Imaging in COVID-19-related myocardial injury

Base-to-apex Gradient Pattern Assessed by Cardiovascular Magnetic Resonance in Takotsubo Cardiomyopathy

OBJECTIVES

The purpose of this study was to investigate the base-to-apex gradient strain pattern as a noncontrast cardiovascular magnetic resonance (CMR) parameter in patients with Takotsubo cardiomyopathy (TTC) and determine whether this pattern may help discriminate TTC from patients with anterior myocardial infarction (AMI).

MATERIALS AND METHODS

A total of 80 patients were included in the analysis: 30 patients with apical ballooning TTC and 50 patients with AMI. Global and regional ventricular function, including longitudinal (LS), circumferential (CS), and radial strain (RS), were assessed using CMR. The base-to-apex LS, RS, and CS gradients, defined as the peak gradient difference between averaged basal and apical strain, were calculated.

RESULTS

The base-to-apex RS gradient was impaired in TTC patients compared with the AMI group (14.04 ± 15.50 vs. -0.43 ± 11.59, P =0.001). Conversely, there were no significant differences in the base-to-apex LS and CS gradients between the AMI group and TTC patients (0.14 ± 2.71 vs. -1.5 ± 3.69, P =0.054: -0.99 ± 6.49 vs. ±1.4 ± 5.43, P =0.47, respectively). Beyond the presence and extension of LGE, base-to-apex RS gradient was the only independent discriminator between TTC and AMI (OR 1.28; 95% CI 1.08, 1.52, P =0.006) in multivariate logistic regression analysis.

CONCLUSION

The findings of this study suggest that the pattern of regional myocardial strain impairment could serve as an additional noncontrast CMR tool to refine the diagnosis of TTC. A pronounced base-to-apex RS gradient may be a specific left ventricle strain pattern of TTC.

Pulmonary transit time as a marker of diastolic dysfunction in Takotsubo syndrome

AIM

To evaluate the pulmonary transit time (PTT) and its derived parameters using cardiac magnetic resonance imaging (CMRI) as markers of diastolic dysfunction in Takotsubo syndrome (TS) and its relationship with transthoracic echocardiography and CMRI parameters.

MATERIALS AND METHODS

Twenty-two patients with TS, who exhibited diastolic dysfunction as assessed by transthoracic echocardiography, were enrolled retrospectively and the PTT, pulmonary transit time index (PTTI), and pulmonary blood volume index (PBVI) were evaluated using first-pass CMRI. PTT was calculated as the number of cardiac cycles required for a bolus of contrast agent to move from the right ventricle (RV) to the left ventricle (LV), whereas PTTI represents the PTT interval corrected for the heart rate. Finally, PBVI was calculated as the product of PTTI, and RV stroke volume indexed for body surface area. Normal references of PTT, PTTI, and PBVI were evaluated in a cohort of 20 age- and sex-matched healthy controls.

RESULTS

Compared with healthy subjects, TS patients showed significantly higher PTT, PTTI, and PBVI (p=0.0001, p=0.0001, and p=0.002, respectively). Using multivariable logistic regression, PBVI provided the best differentiation between TS and controls (AUC 0.84). PBVI was significantly associated with the index of diastolic dysfunction and left atrial strain parameters. In addition, PBVI demonstrated a significant correlation with global T2 mapping (r=0,520, p=0,019).

CONCLUSION

PTT and the derived parameters, as assessed using first-pass CMRI, are potential tools for assessing LV diastolic dysfunction in patients with TS.

Artificial Intelligence Applications in Cardiovascular Magnetic Resonance Imaging: Are We on the Path to Avoiding the Administration of Contrast Media?

In recent years, cardiovascular imaging examinations have experienced exponential growth due to technological innovation, and this trend is consistent with the most recent chest pain guidelines. Contrast media have a crucial role in cardiovascular magnetic resonance (CMR) imaging, allowing for more precise characterization of different cardiovascular diseases. However, contrast media have contraindications and side effects that limit their clinical application in determinant patients. The application of artificial intelligence (AI)-based techniques to CMR imaging has led to the development of non-contrast models. These AI models utilize non-contrast imaging data, either independently or in combination with clinical and demographic data, as input to generate diagnostic or prognostic algorithms. In this review, we provide an overview of the main concepts pertaining to AI, review the existing literature on non-contrast AI models in CMR, and finally, discuss the strengths and limitations of these AI models and their possible future development.

