4,258 research outputs found

    Quantitative CT analysis in ILD and use of artificial intelligence on imaging of ILD

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    Advances in computer technology over the past decade, particularly in the field of medical image analysis, have permitted the identification, characterisation and quantitation of abnormalities that can be used to diagnose disease or determine disease severity. On CT imaging performed in patients with ILD, deep-learning computer algorithms now demonstrate comparable performance with trained observers in the identification of a UIP pattern, which is associated with a poor prognosis in several fibrosing ILDs. Computer tools that quantify individual voxel-level CT features have also come of age and can predict mortality with greater power than visual CT analysis scores. As these tools become more established, they have the potential to improve the sensitivity with which minor degrees of disease progression are identified. Currently, PFTs are the gold standard measure used to assess clinical deterioration. However, the variation associated with pulmonary function measurements may mask the presence of small but genuine functional decline, which in the future could be confirmed by computer tools. The current chapter will describe the latest advances in quantitative CT analysis and deep learning as related to ILDs and suggest potential future directions for this rapidly advancing field

    Multimodality Imaging in Connective Tissue Disease-Related Interstitial Lung Disease

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    Interstitial lung disease is a well-recognised manifestation and a major cause of morbidity and mortality in patients with connective tissue diseases. Interstitial lung disease may arise in the context of an established connective tissue disease or be the initial manifestation of an otherwise occult autoimmune disorder. Early detection and characterisation are paramount for adequate patient management and require a multidisciplinary approach, in which imaging plays a vital role. Computed tomography is currently the imaging method of choice; however, other imaging techniques have recently been investigated, namely ultrasound, magnetic resonance imaging, and positron-emission tomography, with promising results. The aim of this review is to describe the imaging findings of connective tissue disease-related interstitial lung disease and explain the role of each imaging technique in diagnosis and disease characterisation.info:eu-repo/semantics/publishedVersio

    A Survey on Deep Learning in Medical Image Analysis

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    Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

    Diagnosis and monitoring of systemic sclerosis-associated interstitial lung disease using high-resolution computed tomography

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    Patients with systemic sclerosis are at high risk of developing systemic sclerosis-associated interstitial lung disease. Symptoms and outcomes of systemic sclerosis-associated interstitial lung disease range from subclinical lung involvement to respiratory failure and death. Early and accurate diagnosis of systemic sclerosis-associated interstitial lung disease is therefore important to enable appropriate intervention. The most sensitive and specific way to diagnose systemic sclerosis-associated interstitial lung disease is by high-resolution computed tomography, and experts recommend that high-resolution computed tomography should be performed in all patients with systemic sclerosis at the time of initial diagnosis. In addition to being an important screening and diagnostic tool, high-resolution computed tomography can be used to evaluate disease extent in systemic sclerosis-associated interstitial lung disease and may be helpful in assessing prognosis in some patients. Currently, there is no consensus with regards to frequency and scanning intervals in patients at risk of interstitial lung disease development and/or progression. However, expert guidance does suggest that frequency of screening using high-resolution computed tomography should be guided by risk of developing interstitial lung disease. Most experienced clinicians would not repeat high-resolution computed tomography more than once a year or every other year for the first few years unless symptoms arose. Several computed tomography techniques have been developed in recent years that are suitable for regular monitoring, including low-radiation protocols, which, together with other technologies, such as lung ultrasound and magnetic resonance imaging, may further assist in the evaluation and monitoring of patients with systemic sclerosis-associated interstitial lung disease. A video abstract to accompany this article is available at: https://www.globalmedcomms.com/respiratory/Khanna/HRCTinSScILD

    Computer-aided detection of interstitial lung diseases: A texture approach

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    We have developed the flexible scheme for computer-aided detection (CAD) of interstitial lung diseases on chest radiographs. These schemes enable us to perform diagnostics in the broad circumstances of pneumonia and other interstitial lung diseases. It is applied in the case of children pneumonia when conditions are difficult to standardize. In the adults' case the schemes of CAD are more adaptive, as there are more characteristic interstitial lung tissue's changes to all kinds of pathological conditions. Even in the norm of drawing there are more visible and more highlighted features, leading to better results. The CAD scheme works as follows. For the first of all, we are using adopted algorithms of active contours to select the area of lungs, and then to divide this area into subareas - regions of interest (40 different ROI). Then ROIs were subjected to the 2-dimensional Daubechies wavelet transform, and only main transformation was used. For every transformation 12 texture measures were calculated. Principal component analysis (PCA) was used to extract 2 main components for each ROI, and these components were compared to predictive component region

    Artificial Intelligence and Interstitial Lung Disease: Diagnosis and Prognosis.

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    Interstitial lung disease (ILD) is now diagnosed by an ILD-board consisting of radiologists, pulmonologists, and pathologists. They discuss the combination of computed tomography (CT) images, pulmonary function tests, demographic information, and histology and then agree on one of the 200 ILD diagnoses. Recent approaches employ computer-aided diagnostic tools to improve detection of disease, monitoring, and accurate prognostication. Methods based on artificial intelligence (AI) may be used in computational medicine, especially in image-based specialties such as radiology. This review summarises and highlights the strengths and weaknesses of the latest and most significant published methods that could lead to a holistic system for ILD diagnosis. We explore current AI methods and the data use to predict the prognosis and progression of ILDs. It is then essential to highlight the data that holds the most information related to risk factors for progression, e.g., CT scans and pulmonary function tests. This review aims to identify potential gaps, highlight areas that require further research, and identify the methods that could be combined to yield more promising results in future studies
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