Hoconrad9338

for this article. © RSNA, 2022.

To develop and validate a deep learning-based system that predicts the largest ascending and descending aortic diameters at chest CT through automatic thoracic aortic segmentation and identifies aneurysms in each segment.

In this retrospective study conducted from July 2019 to February 2021, a U-Net and a postprocessing algorithm for thoracic aortic segmentation and measurement were developed by using a dataset (dataset A) that included 315 CT studies split into training, hyperparameter-tuning, and testing sets. The U-Net and postprocessing algorithm were associated with a Digital Imaging and Communications in Medicine series filter and visualization interface and were further validated by using a dataset (dataset B) that included 1400 routine CT studies. In dataset B, system-predicted measurements were compared with annotations made by two independent readers as well as radiology reports to evaluate system performance.

In dataset B, the mean absolute error between the automatic and reader-measured diameters was equal to or less than 0.27 cm for both the ascending aorta and the descending aorta. The intraclass correlation coefficients (ICCs) were greater than 0.80 for the ascending aorta and equal to or greater than 0.70 for the descending aorta, and the ICCs between readers were 0.91 (95% CI 0.90, 0.92) and 0.82 (95% CI 0.80, 0.84), respectively. Aneurysm detection accuracy was 88% (95% CI 86, 90) and 81% (95% CI 79, 83) compared with reader 1 and 90% (95% CI 88, 91) and 82% (95% CI 80, 84) compared with reader 2 for the ascending aorta and descending aorta, respectively.

Thoracic aortic aneurysms were accurately predicted at CT by using deep learning.

Aorta, Convolutional Neural Network, Machine Learning, CT, Thorax, Aneurysms

.© RSNA, 2022.

Thoracic aortic aneurysms were accurately predicted at CT by using deep learning.Keywords Aorta, Convolutional Neural Network, Machine Learning, CT, Thorax, AneurysmsSupplemental material is available for this article.© RSNA, 2022.Quantitative imaging measurements can be facilitated by artificial intelligence (AI) algorithms, but how they might impact decision-making and be perceived by radiologists remains uncertain. After creation of a dedicated inspiratory-expiratory CT examination and concurrent deployment of a quantitative AI algorithm for assessing air trapping, five cardiothoracic radiologists retrospectively evaluated severity of air trapping on 17 examination studies. Air trapping severity of each lobe was evaluated in three stages qualitatively (visually); semiquantitatively, allowing manual region-of-interest measurements; and quantitatively, using results from an AI algorithm. Readers were surveyed on each case for their perceptions of the AI algorithm. The algorithm improved interreader agreement (intraclass correlation coefficients visual, 0.28; semiquantitative, 0.40; quantitative, 0.84; P less then .001) and improved correlation with pulmonary function testing (forced expiratory volume in 1 second-to-forced vital capacity ratio) (visual r = -0.26, semiquantitative r = -0.32, quantitative r = -0.44). Readers perceived moderate agreement with the AI algorithm (Likert scale average, 3.7 of 5), a mild impact on their final assessment (average, 2.6), and a neutral perception of overall utility (average, 3.5). Though the AI algorithm objectively improved interreader consistency and correlation with pulmonary function testing, individual readers did not immediately perceive this benefit, revealing a potential barrier to clinical adoption. Keywords Technology Assessment, Quantification © RSNA, 2021.Mammographic breast density (BD) is commonly visually assessed using the Breast Imaging Reporting and Data System (BI-RADS) four-category scale. To overcome inter- and intraobserver variability of visual assessment, the authors retrospectively developed and externally validated a software for BD classification based on convolutional neural networks from mammograms obtained between 2017 and 2020. The tool was trained using the majority BD category determined by seven board-certified radiologists who independently visually assessed 760 mediolateral oblique (MLO) images in 380 women (mean age, 57 years ± 6 [SD]) from center 1; this process mimicked training from a consensus of several human readers. External validation of the model was performed by the three radiologists whose BD assessment was closest to the majority (consensus) of the initial seven on a dataset of 384 MLO images in 197 women (mean age, 56 years ± 13) obtained from center 2. The model achieved an accuracy of 89.3% in distinguishing BI-RADS a or b (nondense breasts) versus c or d (dense breasts) categories, with an agreement of 90.4% (178 of 197 mammograms) and a reliability of 0.807 (Cohen κ) compared with the mode of the three readers. This study demonstrates accuracy and reliability of a fully automated software for BD classification. Keywords Mammography, Breast, Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning Algorithms Supplemental material is available for this article. © RSNA, 2022.Artificial intelligence (AI)-based image enhancement has the potential to reduce scan times while improving signal-to-noise ratio (SNR) and maintaining spatial resolution. This study prospectively evaluated AI-based image enhancement in 32 consecutive patients undergoing clinical brain MRI. Standard-of-care (SOC) three-dimensional (3D) T1 precontrast, 3D T2 fluid-attenuated inversion recovery, and 3D T1 postcontrast sequences were performed along with 45% faster versions of these sequences using half the number of phase-encoding steps. Images from the faster sequences were processed by a Food and Drug Administration-cleared AI-based image enhancement software for resolution enhancement. Four board-certified neuroradiologists scored the SOC and AI-enhanced image series independently on a five-point Likert scale for image SNR, anatomic conspicuity, overall image quality, imaging artifacts, and diagnostic confidence. While interrater κ was low to fair, the AI-enhanced scans were noninferior for all metrics and actually demonstrated a qualitative SNR improvement. Quantitative analyses showed that the AI software restored the high spatial resolution of small structures, such as the septum pellucidum. In conclusion, AI-based software can achieve noninferior image quality for 3D brain MRI sequences with a 45% scan time reduction, potentially improving the patient experience and scanner efficiency without sacrificing diagnostic quality. Keywords MR Imaging, CNS, Brain/Brain Stem, Reconstruction Algorithms © RSNA, 2022.