Exploring the EVolution in PrognOstic CapabiLity of MUltisequence Cardiac MagneTIc ResOnance in PatieNts Affected by Takotsubo Cardiomyopathy Based on Machine Learning Analysis: Design and Rationale of the EVOLUTION Study

PURPOSE

Takotsubo cardiomyopathy (TTC) is a transient but severe acute myocardial dysfunction with a wide range of outcomes from favorable to life-threatening. The current risk stratification scores of TTC patients do not include cardiac magnetic resonance (CMR) parameters. To date, it is still unknown whether and how clinical, trans-thoracic echocardiography (TTE), and CMR data can be integrated to improve risk stratification.

METHODS

EVOLUTION (Exploring the eVolution in prognOstic capabiLity of mUlti-sequence cardiac magneTIc resOnance in patieNts affected by Takotsubo cardiomyopathy) is a multicenter, international registry of TTC patients who will undergo a clinical, TTE, and CMR evaluation. Clinical data including demographics, risk factors, comorbidities, laboratory values, ECG, and results from TTE and CMR analysis will be collected, and each patient will be followed-up for in-hospital and long-term outcomes. Clinical outcome measures during hospitalization will include cardiovascular death, pulmonary edema, arrhythmias, stroke, or transient ischemic attack.Clinical long-term outcome measures will include cardiovascular death, pulmonary edema, heart failure, arrhythmias, sudden cardiac death, and major adverse cardiac and cerebrovascular events defined as a composite endpoint of death from any cause, myocardial infarction, recurrence of TTC, transient ischemic attack, and stroke. We will develop a comprehensive clinical and imaging score that predicts TTC outcomes and test the value of machine learning models, incorporating clinical and imaging parameters to predict prognosis.

CONCLUSIONS

The main goal of the study is to develop a comprehensive clinical and imaging score, that includes TTE and CMR data, in a large cohort of TTC patients for risk stratification and outcome prediction as a basis for possible changes in patient management.

Myocardial perfusion imaging in the era of COVID-19: a systematic review

PURPOSE

As COVID-19 was uncovered, it became evident that specific individuals could experience multi-organ complications for quite a while after infection. Among them, there were several cardiovascular complications. Myocardial perfusion imaging single photon emission computed tomography (MPI SPECT) can be utilized to detect and evaluate cardiac problems regardless of whether COVID caused them. By examining all publications relevant to the impacts of the pandemic on SPECT MPI imaging, we aimed to understand how the COVID pandemic affected different aspects of the MPI, how intense these effects were, and what the consequences were.

METHOD

On the 6th of June, 2022, a four-domain search strategy was developed and implemented by searching the following databases: PubMed, SCOPUS, EMBASE, Web of Science, and the Cochrane Central Register of Controlled Trials. The retrieved records have been put through two levels of screening. The search for forward and backward citations provided more results.

RESULTS

This study contained 32 papers, divided into the following three categories: 1. Case reports and series; 2. A comparison of the number of MPIs conducted before and after the pandemic; and 3. SPECT MPI findings.

CONCLUSION

We observed through the article review that CT scans performed in combination with MPI are crucial and should be interpreted within the context of COVID, especially during outbreaks. Moreover, we discovered that in the initial months of the pandemic, the number of SPECT MPIs performed globally decreased, with the fall being more significant in some countries, primarily in low- to middle-income regions. Lastly, we found that individuals with a history of COVID-19 may be more prone to having MPIs that demonstrate abnormalities, such as ischemia.

Segmentation-Based Classification Deep Learning Model Embedded with Explainable AI for COVID-19 Detection in Chest X-ray Scans

Background and Motivation: COVID-19 has resulted in a massive loss of life during the last two years. The current imaging-based diagnostic methods for COVID-19 detection in multiclass pneumonia-type chest X-rays are not so successful in clinical practice due to high error rates. Our hypothesis states that if we can have a segmentation-based classification error rate <5%, typically adopted for 510 (K) regulatory purposes, the diagnostic system can be adapted in clinical settings. Method: This study proposes 16 types of segmentation-based classification deep learning-based systems for automatic, rapid, and precise detection of COVID-19. The two deep learning-based segmentation networks, namely UNet and UNet+, along with eight classification models, namely VGG16, VGG19, Xception, InceptionV3, Densenet201, NASNetMobile, Resnet50, and MobileNet, were applied to select the best-suited combination of networks. Using the cross-entropy loss function, the system performance was evaluated by Dice, Jaccard, area-under-the-curve (AUC), and receiver operating characteristics (ROC) and validated using Grad-CAM in explainable AI framework. Results: The best performing segmentation model was UNet, which exhibited the accuracy, loss, Dice, Jaccard, and AUC of 96.35%, 0.15%, 94.88%, 90.38%, and 0.99 (p-value <0.0001), respectively. The best performing segmentation-based classification model was UNet+Xception, which exhibited the accuracy, precision, recall, F1-score, and AUC of 97.45%, 97.46%, 97.45%, 97.43%, and 0.998 (p-value <0.0001), respectively. Our system outperformed existing methods for segmentation-based classification models. The mean improvement of the UNet+Xception system over all the remaining studies was 8.27%. Conclusion: The segmentation-based classification is a viable option as the hypothesis (error rate <5%) holds true and is thus adaptable in clinical practice.

