AIMC Topic: Pulmonary Fibrosis
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A fully automated micro‑CT deep learning approach for precision preclinical investigation of lung fibrosis progression and response to therapy.
Respiratory research
May 9, 2023
Micro-computed tomography (µCT)-based imaging plays a key role in monitoring disease progression and response to candidate drugs in various animal models of human disease, but manual image processing is still highly time-consuming and prone to operat...
A fully automated deep learning pipeline for micro-CT-imaging-based densitometry of lung fibrosis murine models.
Respiratory research
Nov 11, 2022
Idiopathic pulmonary fibrosis, the archetype of pulmonary fibrosis (PF), is a chronic lung disease of a poor prognosis, characterized by progressively worsening of lung function. Although histology is still the gold standard for PF assessment in prec...
The characteristics and evolution of pulmonary fibrosis in COVID-19 patients as assessed by AI-assisted chest HRCT.
PloS one
Mar 23, 2021
The characteristics and evolution of pulmonary fibrosis in patients with coronavirus disease 2019 (COVID-19) have not been adequately studied. AI-assisted chest high-resolution computed tomography (HRCT) was used to investigate the proportion of COVI...
CT image segmentation for inflamed and fibrotic lungs using a multi-resolution convolutional neural network.
Scientific reports
Jan 14, 2021
The purpose of this study was to develop a fully-automated segmentation algorithm, robust to various density enhancing lung abnormalities, to facilitate rapid quantitative analysis of computed tomography images. A polymorphic training approach is pro...
Artificial intelligence and machine learning in respiratory medicine.
Expert review of respiratory medicine
Mar 17, 2020
: The application of artificial intelligence (AI) and machine learning (ML) in medicine and in particular in respiratory medicine is an increasingly relevant topic.: We aimed to identify and describe the studies published on the use of AI and ML in t...
Imaging research in fibrotic lung disease; applying deep learning to unsolved problems.
The Lancet. Respiratory medicine
Feb 25, 2020
Over the past decade, there has been a groundswell of research interest in computer-based methods for objectively quantifying fibrotic lung disease on high resolution CT of the chest. In the past 5 years, the arrival of deep learning-based image anal...
Novelties in imaging in pulmonary fibrosis and nodules. A narrative review.
Pulmonology
Nov 7, 2019
In recent months two major fields of interest in pulmonary imaging have stood out: pulmonary fibrosis and pulmonary nodules. New guidelines have been released to define pulmonary fibrosis and subsequent studies have proved the value of these changes....
Development of Adverse Outcome Pathway for PPARγ Antagonism Leading to Pulmonary Fibrosis and Chemical Selection for Its Validation: ToxCast Database and a Deep Learning Artificial Neural Network Model-Based Approach.
Chemical research in toxicology
Jun 7, 2019
Exposure to certain chemicals such as disinfectants through inhalation is suspected to be involved in the development of pulmonary fibrosis, a lung disease in which lung tissue becomes damaged and scarred. Pulmonary fibrosis is known to be regulated ...
Deep learning and the evaluation of pulmonary fibrosis.
The Lancet. Respiratory medicine
Sep 16, 2018
Biomarkers for Pulmonary Inflammation and Fibrosis and Lung Ventilation Function in Chinese Occupational Refractory Ceramic Fibers-Exposed Workers.
International journal of environmental research and public health
Dec 27, 2017
Refractory ceramic fibers (RCFs) can cause adverse health effects on workers' respiratory system, yet no proper biomarkers have been used to detect early pulmonary injury of RCFs-exposed workers. This study assessed the levels of two biomarkers that ...
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