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PURPOSE: To characterize the recovery of diagnostic cardiovascular procedure volumes in U.S. and non-U.S. facilities in the year following the initial COVID-19 outbreak. MATERIALS AND METHODS: The International Atomic Energy Agency (IAEA) coordinated a worldwide study called the IAEA Noninvasive Cardiology Protocols Study of COVID-19 2 (INCAPS COVID 2), collecting data from 669 facilities in 107 countries, including 93 facilities in 34 U.S. states, to determine the impact of the pandemic on diagnostic cardiovascular procedure volumes. Participants reported volumes for each diagnostic imaging modality used at their facility for March 2019 (baseline), April 2020, and April 2021. This secondary analysis of INCAPS COVID 2 evaluated differences in changes in procedure volume between U.S. and non-U.S. facilities and among U.S. regions. Factors associated with return to prepandemic volumes in the United States were also analyzed in a multivariable regression analysis. RESULTS: Reduction in procedure volumes in April 2020 compared with baseline was similar for U.S. and non-U.S. facilities (-66% vs -71%, P = .27). U.S. facilities reported greater return to baseline in April 2021 than did all non-U.S. facilities (4% vs -6%, P = .008), but there was no evidence of a difference when comparing U.S. facilities with non-U.S. high-income country (NUHIC) facilities (4% vs 0%, P = .18). U.S. regional differences in return to baseline were observed between the Midwest (11%), Northeast (9%), South (1%), and West (-7%, P = .03), but no studied factors were significant predictors of 2021 change from prepandemic baseline. CONCLUSION: The reductions in cardiac testing during the early pandemic have recovered within a year to prepandemic baselines in the United States and NUHICs, while procedure volumes remain depressed in lower-income countries.Keywords: SPECT, Cardiac, Epidemiology, Angiography, CT Angiography, CT, Echocardiography, SPECT/CT, MR Imaging, Radionuclide Studies, COVID-19, Cardiovascular Imaging, Diagnostic Cardiovascular Procedure, Cardiovascular Disease, Cardiac Testing Supplemental material is available for this article. © RSNA, 2023.

Amyloid transthyretin (ATTR) amyloidosis is a protein-misfolding disease characterized by fibril accumulation in the extracellular space that can result in local tissue disruption and organ dysfunction. Cardiac involvement drives morbidity and mortality, and the heart is the major organ affected by ATTR amyloidosis. Multimodality cardiac imaging (ie, echocardiography, scintigraphy, and cardiac magnetic resonance) allows accurate diagnosis of ATTR cardiomyopathy (ATTR-CM), and this is of particular importance because ATTR-targeting therapies have become available and probably exert their greatest benefit at earlier disease stages. Apart from establishing the diagnosis, multimodality cardiac imaging may help to better understand pathogenesis, predict prognosis, and monitor treatment response. The aim of this review is to give an update on contemporary and evolving cardiac imaging methods and their role in diagnosing and managing ATTR-CM. Further, an outlook is presented on how artificial intelligence in cardiac imaging may improve future clinical decision making and patient management in the setting of ATTR-CM. [Display omitted] • ATTR-CM is still an underrecognized entity, represented by accumulation of subtype-specific misfolded protein fibrils, which leads to progressive thickening of myocardial walls, impaired diastolic function, and restrictive CM, heart failure, and death. • Recent European and American guidelines promote noninvasive imaging in combination with laboratory testing to establish the diagnosis of ATTR-CM. • Future research in multimodality imaging holds the potential for deeper disease insight, treatment response monitoring, and improved outcome prediction. AI-based noninvasive image analysis and combining imaging and clinical data may provide more robust diagnostics, outcome prediction, and possible improvements of clinical decision making in the future. [ABSTRACT FROM AUTHOR]

Explainable artificial intelligence (AI) can be integrated within standard clinical software to facilitate the acceptance of the diagnostic findings during clinical interpretation. This study sought to develop and evaluate a novel, general purpose, explainable deep learning model (coronary artery disease–deep learning [CAD-DL]) for the detection of obstructive CAD following single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). A total of 3,578 patients with suspected CAD undergoing SPECT MPI and invasive coronary angiography within a 6-month interval from 9 centers were studied. CAD-DL computes the probability of obstructive CAD from stress myocardial perfusion, wall motion, and wall thickening maps, as well as left ventricular volumes, age, and sex. Myocardial regions contributing to the CAD-DL prediction are highlighted to explain the findings to the physician. A clinical prototype was integrated using a standard clinical workstation. Diagnostic performance by CAD-DL was compared to automated quantitative total perfusion deficit (TPD) and reader diagnosis. In total, 2,247 patients (63%) had obstructive CAD. In 10-fold repeated testing, the area under the receiver-operating characteristic curve (AUC) (95% CI) was higher according to CAD-DL (AUC: 0.83 [95% CI: 0.82-0.85]) than stress TPD (AUC: 0.78 [95% CI: 0.77-0.80]) or reader diagnosis (AUC: 0.71 [95% CI: 0.69-0.72]; P < 0.0001 for both). In external testing, the AUC in 555 patients was higher according to CAD-DL (AUC: 0.80 [95% CI: 0.76-0.84]) than stress TPD (AUC: 0.73 [95% CI: 0.69-0.77]) or reader diagnosis (AUC: 0.65 [95% CI: 0.61-0.69]; P < 0.001 for all). The present model can be integrated within standard clinical software and generates results rapidly (<12 seconds on a standard clinical workstation) and therefore could readily be incorporated into a typical clinical workflow. The deep-learning model significantly surpasses the diagnostic accuracy of standard quantitative analysis and clinical visual reading for MPI. Explainable artificial intelligence can be integrated within standard clinical software to facilitate acceptance of artificial intelligence diagnosis of CAD following MPI. [Display omitted] [ABSTRACT FROM AUTHOR]

This study sought to describe worldwide variations in the use of myocardial perfusion imaging hardware, software, and imaging protocols and their impact on radiation effective dose (ED). Concerns about long-term effects of ionizing radiation have prompted efforts to identify strategies for dose optimization in myocardial perfusion scintigraphy. Studies have increasingly shown opportunities for dose reduction using newer technologies and optimized protocols. Data were submitted voluntarily to the INCAPS (International Atomic Energy Agency Nuclear Cardiology Protocols Study) registry, a multinational, cross-sectional study comprising 7,911 imaging studies from 308 labs in 65 countries. The study compared regional use of camera technologies, advanced post-processing software, and protocol characteristics and analyzed the influence of each factor on ED. Cadmium-zinc-telluride and positron emission tomography (PET) cameras were used in 10% (regional range 0% to 26%) and 6% (regional range 0% to 17%) of studies worldwide. Attenuation correction was used in 26% of cases (range 10% to 57%), and advanced post-processing software was used in 38% of cases (range 26% to 64%). Stress-first single-photon emission computed tomography (SPECT) imaging comprised nearly 20% of cases from all world regions, except North America, where it was used in just 7% of cases. Factors associated with lower ED and odds ratio for achieving radiation dose ≤9 mSv included use of cadmium-zinc-telluride, PET, advanced post-processing software, and stress- or rest-only imaging. Overall, 39% of all studies (97% PET and 35% SPECT) were ≤9 mSv, while just 6% of all studies (32% PET and 4% SPECT) achieved a dose ≤3 mSv. Newer-technology cameras, advanced software, and stress-only protocols were associated with reduced ED, but worldwide adoption of these practices was generally low and varied significantly between regions. The implementation of dose-optimizing technologies and protocols offers an opportunity to reduce patient radiation exposure across all world regions. [Display omitted] [ABSTRACT FROM AUTHOR]