Multicenter Study on COVID-19 Lung Computed Tomography Segmentation with varying Glass Ground Opacities using Unseen Deep Learning Artificial Intelligence Paradigms: COVLIAS 1.0 Validation

Variations in COVID-19 lesions such as glass ground opacities (GGO), consolidations, and crazy paving can compromise the ability of solo-deep learning (SDL) or hybrid-deep learning (HDL) artificial intelligence (AI) models in predicting automated COVID-19 lung segmentation in Computed Tomography (CT) from unseen data leading to poor clinical manifestations. As the first study of its kind, “COVLIAS 1.0-Unseen” proves two hypotheses, (i) contrast adjustment is vital for AI, and (ii) HDL is superior to SDL. In a multicenter study, 10,000 CT slices were collected from 72 Italian (ITA) patients with low-GGO, and 80 Croatian (CRO) patients with high-GGO. Hounsfield Units (HU) were automatically adjusted to train the AI models and predict from test data, leading to four combinations—two Unseen sets: (i) train-CRO:test-ITA, (ii) train-ITA:test-CRO, and two Seen sets: (iii) train-CRO:test-CRO, (iv) train-ITA:test-ITA. COVILAS used three SDL models: PSPNet, SegNet, UNet and six HDL models: VGG-PSPNet, VGG-SegNet, VGG-UNet, ResNet-PSPNet, ResNet-SegNet, and ResNet-UNet. Two trained, blinded senior radiologists conducted ground truth annotations. Five types of performance metrics were used to validate COVLIAS 1.0-Unseen which was further benchmarked against MedSeg, an open-source web-based system. After HU adjustment for DS and JI, HDL (Unseen AI) > SDL (Unseen AI) by 4% and 5%, respectively. For CC, HDL (Unseen AI) > SDL (Unseen AI) by 6%. The COVLIAS-MedSeg difference was < 5%, meeting regulatory guidelines.Unseen AI was successfully demonstrated using automated HU adjustment. HDL was found to be superior to SDL.

Frontiers of COVID-19-related myocarditis as assessed by cardiovascular magnetic resonance

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. In some patients, COVID-19 is complicated with myocarditis. Early detection of myocardial injury and timely intervention can significantly improve the clinical outcomes of COVID-19 patients. Although endomyocardial biopsy (EMB) is currently recognized as the ‘gold standard’ for the diagnosis of myocarditis, there are large sampling errors, many complications and a lack of unified diagnostic criteria. In addition, the clinical methods of treating acute and chronic COVID-19-related myocarditis are different. Cardiac magnetic resonance (CMR) can evaluate the morphology of the heart, left and right ventricular functions, myocardial perfusion, capillary leakage and myocardial interstitial fibrosis to provide a noninvasive and radiation-free diagnostic basis for the clinical detection, efficacy and risk assessment, and follow-up observation of COVID-19-related myocarditis. However, for the diagnosis of COVID-19-related myocarditis, the Lake Louise Consensus Criteria may not be fully applicable. COVID-19-related myocarditis is different from myocarditis related to other viral infections in terms of signal intensity and lesion location as assessed by CMR, which is used to visualize myocardial damage, locate lesions and quantify pathological changes based on various sequences. Therefore, the standardized application of CMR to timely and accurately evaluate heart injury in COVID-19-related myocarditis and develop rational treatment strategies could be quite effective in improving the prognosis of patients and preventing potential late-onset effects in convalescent patients with COVID-19.

Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A multicenter study using COVLIAS 2.0

BACKGROUND

COVLIAS 1.0: an automated lung segmentation was designed for COVID-19 diagnosis. It has issues related to storage space and speed. This study shows that COVLIAS 2.0 uses pruned AI (PAI) networks for improving both storage and speed, wiliest high performance on lung segmentation and lesion localization.

