Transient left bundle branch block associated with very high coronary artery calcium: a case report.

Coronary artery calcium (CAC) is the measure of subclinical coronary artery atherosclerosis most strongly associated with atherosclerotic cardiovascular disease (ASCVD) risk. However, CAC is rarely reported in the inpatient setting to guide chest pain management. We present a case of very high CAC in a 64-year-old woman with hypertension, type 2 diabetes, and hyperlipidemia presenting with dyspnea. Initial electrocardiogram (ECG) demonstrated normal conduction with a heart rate of 76 beats/min, but new T-wave inversions in V1-V4 and a high-sensitivity troponin-I (hsTnI) value of 6 ng/L (normal < 6 ng/L). Repeat ECG in the emergency department showed normal sinus rhythm (heart rate of 80 beats/min); however, it subsequently demonstrated a left bundle branch block (LBBB) with a repeat hsTnI of 7 ng/L. Stress testing with pharmacologic single-photon emission computerized tomography did not show scintigraphic evidence of ischemia but noted extensive CAC and a concern for balanced ischemia. Subsequent coronary computed tomography angiography (CCTA) showed nonobstructive disease and a total Agatston CAC score of 1262. Invasive evaluation with left heart catheterization was deferred given the patient's unchanged symptoms and CCTA findings. Statin therapy was intensified and aspirin, metoprolol succinate, and antihypertension therapies were continued. Initiation of glucose-lowering therapy and lipoprotein(a) testing was strongly recommended on follow-up. Our case suggests that CAC ⩾ 1000 may be incidentally associated with transient LBBB during the workup of coronary artery disease. Here, we specifically show that functional testing that incorporates measurement of CAC burden can help to improve ASCVD-preventive pharmacotherapy initiation and intensification beyond the identification of obstructive disease alone.

Detecting COVID-19 from chest computed tomography scans using AI-driven android application.

The COVID-19 (coronavirus disease 2019) pandemic affected more than 186 million people with over 4 million deaths worldwide by June 2021. The magnitude of which has strained global healthcare systems. Chest Computed Tomography (CT) scans have a potential role in the diagnosis and prognostication of COVID-19. Designing a diagnostic system, which is cost-efficient and convenient to operate on resource-constrained devices like mobile phones would enhance the clinical usage of chest CT scans and provide swift, mobile, and accessible diagnostic capabilities. This work proposes developing a novel Android application that detects COVID-19 infection from chest CT scans using a highly efficient and accurate deep learning algorithm. It further creates an attention heatmap, augmented on the segmented lung parenchyma region in the chest CT scans which shows the regions of infection in the lungs through an algorithm developed as a part of this work, and verified through radiologists. We propose a novel selection approach combined with multi-threading for a faster generation of heatmaps on a Mobile Device, which reduces the processing time by about 93%. The neural network trained to detect COVID-19 in this work is tested with a F1 score and accuracy, both of 99.58% and sensitivity of 99.69%, which is better than most of the results in the domain of COVID diagnosis from CT scans. This work will be beneficial in high-volume practices and help doctors triage patients for the early diagnosis of COVID-19 quickly and efficiently.

AI-driven quantification of ground glass opacities in lungs of COVID-19 patients using 3D computed tomography imaging.

OBJECTIVES: Ground-glass opacity (GGO)-a hazy, gray appearing density on computed tomography (CT) of lungs-is one of the hallmark features of SARS-CoV-2 in COVID-19 patients. This AI-driven study is focused on segmentation, morphology, and distribution patterns of GGOs. METHOD: We use an AI-driven unsupervised machine learning approach called PointNet++ to detect and quantify GGOs in CT scans of COVID-19 patients and to assess the severity of the disease. We have conducted our study on the "MosMedData", which contains CT lung scans of 1110 patients with or without COVID-19 infections. We quantify the morphologies of GGOs using Minkowski tensors and compute the abnormality score of individual regions of segmented lung and GGOs. RESULTS: PointNet++ detects GGOs with the highest evaluation accuracy (98%), average class accuracy (95%), and intersection over union (92%) using only a fraction of 3D data. On average, the shapes of GGOs in the COVID-19 datasets deviate from sphericity by 15% and anisotropies in GGOs are dominated by dipole and hexapole components. These anisotropies may help to quantitatively delineate GGOs of COVID-19 from other lung diseases. CONCLUSION: The PointNet++ and the Minkowski tensor based morphological approach together with abnormality analysis will provide radiologists and clinicians with a valuable set of tools when interpreting CT lung scans of COVID-19 patients. Implementation would be particularly useful in countries severely devastated by COVID-19 such as India, where the number of cases has outstripped available resources creating delays or even breakdowns in patient care. This AI-driven approach synthesizes both the unique GGO distribution pattern and severity of the disease to allow for more efficient diagnosis, triaging and conservation of limited resources.

