Publications by authors named "J A Verschakelen"
Members of the Fleischner Society have compiled a glossary of terms for thoracic imaging that replaces previous glossaries published in 1984, 1996, and 2008, respectively. The impetus to update the previous version arose from multiple considerations. These include an awareness that new terms and concepts have emerged, others have become obsolete, and the usage of some terms has either changed or become inconsistent to a degree that warranted a new definition.
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- - The assessment of donor lungs for transplantation is mostly subjective and varies greatly, lacking standardized criteria; researchers explored using a CT-based machine learning algorithm to evaluate donor lungs before surgery.
- - The study collected clinical data and CT scans from 100 cases, training a machine learning method called dictionary learning to identify specific image patterns related to lung health.
- - The algorithm successfully detected lung abnormalities, highlighting patients with a higher risk of complications post-transplant and emphasizing the need for objective screening methods as the use of less-than-ideal donor lungs increases.
Background: Assessment and selection of donor lungs remains largely subjective and experience based. Criteria to accept or decline lungs are poorly standardized and are not compliant with the current donor pool. Using ex vivo CT images, we investigated the use of a CT-based machine learning algorithm for screening donor lungs prior to transplantation.
View Article and Find Full Text PDFBackground Interstitial lung abnormalities (ILAs) reflect imaging features on lung CT scans that are compatible with (early) interstitial lung disease. Despite accumulating evidence regarding the incidence, risk factors, and prognosis of ILAs, the histopathologic correlates of ILAs remain elusive. Purpose To determine the correlation between radiologic and histopathologic findings in CT-defined ILAs in human lung explants.
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