METHOD

ology: The proposed study uses multicenter ∼9,000 CT slices from two different nations, namely, CroMed from Croatia (80 patients, experimental data), and NovMed from Italy (72 patients, validation data). We hypothesize that by using pruning and evolutionary optimization algorithms, the size of the AI models can be reduced significantly, ensuring optimal performance. Eight different pruning techniques (i) differential evolution (DE), (ii) genetic algorithm (GA), (iii) particle swarm optimization algorithm (PSO), and (iv) whale optimization algorithm (WO) in two deep learning frameworks (i) Fully connected network (FCN) and (ii) SegNet were designed. COVLIAS 2.0 was validated using "Unseen NovMed" and benchmarked against MedSeg. Statistical tests for stability and reliability were also conducted.

RESULTS

Pruning algorithms (i) FCN-DE, (ii) FCN-GA, (iii) FCN-PSO, and (iv) FCN-WO showed improvement in storage by 92.4%, 95.3%, 98.7%, and 99.8% respectively when compared against solo FCN, and (v) SegNet-DE, (vi) SegNet-GA, (vii) SegNet-PSO, and (viii) SegNet-WO showed improvement by 97.1%, 97.9%, 98.8%, and 99.2% respectively when compared against solo SegNet. AUC > 0.94 (p < 0.0001) on CroMed and > 0.86 (p < 0.0001) on NovMed data set for all eight EA model. PAI <0.25 s per image. DenseNet-121-based Grad-CAM heatmaps showed validation on glass ground opacity lesions.

CONCLUSIONS

Eight PAI networks that were successfully validated are five times faster, storage efficient, and could be used in clinical settings.

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID-19: A Narrative Review

Background and Motivation: Parkinson’s disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID-19 causes the ML systems to become severely non-linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well-explained ML paradigms. Deep neural networks are powerful learning machines that generalize non-linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID-19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID-19 framework. We study the hypothesis that PD in the presence of COVID-19 can cause more harm to the heart and brain than in non-COVID-19 conditions. COVID-19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID-19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID-19 lesions, office and laboratory arterial atherosclerotic image-based biomarkers, and medicine usage for the PD patients for the design of DL point-based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID-19 environment and this was also verified. DL architectures like long short-term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID-19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID-19.

COVLIAS 1.0Lesion vs. MedSeg: An Artificial Intelligence Framework for Automated Lesion Segmentation in COVID-19 Lung Computed Tomography Scans

Background: COVID-19 is a disease with multiple variants, and is quickly spreading throughout the world. It is crucial to identify patients who are suspected of having COVID-19 early, because the vaccine is not readily available in certain parts of the world. Methodology: Lung computed tomography (CT) imaging can be used to diagnose COVID-19 as an alternative to the RT-PCR test in some cases. The occurrence of ground-glass opacities in the lung region is a characteristic of COVID-19 in chest CT scans, and these are daunting to locate and segment manually. The proposed study consists of a combination of solo deep learning (DL) and hybrid DL (HDL) models to tackle the lesion location and segmentation more quickly. One DL and four HDL models—namely, PSPNet, VGG-SegNet, ResNet-SegNet, VGG-UNet, and ResNet-UNet—were trained by an expert radiologist. The training scheme adopted a fivefold cross-validation strategy on a cohort of 3000 images selected from a set of 40 COVID-19-positive individuals. Results: The proposed variability study uses tracings from two trained radiologists as part of the validation. Five artificial intelligence (AI) models were benchmarked against MedSeg. The best AI model, ResNet-UNet, was superior to MedSeg by 9% and 15% for Dice and Jaccard, respectively, when compared against MD 1, and by 4% and 8%, respectively, when compared against MD 2. Statistical tests—namely, the Mann−Whitney test, paired t-test, and Wilcoxon test—demonstrated its stability and reliability, with p < 0.0001. The online system for each slice was <1 s. Conclusions: The AI models reliably located and segmented COVID-19 lesions in CT scans. The COVLIAS 1.0Lesion lesion locator passed the intervariability test.