AI-DRIVEN QUANTIFICATION OF GROUND GLASS OPACITIES IN LUNGS OF COVID-19 PATIENTS USING 3D COMPUTED TOMOGRAPHY IMAGING.

OBJECTIVES: Ground-glass opacity (GGO) - a hazy, gray appearing density on computed tomography (CT) of lungs - is one of the hallmark features of SARS-CoV-2 in COVID-19 patients. This AI-driven study is focused on segmentation, morphology, and distribution patterns of GGOs. METHOD: We use an AI-driven unsupervised machine learning approach called PointNet++ to detect and quantify GGOs in CT scans of COVID-19 patients and to assess the severity of the disease. We have conducted our study on the "MosMedData", which contains CT lung scans of 1110 patients with or without COVID-19 infections. We quantify the morphologies of GGOs using Minkowski tensors and compute the abnormality score of individual regions of segmented lung and GGOs. RESULTS: PointNet++ detects GGOs with the highest evaluation accuracy (98%), average class accuracy (95%), and intersection over union (92%) using only a fraction of 3D data. On average, the shapes of GGOs in the COVID-19 datasets deviate from sphericity by 15% and anisotropies in GGOs are dominated by dipole and hexapole components. These anisotropies may help to quantitatively delineate GGOs of COVID-19 from other lung diseases. CONCLUSION: The PointNet++ and the Minkowski tensor based morphological approach together with abnormality analysis will provide radiologists and clinicians with a valuable set of tools when interpreting CT lung scans of COVID-19 patients. Implementation would be particularly useful in countries severely devastated by COVID-19 such as India, where the number of cases has outstripped available resources creating delays or even breakdowns in patient care. This AI-driven approach synthesizes both the unique GGO distribution pattern and severity of the disease to allow for more efficient diagnosis, triaging and conservation of limited resources. KEY POINTS: Our approach to GGO analysis has four distinguishing features:We combine an unsupervised computer vision approach with convex hull and convex points algorithms to segment and preserve the actual structure of the lung.To the best of our knowledge, we are the first group to use PointNet++ architecture for 3D visualization, segmentation, classification, and pattern analysis of GGOs.We make abnormality predictions using a deep network and Cox proportional hazards model using lung CT images of COVID-19 patients.We quantify the shapes and sizes of GGOs using Minkowski tensors to understand the morphological variations of GGOs within the COVID-19 cohort.

Season and clinical factors influence epicardial adipose tissue attenuation measurement on computed tomography and may hamper its utilization as a risk marker.

BACKGROUND AND AIMS: A small difference in epicardial adipose tissue (EAT) attenuation measured on computed tomography (CT) imaging has been reported between patients who suffered coronary events and event-free patients. EAT consists of beige adipose tissue functionally similar to brown adipose tissue and its attenuation may be affected by seasonal temperature variations and clinical factors. METHODS: We retrospectively measured EAT attenuation on cardiac CT in 597 patients submitted to cardiac CT imaging for coronary artery calcium scoring. All scans were performed on the same CT scanner during the summer (June, July, August) or winter (December, January, February) months. EAT attenuation in Hounsfield units (HU) was assessed near the proximal right coronary artery in an area free of artifacts. For comparison, subcutaneous adipose tissue (SCAT) attenuation was measure along the midaxillary line. RESULTS: The clinical and demographic characteristics of patients scanned during the summer (N = 253) and the winter (N = 344) months were similar. One third of patients were women, one quarter used statins and anti-hypertensive drugs and 30% were obese. The EAT attenuation was significantly lower during the summer than the winter months (-98.17 ± 6.94 HUs vs -95.64 ± 7.99 HUs; p<0.001). Sex, white race, body mass index, diabetes status, treatment with statins and anti-hypertensive agents significantly modulated the seasonal variation in EAT attenuation. SCAT attenuation was not affected by season or other factors. CONCLUSIONS: The measurement of EAT attenuation is complex and is affected by season, demographic and clinical factors. These factors may hinder the utilization of EAT attenuation as a biomarker of cardiovascular risk.

Interobserver Reliability of the Coronary Artery Disease Reporting and Data System in Clinical Practice.

