Smoking-Related Interstitial Lung Disease and Emphysema.

Diagnosis and treatment of patients with smoking-related lung diseases often requires multidisciplinary contributions to optimize care. Imaging plays a key role in characterizing the underlying disease, quantifying its severity, identifying potential complications, and directing management. The primary goal of this article is to provide an overview of the imaging findings and distinguishing features of smoking-related lung diseases, specifically, emphysema/chronic obstructive pulmonary disease, respiratory bronchiolitis-interstitial lung disease, smoking-related interstitial fibrosis, desquamative interstitial pneumonitis, combined pulmonary fibrosis and emphysema, pulmonary Langerhans cell histiocytosis, and E-cigarette or vaping related lung injury.

Algorithmic approach in the management of COVID-19 patients with residual pulmonary symptoms.

Coronavirus-19 emerged about 3 years ago and has proven to be a devastating disease, crippling communities worldwide and accounting for more than 6.31 million deaths. The true disease burden of COVID-19 will come to light in the upcoming years as we care for COVID-19 survivors with post-COVID-19 syndrome (PCS) with residual long-term symptoms affecting every organ system.

Practice patterns in reporting interstitial lung abnormality at a tertiary academic medical center.

Purpose: Interstitial lung abnormality (ILA) is a common finding on chest CTs and is associated with higher all-cause mortality. The 2020 Fleischner Society position paper standardized the terminology and definition of ILA. Despite these published guidelines, the extent to which radiologists use this term is unknown.

How Does Pruning Impact Long-Tailed Multi-label Medical Image Classifiers?

Pruning has emerged as a powerful technique for compressing deep neural networks, reducing memory usage and inference time without significantly affecting overall performance. However, the nuanced ways in which pruning impacts model behavior are not well understood, particularly for , datasets commonly found in clinical settings. This knowledge gap could have dangerous implications when deploying a pruned model for diagnosis, where unexpected model behavior could impact patient well-being.