Chest Computed Tomography Is an Efficient Method for Initial Diagnosis of COVID-19: An Observational Study

Coronavirus disease (COVID-19) is an infectious disease that can lead to pneumonia, pulmonary oedema, acute respiratory distress syndrome, multiple organ and system dysfunction, and death. This study aimed to verify the efficacy of chest computed tomography (CT) for the initial diagnosis of COVID-19. This observational, retrospective, cross-sectional study included 259 individuals who underwent clinical evaluation, blood collection, chest CT, and a reverse transcription polymerase chain reaction (RT-PCR) diagnostic test for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during their course of treatment at a reference hospital in Belém, Pará, Brazil between April and June 2020. Inclusion criteria were flu-like symptoms in adults of both sexes. Individuals with an inconclusive COVID-19 molecular test or who had artifacts in the chest CT images were excluded. Parametric data were analyzed using Student-t-test and non-parametric data were analyzed using average test and Fisher exact test. Participants were divided into two groups: Group 1 (COVID-19 positive), n = 211 (124 males, 87 females), 51.8 ± 17.9 years old and Group 2 (COVID-19 negative), n = 48 (22 males, 26 females), 47.6 ± 18.6 years old. Most frequent symptoms were cough [Group 1 n = 199 (94%)/Group 2 n = 46 (95%)], fever [Group 1 n = 154 (72%)/Group 2 n = 28 (58%)], myalgia [Group 1 n = 172 (81%)/Group 2 n = 38 (79%)], dyspnoea [Group 1 n = 169 (80%) / Group 2 n = 37 (77%)], headache [Group 1 n = 163 (77%)/Group 2 n = 32 (66%)], and anosmia [Group 1 n = 154 (73%)/Group 2 n = 29 (60%)]. Group 1 had a higher proportion of ground-glass opacity [Group 1 n = 175 (83%)/Group 2 n = 24 (50%), 0.00], vascular enhancement sign [Group 1 n = 128 (60%)/Group 2 n = 15 (31%), 0.00], septal thickening [Group 1 n = 99 (47%)/Group 2 n = 13 (27%), 0.01], crazy-paving pattern [Group 1 n = 98 (46%) / Group 2 n = 13 (27%), 0.01], consolidations [Group 1 n = 92 (43%)/Group 2 n = 8 (16%), 0.00], and CO-RADS 4 and 5 [Group 1 n = 163 (77.25%)/Group 2 n = 24 (50%), 0.00] categories in chest CT. Chest CT, when available, was found to be an efficient method for the initial diagnosis and better management of individuals with COVID-19.

The emerging role of atrial strain assessed by cardiac MRI in different cardiovascular settings: an up-to-date review

The left atrium (LA) has a crucial function in maintaining left ventricular filling, which is responsible for about one-third of all cardiac filling. A growing body of evidence shows that LA is involved in several cardiovascular diseases from a clinical and prognostic standpoint. LA enlargement has been recognized as a predictor of the outcomes of many diseases. However, LA enlargement itself does not explain the whole LA's function during the cardiac cycle. For this reason, the recently proposed assessment of atrial strain at advanced cardiac magnetic resonance (CMR) enables the usual limitations of the sole LA volumetric measurement to be overcome. Moreover, the left atrial strain impairment might allow several cardiovascular diseases to be detected at an earlier stage. While traditional CMR has a central role in assessing LA volume and, through cine sequences, a marginal role in evaluating LA function, feature tracking at advanced CMR (CMR-FT) has been increasingly confirmed as a feasible and reproducible technique for assessing LA function through strain. In comparison to atrial function evaluations via speckle tracking echocardiography, CMR-FT has a higher spatial resolution, larger field of view, and better reproducibility. In this literature review on atrial strain analysis, we describe the strengths, limitations, recent applications, and promising developments of studying atrial function using CMR-FT in clinical practice. KEY POINTS: • The left atrium has a crucial function in maintaining left ventricular filling; left atrial size has been recognized as a predictor of the outcomes of many diseases. • Left atrial strain has been confirmed as a marker of atrial functional status and demonstrated to be a sensitive tool in the subclinical phase of a disease. • A comprehensive evaluation of the three phases of atrial function by CMR-FT demonstrates an impairment before the onset of atrial enlargement, thus helping clinicians in their decision-making and improving patient outcomes.

Atrial Impairment as a Marker in Discriminating Between Takotsubo and Acute Myocarditis Using Cardiac Magnetic Resonance

PURPOSE

The purpose of this study was to comprehensively compare the left and right atrium strain and strain rate (SR) parameters by cardiac magnetic resonance (CMR) between patients with Takotsubo (TS) and patients with acute myocarditis (AM).

