the prediction of pulmonary fibrosis severity on CT, correlating their performances with spirometry. Using the OSIC data set, Wong et al 37 introduced an open-source CNN "Fibrosis-Net" with the goal to predict progression of pulmonary fibrosis on CT scans. The authors leveraged a unique approach of explainability-driven performance validation strategy to study whether the model's predictions are based on clinically relevant imaging features.…”

Artificial Intelligence and Interstitial Lung Disease

“…As the FVC is a continuous number, it falls into the category of regression. Two related studies, 32 , 33 employed an end-to-end multi-modal based CNN to predict FVC decline. Experiments were run on the OSIC Pulmonary Fibrosis Progression Challenge Benchmark Dataset, 34 the most popular dataset to train models for predicting patients’ severity of decline in lung function.…”

“… 32 Then, the prediction of the FVC slope is performed by using regression. Similarly, Fibrosis-Net 33 extracts image features and predicts FVC by fusing the CNN output features and demographic data. The authors also used the GSInquire method 35 to make the model more explainable concerning its predictions.…”