AIMC Topic: Lung Diseases, Interstitial

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Value of CT quantification in progressive fibrosing interstitial lung disease: a deep learning approach.

European radiology
OBJECTIVES: To evaluate the relationship of changes in the deep learning-based CT quantification of interstitial lung disease (ILD) with changes in forced vital capacity (FVC) and visual assessments of ILD progression, and to investigate their progno...

U-net convolutional neural network applied to progressive fibrotic interstitial lung disease: Is progression at CT scan associated with a clinical outcome?

Respiratory medicine and research
BACKGROUND: Computational advances in artificial intelligence have led to the recent emergence of U-Net convolutional neural networks (CNNs) applied to medical imaging. Our objectives were to assess the progression of fibrotic interstitial lung disea...

Walking the path of treatable traits in interstitial lung diseases.

Respiratory research
Interstitial lung diseases (ILDs) are complex and heterogeneous diseases. The use of traditional diagnostic classification in ILD can lead to suboptimal management, which is worsened by not considering the molecular pathways, biological complexity, a...

A Deep Learning-Based Radiomic Classifier for Usual Interstitial Pneumonia.

Chest
BACKGROUND: Because chest CT scan has largely supplanted surgical lung biopsy for diagnosing most cases of interstitial lung disease (ILD), tools to standardize CT scan interpretation are urgently needed.

Development and validation of a CT-based deep learning algorithm to augment non-invasive diagnosis of idiopathic pulmonary fibrosis.

Respiratory medicine
RATIONALE: Non-invasive diagnosis of idiopathic pulmonary fibrosis (IPF) involves identification of usual interstitial pneumonia (UIP) pattern by computed tomography (CT) and exclusion of other known etiologies of interstitial lung disease (ILD). How...

External validation, radiological evaluation, and development of deep learning automatic lung segmentation in contrast-enhanced chest CT.

European radiology
OBJECTIVES: There is a need for CT pulmonary angiography (CTPA) lung segmentation models. Clinical translation requires radiological evaluation of model outputs, understanding of limitations, and identification of failure points. This multicentre stu...

The impact of deep learning reconstruction in low dose computed tomography on the evaluation of interstitial lung disease.

PloS one
To evaluate the effect of the deep learning model reconstruction (DLM) method in terms of image quality and diagnostic agreement in low-dose computed tomography (LDCT) for interstitial lung disease (ILD), 193 patients who underwent LDCT for suspected...

Deep Learning-Based CT Reconstruction Kernel Conversion in the Quantification of Interstitial Lung Disease: Effect on Reproducibility.

Academic radiology
RATIONALE AND OBJECTIVES: The effect of different computed tomography (CT) reconstruction kernels on the quantification of interstitial lung disease (ILD) has not been clearly demonstrated. The study aimed to investigate the effect of reconstruction ...

Computed Tomography-Based Deep Learning Model for Assessing the Severity of Patients With Connective Tissue Disease-Associated Interstitial Lung Disease.

Journal of computer assisted tomography
OBJECTIVES: This study aimed to develop a computed tomography (CT)-based deep learning model for assessing the severity of patients with connective tissue disease (CTD)-associated interstitial lung disease (ILD).

Leukocyte differentiation in bronchoalveolar lavage fluids using higher harmonic generation microscopy and deep learning.

PloS one
BACKGROUND: In diseases such as interstitial lung diseases (ILDs), patient diagnosis relies on diagnostic analysis of bronchoalveolar lavage fluid (BALF) and biopsies. Immunological BALF analysis includes differentiation of leukocytes by standard cyt...