MATERIALS AND METHODS

We retrospectively enrolled 3 groups of patients: TS (n=18), AM (n=14), and 11 healthy subjects. All the patients had complete CMR data for features tracking assessment.Differences in reservoir, conduit strain (εe), conduit strain rate (SRe), and booster phase of biatrial strain were analyzed between the groups using analysis of variance and multivariate analysis of covariance analyses. Intraobserver and interobserver reproducibility was assessed for all strain and SR parameters using intraclass correlation coefficients and Bland-Altman analysis.

RESULTS

Atrial strain was feasible in all patients and controls. In TS, left atrium (LA) reservoir strain (εs), reservoir SR, εe, and SRe were significantly lower compared with the other groups (P=0,001 for all). multivariate analysis of covariance analysis showed association of these parameters after correction for age and sex, while LA booster deformation (εa and SRa) strain parameters were preserved. LA SRe proved to have excellent sensitivity in differentiating patients with TS from those with AM (areas under the curves of 0.903, 95% confidence interval: 0.81-0.99).Biatrial strain and SR parameters showed good (excellent) intraobserver and interobserver reproducibility (ranged between 0.61 to 0.96 and 0.50 to 0.90, respectively).

CONCLUSION

Compared with AM, patients with TS showed significantly decreased LA reservoir, conduit strain, and SR parameters. Therefore, LA strain assessment may have a role in discriminating between TS and AM.

Could CMR Tissue-Tracking and Parametric Mapping Distinguish Between Takotsubo Syndrome and Acute Myocarditis? A Pilot Study

RATIONALE AND OBJECTIVE

Takotsubo syndrome (TS) is a transient and often misdiagnosed form of left ventricular dysfunction. Acute myocarditis (AM) is usually included in TS differential diagnosis. The aim of this study is to assess the role of cardiac magnetic resonance imaging coupled with tissue-tracking technique (CMR-TT) and parametric mappings analysis in discriminating between TS and AM.

MATERIALS AND METHODS

We retrospectively enrolled three groups: patients with TS (n = 12), patients with AM (n = 14), and 10 healthy controls. All the patients had a comprehensive CMR examination, including the assessment of global and segmental longitudinal strain, circumferential strain, radial strain (RS), and parametric mapping.

RESULTS

The analysis of variance was used to compare the different groups. In TS patients, basal RS, global T1 mapping, global T2 mapping, mid T2 mapping, apical T1 and T2 mapping were statistically significantly different compared with the other groups. MANCOVA analysis confirmed that the association between myocardial strain data and parametric mapping was independent on age and sex. Apical T1 and T2 mapping proved to have a good performance in differentiating TS from AM (area under the curves of 0.908 and 0.879, respectively).

CONCLUSION

Basal RS and apical tissue mapping analysis are the most advanced CMR-derived parameters in making a differential diagnosis between TS and AM.

Role of imaging in rare COVID-19 vaccine multiorgan complications

As of September 18th, 2021, global casualties due to COVID-19 infections approach 200 million, several COVID-19 vaccines have been authorized to prevent COVID-19 infection and help mitigate the spread of the virus. Despite the vast majority having safely received vaccination against SARS-COV-2, the rare complications following COVID-19 vaccination have often been life-threatening or fatal. The mechanisms underlying (multi) organ complications are associated with COVID-19, either through direct viral damage or from host immune response (i.e., cytokine storm). The purpose of this manuscript is to review the role of imaging in identifying and elucidating multiorgan complications following SARS-COV-2 vaccination-making clear that, in any case, they represent a minute fraction of those in the general population who have been vaccinated. The authors are both staunch supporters of COVID-19 vaccination and vaccinated themselves as well.

Four Types of Multiclass Frameworks for Pneumonia Classification and Its Validation in X-ray Scans Using Seven Types of Deep Learning Artificial Intelligence Models

Background and Motivation: The novel coronavirus causing COVID-19 is exceptionally contagious, highly mutative, decimating human health and life, as well as the global economy, by consistent evolution of new pernicious variants and outbreaks. The reverse transcriptase polymerase chain reaction currently used for diagnosis has major limitations. Furthermore, the multiclass lung classification X-ray systems having viral, bacterial, and tubercular classes—including COVID-19—are not reliable. Thus, there is a need for a robust, fast, cost-effective, and easily available diagnostic method. Method: Artificial intelligence (AI) has been shown to revolutionize all walks of life, particularly medical imaging. This study proposes a deep learning AI-based automatic multiclass detection and classification of pneumonia from chest X-ray images that are readily available and highly cost-effective. The study has designed and applied seven highly efficient pre-trained convolutional neural networks—namely, VGG16, VGG19, DenseNet201, Xception, InceptionV3, NasnetMobile, and ResNet152—for classification of up to five classes of pneumonia. Results: The database consisted of 18,603 scans with two, three, and five classes. The best results were using DenseNet201, VGG16, and VGG16, respectively having accuracies of 99.84%, 96.7%, 92.67%; sensitivity of 99.84%, 96.63%, 92.70%; specificity of 99.84, 96.63%, 92.41%; and AUC of 1.0, 0.97, 0.92 (p < 0.0001 for all), respectively. Our system outperformed existing methods by 1.2% for the five-class model. The online system takes <1 s while demonstrating reliability and stability. Conclusions: Deep learning AI is a powerful paradigm for multiclass pneumonia classification.