To develop a model to accurately segment mouse lungs with varying levels of fibrosis and investigate its applicability to mouse images with different resolutions.

In this experimental retrospective study, a U-Net was trained to automatically segment lungs on mouse CT images. The model was trained (

= 1200), validated (

= 300), and tested (

= 154) on longitudinally acquired and semiautomatically segmented CT images, which included both healthy and irradiated mice (group A). A second independent group of 237 mice (group B) was used for external testing. The Dice score coefficient (DSC) and Hausdorff distance (HD) were used as metrics to quantify segmentation accuracy. Transfer learning was applied to adapt the model to high-spatial-resolution mouse micro-CT segmentation (

= 20; group C [

= 16 for training and

= 4 for testing]).

The trained model yielded a high median DSC in both test datasets 0.984 (interquartile range [IQR], 0.977-0.988) in group A and 0.966 (IQR, 0.955-0.972) in group B. Thimal Studies, CT, Thorax, Lung Supplemental material is available for this article. Published under a CC BY 4.0 license.

To automatically identify a cohort of patients with pancreatic cystic lesions (PCLs) and extract PCL measurements from historical CT and MRI reports using natural language processing (NLP) and a question answering system.

Institutional review board approval was obtained for this retrospective Health Insurance Portability and Accountability Act-compliant study, and the requirement to obtain informed consent was waived. Triapine order A cohort of free-text CT and MRI reports generated between January 1991 and July 2019 that covered the pancreatic region were identified. A PCL identification model was developed by modifying a rule-based information extraction model; measurement extraction was performed using a state-of-the-art question answering system. The system's performance was evaluated against radiologists' annotations.

For this study, 430 426 free-text radiology reports from 199 783 unique patients were identified. The NLP model for identifying PCL was applied to 1000 test samples. The interobserver agreement betwtext radiology reports. This approach may prove valuable to study the natural history and potential risks of PCLs and can be applied to many other use cases.Keywords Informatics, Abdomen/GI, Pancreas, Cysts, Computer Applications-General (Informatics), Named Entity Recognition Supplemental material is available for this article. © RSNA, 2022See also commentary by Horii in this issue.

The influence of Coronavirus disease 2019 (COVID-19) pandemic on mental health has been widely studied; however, literature evaluating the mental health effects of the pandemic on small groups of people is scarce. We aim to evaluate the impact of the COVID-19 pandemic on anxiety levels of anesthesiology providers in an academic institution.

We conducted a cross-sectional study including one hundred and five participants (Faculty anesthesiologists, anesthesia residents, certified registered and student nurse anesthetists). The generalized anxiety disorder questionnaire (GAD-7) was administered to participants.

Approximately half of the 105 participants experienced various degrees of anxiety, with only 14.3% exhibiting moderate to severe symptoms of anxiety. Anxiety interfering with daily activities was reported in 54.9% of the participants. Anxiety-generating factors such as access to protective equipment and transmitting the disease to family members were identified.

The COVID-19 pandemic is associated with different degrees of anxiety. The prevalence of severe anxiety is relatively low, probably due to differential individual perceptions, feelings of invulnerability, and resilience of anesthesia providers.

The COVID-19 pandemic is associated with different degrees of anxiety. The prevalence of severe anxiety is relatively low, probably due to differential individual perceptions, feelings of invulnerability, and resilience of anesthesia providers.

The study had three primary goals. First, we estimated survey-assessed DSM-5 insomnia disorder rates in pregnancy, and described associated sociodemographics, and sleep-wake and mental health symptoms. Second, we derived cutoffs for detecting DSM-5 insomnia disorder using common self-report measures of sleep symptoms. Third, we identified clinically relevant cut-points on measures of nocturnal cognitive and somatic arousal.

Ninety-nine women (85.9% in the 2

trimester) completed online surveys including DSM-5 insomnia disorder criteria, the Insomnia Severity Index (ISI), Pittsburgh Sleep Quality Index (PSQI), Presleep Arousal Scale's Cognitive (PSASC) and Somatic (PSASS) factors, and Edinburgh Postnatal Depression Scale.

DSM-5 insomnia disorder rate was 19.2%. Insomnia was associated with depression, suicidality, nocturnal cognitive and somatic arousal, and daytime sleepiness. An ISI scoring method that aligns with DSM-5 criteria yielded excellent metrics for detecting insomnia disorder and good sleep.