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Your search keyword '"Andrea S. Oh"' showing total 4 results
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4 results on '"Andrea S. Oh"'

1. Spatial resolution enhancement using deep learning improves chest disease diagnosis based on thick slice CT

Author

Pengxin Yu, Haoyue Zhang, Dawei Wang, Rongguo Zhang, Mei Deng, Haoyu Yang, Lijun Wu, Xiaoxu Liu, Andrea S. Oh, Fereidoun G. Abtin, Ashley E. Prosper, Kathleen Ruchalski, Nana Wang, Huairong Zhang, Ye Li, Xinna Lv, Min Liu, Shaohong Zhao, Dasheng Li, John M. Hoffman, Denise R. Aberle, Chaoyang Liang, Shouliang Qi, and Corey Arnold

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

Abstract CT is crucial for diagnosing chest diseases, with image quality affected by spatial resolution. Thick-slice CT remains prevalent in practice due to cost considerations, yet its coarse spatial resolution may hinder accurate diagnoses. Our multicenter study develops a deep learning synthetic model with Convolutional-Transformer hybrid encoder-decoder architecture for generating thin-slice CT from thick-slice CT on a single center (1576 participants) and access the synthetic CT on three cross-regional centers (1228 participants). The qualitative image quality of synthetic and real thin-slice CT is comparable (p = 0.16). Four radiologists’ accuracy in diagnosing community-acquired pneumonia using synthetic thin-slice CT surpasses thick-slice CT (p 0.99). For lung nodule detection, sensitivity with thin-slice CT outperforms thick-slice CT (p 0.05). These findings indicate the potential of our model to generate high-quality synthetic thin-slice CT as a practical alternative when real thin-slice CT is preferred but unavailable.

Published
2024
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2. Quantitative imaging analysis detects subtle airway abnormalities in symptomatic military deployers

Author

Lauren M. Zell-Baran, Stephen M. Humphries, Camille M. Moore, David A. Lynch, Jean-Paul Charbonnier, Andrea S. Oh, and Cecile S. Rose

Subjects
Military deployment, HRCT, Quantitative image analysis, Emphysema, Bronchial wall thickening, Air trapping, Diseases of the respiratory system, RC705-779
Abstract

Abstract Background Exposure to inhalational hazards during post-9/11 deployment to Southwest Asia and Afghanistan puts military personnel at risk for respiratory symptoms and disease. Pulmonary function and qualitative chest high resolution computed tomography (HRCT) are often normal in “deployers” with persistent respiratory symptoms. We explored the utility of quantitative HRCT imaging markers of large and small airways abnormalities, including airway wall thickness, emphysema, and air trapping, in symptomatic deployers with clinically-confirmed lung disease compared to controls. Methods Chest HRCT images from 45 healthy controls and 82 symptomatic deployers with asthma, distal lung disease or both were analyzed using Thirona Lung quantification software to calculate airway wall thickness (by Pi10), emphysema (by percentage of lung volume with attenuation

Published
2022
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3. Emphysema Progression at CT by Deep Learning Predicts Functional Impairment and Mortality: Results from the COPDGene Study

Author

Andrea S, Oh, David, Baraghoshi, David A, Lynch, Samuel Y, Ash, James D, Crapo, and Stephen M, Humphries

Subjects
Emphysema, Male, Middle Aged, Severity of Illness Index, Pulmonary Disease, Chronic Obstructive, Deep Learning, Pulmonary Emphysema, Humans, Female, Radiology, Nuclear Medicine and imaging, Prospective Studies, Tomography, X-Ray Computed, Lung, Aged
Abstract

Background Visual assessment remains the standard for evaluating emphysema at CT; however, it is time consuming, is subjective, requires training, and is affected by variability that may limit sensitivity to longitudinal change. Purpose To evaluate the clinical and imaging significance of increasing emphysema severity as graded by a deep learning algorithm on sequential CT scans in cigarette smokers. Materials and Methods A secondary analysis of the prospective Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPDGene) study participants was performed and included baseline and 5-year follow-up CT scans from 2007 to 2017. Emphysema was classified automatically according to the Fleischner emphysema grading system at baseline and 5-year follow-up using a deep learning model. Baseline and change in clinical and imaging parameters at 5-year follow-up were compared in participants whose emphysema progressed versus those who did not. Kaplan-Meier analysis and multivariable Cox regression were used to assess the relationship between emphysema score progression and mortality. Results A total of 5056 participants (mean age, 60 years ± 9 [SD]; 2566 men) were evaluated. At 5-year follow-up, 1293 of the 5056 participants (26%) had emphysema progression according to the Fleischner grading system. This group demonstrated progressive airflow obstruction (forced expiratory volume in 1 second [percent predicted]: -3.4 vs -1.8), a greater decline in 6-minute walk distance (-177 m vs -124 m), and greater progression in quantitative emphysema extent (adjusted lung density: -1.4 g/L vs 0.5 g/L; percentage of lung voxels with CT attenuation less than -950 HU: 0.6 vs 0.2) than those with nonprogressive emphysema (

Published
2022

4. Interstitial Lung Abnormality-Why Should I Care and What Should I Do About It?

Author

Andrea S. Oh and David A. Lynch

Subjects
Lung Neoplasms, Humans, Radiology, Nuclear Medicine and imaging, General Medicine, Respiratory System Abnormalities, Lung Diseases, Interstitial, Tomography, X-Ray Computed, Lung, Early Detection of Cancer
Abstract

Interstitial lung abnormalities (ILAs) are specific computed tomography (CT) findings that are potentially compatible with interstitial lung disease (ILD) in patients without clinical suspicion for disease. ILAs are associated with adverse clinical outcomes including increased mortality, imaging progression and lung function decline, and increased lung injury risk with lung cancer therapies. It is expected that identification of ILAs will increase with implementation of lung cancer screening and diagnostic CT imaging for workup of other pathologies. As such, radiologists will play a critical role in the diagnosis and management of ILAs.

Published
2022

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