Bias Investigation in Artificial Intelligence Systems for Early Detection of Parkinson's Disease: A Narrative Review

Background and Motivation: Diagnosis of Parkinson's disease (PD) is often based on medical attention and clinical signs. It is subjective and does not have a good prognosis. Artificial Intelligence (AI) has played a promising role in the diagnosis of PD. However, it introduces bias due to lack of sample size, poor validation, clinical evaluation, and lack of big data configuration. The purpose of this study is to compute the risk of bias (RoB) automatically.

METHOD

The PRISMA search strategy was adopted to select the best 39 AI studies out of 85 PD studies closely associated with early diagnosis PD. The studies were used to compute 30 AI attributes (based on 6 AI clusters), using AP(ai)Bias 1.0 (AtheroPointTM, Roseville, CA, USA), and the mean aggregate score was computed. The studies were ranked and two cutoffs (Moderate-Low (ML) and High-Moderate (MH)) were determined to segregate the studies into three bins: low-, moderate-, and high-bias.

RESULT

The ML and HM cutoffs were 3.50 and 2.33, respectively, which constituted 7, 13, and 6 for low-, moderate-, and high-bias studies. The best and worst architectures were "deep learning with sketches as outcomes" and "machine learning with Electroencephalography," respectively. We recommend (i) the usage of power analysis in big data framework, (ii) that it must undergo scientific validation using unseen AI models, and (iii) that it should be taken towards clinical evaluation for reliability and stability tests.

CONCLUSION

The AI is a vital component for the diagnosis of early PD and the recommendations must be followed to lower the RoB.

Long-COVID diagnosis: from diagnostic to advanced AI-driven models

SARS-COV 2 is recognized to be responsible for a multi-organ syndrome. In most patients, symptoms are mild. However, in certain subjects, COVID-19 tends to progress more severely.

Most of the patients infected with SARS-COV2 fully recovered within some weeks. In a considerable number of patients, like many other viral infections, various long-lasting symptoms have been described, now defined as “long COVID-19 syndrome”.

Given the high number of contagious over the world, it is necessary to understand and comprehend this emerging pathology to enable early diagnosis and improve patents outcomes.

In this scenario, AI-based models can be applied in long-COVID-19 patients to assist clinicians and at the same time, to reduce the considerable impact on the care and rehabilitation unit.

The purpose of this manuscript is to review different aspects of long-COVID-19 syndrome from clinical presentation to diagnosis, highlighting the considerable impact that AI can have.

LINC00599 influences smoke-related chronic obstructive pulmonary disease and regulates CSE-induced epithelial cell apoptosis and inflammation by targeting miR-212-5p/BASP1 axis

LINC00599 has been reported to be upregulated in response to cigarette smoking. However, the effect and underlying mechanism of LINC00599 in chronic obstructive pulmonary disease (COPD) are still under exploration. In this study, LINC00599 was upregulated in the COPD patients and was of clinical value to distinguish COPD patients. COPD cell models were established using 16HBE cells under cigarette smoke extract (CSE) treatment. LINC00599 levels were elevated in a dose and time-dependent way in response to CSE stimulation. The effect of LINC00599 on CSE-induced 16HBE cells was explored. The results showed that LINC00599 deficiency reversed the CSE-induced inhibition on cell viability and proliferation, and rescued the CSE-induced enhancement on cell 16HBE cell apoptosis and inflammation response. Moreover, LINC00599 bound with miR-212-5p to upregulate the BASP1 (brain abundant membrane attached signal protein 1) expression. MiR-212-5p was expressed at a low level in the tissue samples of COPD patients, and its levels were upregulated in LINC00599 silenced cells. BASP1 was targeted by miR-212-5p and its upregulation was identified in the tissue samples of COPD patients and cell models. BASP1 levels were downregulated after miR-212-5p overexpression or LINC00599 silencing. Moreover, the rescue assays demonstrated that BASP1 overexpression reversed the effect of silenced LINC00599 on 16HBE cells after CSE treatment, which indicated that LINC00599 promoted the COPD development by regulating BASP1 expression. In conclusion, LINC00599 facilitated CSE-induced cell apoptosis and inflammation response, while inhibiting the cell viability and proliferation in COPD progression via modulating miR-212-5p/BASP1 axis.