PURPOSE: This study aimed to evaluate interobserver reproducibility between cardiothoracic radiologists applying the Coronary Artery Disease Reporting and Data System (CAD-RADS) to describe atherosclerotic burden on coronary computed tomography angiography. METHODS: Forty clinical computed tomography angiography cases were retrospectively and independently evaluated by 3 attending and 2 fellowship-trained cardiothoracic radiologists using the CAD-RADS lexicon. Radiologists were blinded to patient history and underwent initial training using a practice set of 10 subjects. Interobserver reproducibility was assessed using an intraclass correlation (ICC) on the basis of single-observer scores, absolute agreement, and a 2-way random-effects model. Nondiagnostic studies were excluded. ICC was also performed for CAD-RADS scores grouped by management recommendations for absent (0), nonobstructive (1 to 2), and potentially obstructive (3 to 5) CAD. RESULTS: Interobserver reproducibility was moderate to good (ICC: 0.748, 95% confidence interval [CI]: 0.639-0.842, P<0.0001), with higher agreement among cardiothoracic radiology fellows (ICC: 0.853, 95% CI: 0.730-0.922, P<0.0001) than attending radiologists (ICC: 0.711, 95% CI: 0.568-0.824, P<0.0001). Interobserver reproducibility for clinical management categories was marginally decreased (ICC: 0.692, 95% CI: 0.570-0.802, P<0.0001). The average percent agreement between pairs of radiologists was 84.74%. Percent observer agreement was significantly reduced in the presence (M=62.22%, SD=15.17%) versus the absence (M=80.91%, SD=17.97%) of modifiers, t(37.95)=3.566, P=0.001. CONCLUSIONS: Interobserver reliability and agreement with the CAD-RADS terminology are moderate to good in clinical practice. However, further investigations are needed to characterize the causes of interobserver disagreement that may lead to differences in management recommendations.

SCOT-HEART trial: reshuffling our approach to stable ischemic heart disease.

The role of diagnostic testing in triaging patients with stable ischemic heart disease continues to evolve towards recognizing the benefits of coronary CT angiography (CCTA) over functional testing. The SCOT-HEART (Scottish Computed Tomography of the HEART) trial highlights this paradigm shift finding a significant reduction of death from coronary heart disease or non-fatal myocardial infarction without a significant increased rate of invasive coronary angiography over a 5 year follow-up period when implementing CCTA with standard care vs standard care alone. The better negative predictive value and ability to identify nonobstructive coronary artery disease to optimize medical therapy highlight the benefits of a CCTA first strategy. With the advent of noninvasive fractional flow reserve (FFR) and widespread availability and ease of CT, CCTA continues to establish itself as a pivotal diagnostic exam for patients with stable ischemic heart disease. In this commentary, we review the SCOT-HEART trial and its impact on CCTA for patients with stable ischemic heart disease.

Intracavitary Coronary Artery: An Unusual Coronary Anomaly.

PURPOSE: A few case reports of intracavitary coronary arteries (ICCA) have been reported and only a single case series on the coronary computed tomography angiography (CCTA) prevalence rate of ICCA of the right coronary artery (RCA). We describe several cases of ICCA that were noted incidentally and also determine the overall prevalence rate of anomalous ICCA. MATERIALS AND METHODS: A retrospective analysis of ICCA was performed consisting of consecutive CCTA cases as well as a group of ICCA from teaching files. To establish a prevalence rate, we reviewed 464 consecutive CCTA referred to our center for transcatheter aortic valve replacement. The presence of ICCA and several imaging features were evaluated. RESULTS: Our cohort comprises a total of 12 patients with ICCA, with 1 patient containing 2 anomalous ICCA. 83.3% of affected patients were adult males, with an average age of 69.8 years. The RCA was the most commonly affected vessel (53.8%). The mean length of the intracavitary segment was 33.4 mm for the RCA and 27 mm for the LAD. No cases involved the left circumflex coronary artery. Six of the cases were identified routinely as part of clinical practice and therefore not included in the prevalence analysis. On review of our transcatheter aortic valve replacement database, there was a 1.3% prevalence rate of ICCA. RCA had a prevalence of 0.4%, whereas LAD had a prevalence of 0.9%. CONCLUSIONS: Although rare, our study suggests that ICCA may be more common than previously described. Its presence is important to communicate to clinicians prior to invasive cardiac procedures to prevent potentially catastrophic outcomes.

Granulomatous-Lymphocytic Interstitial Lung Disease in a Patient With Common Variable Immunodeficiency.

Common variable immunodeficiency is the most common primary immunodeficiency and consists of impaired immunoglobulin production causing recurrent sinopulmonary infections. The most common cause of mortality for this disorder, however, is from the development of malignancy and autoimmune disorders. One common entity that develops is a systemic granulomatous and lymphoproliferative disorder that can cause an interstitial lung disease more formally referred to as granulomatous-lymphocytic interstitial lung disease (GL-ILD). We discuss a case of a 25-year-old woman with common variable immunodeficiency and GL-ILD and review the literature to summarize the most common radiological findings to raise the suspicion for GL-ILD on high-resolution computed tomography and delineate this from infection and other mimickers. We will also review key histopathological characteristics for diagnosis and the clinical approach and treatment options for this rare disease.

Pulmonary calcifications: a pictorial review and approach to formulating a differential diagnosis.

Pulmonary calcifications encompass a wide range of causes, both common and rare, such as calcified pulmonary nodules from chronic fungal infections and pulmonary alveolar microlithiasis. In this pictorial review, we categorize them based on etiology, which includes neoplastic calcifications, nonneoplastic calcified nodules, and iatrogenic- and exposure-related causes of pulmonary calcifications. We also illustrate the most characteristic imaging findings and outline the clinical implications for each of these entities to provide a sensible approach to pulmonary calcifications.