COVLIAS 1.0 vs. MedSeg: Artificial Intelligence-Based Comparative Study for Automated COVID-19 Computed Tomography Lung Segmentation in Italian and Croatian Cohorts

(1) Background: COVID-19 computed tomography (CT) lung segmentation is critical for COVID lung severity diagnosis. Earlier proposed approaches during 2020–2021 were semiautomated or automated but not accurate, user-friendly, and industry-standard benchmarked. The proposed study compared the COVID Lung Image Analysis System, COVLIAS 1.0 (GBTI, Inc., and AtheroPoint^TM, Roseville, CA, USA, referred to as COVLIAS), against MedSeg, a web-based Artificial Intelligence (AI) segmentation tool, where COVLIAS uses hybrid deep learning (HDL) models for CT lung segmentation. (2) Materials and Methods: The proposed study used 5000 ITALIAN COVID-19 positive CT lung images collected from 72 patients (experimental data) that confirmed the reverse transcription-polymerase chain reaction (RT-PCR) test. Two hybrid AI models from the COVLIAS system, namely, VGG-SegNet (HDL 1) and ResNet-SegNet (HDL 2), were used to segment the CT lungs. As part of the results, we compared both COVLIAS and MedSeg against two manual delineations (MD 1 and MD 2) using (i) Bland–Altman plots, (ii) Correlation coefficient (CC) plots, (iii) Receiver operating characteristic curve, and (iv) Figure of Merit and (v) visual overlays. A cohort of 500 CROATIA COVID-19 positive CT lung images (validation data) was used. A previously trained COVLIAS model was directly applied to the validation data (as part of Unseen-AI) to segment the CT lungs and compare them against MedSeg. (3) Result: For the experimental data, the four CCs between COVLIAS (HDL 1) vs. MD 1, COVLIAS (HDL 1) vs. MD 2, COVLIAS (HDL 2) vs. MD 1, and COVLIAS (HDL 2) vs. MD 2 were 0.96, 0.96, 0.96, and 0.96, respectively. The mean value of the COVLIAS system for the above four readings was 0.96. CC between MedSeg vs. MD 1 and MedSeg vs. MD 2 was 0.98 and 0.98, respectively. Both had a mean value of 0.98. On the validation data, the CC between COVLIAS (HDL 1) vs. MedSeg and COVLIAS (HDL 2) vs. MedSeg was 0.98 and 0.99, respectively. For the experimental data, the difference between the mean values for COVLIAS and MedSeg showed a difference of <2.5%, meeting the standard of equivalence. The average running times for COVLIAS and MedSeg on a single lung CT slice were ~4 s and ~10 s, respectively. (4) Conclusions: The performances of COVLIAS and MedSeg were similar. However, COVLIAS showed improved computing time over MedSeg.

Long non-coding RNA OIP5-AS1 regulates smoke-related chronic obstructive pulmonary disease via targeting micro RNA -410-3p/IL-13

This investigation aimed to assess the levels of serum OIP5-AS1 and micro RNA-410-3p (miR-410-3p) in patients with chronic obstructive pulmonary disease (COPD) and their potential molecular mechanism. The levels of OIP5-AS1 and miR-410-3p as well as mRNA levels of IL-13 were measured. Pearson variable linear test was applied to analyze the correlations between forced expiratory volume in 1 second (FEV1) and OIP5-AS1. The receiver operating characteristic curve was used to predict the predictive possibility of OIP5-AS1. The viable cells were detected by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) and flow cytometry was used to detect the cell apoptosis. An enzyme-linked immunosorbent assay was performed to indicate the inflammatory situation of 16HBE cells. Luciferase activity assay was conducted to examine the relationships between OIP5-AS1 and miR-410-3p together with miR-410-3p and IL-13. Augmented levels of OIP5-AS1, declined levels of miR-410-3p, and enhanced expression of IL-13 were unveiled. The expression of OIP5-AS1 and miR-410-3p was related to the ratio of FEV1 respectively. OIP5-AS1 might serve as a diagnostic biomarker. Interference of OIP5-AS1 restored the abnormal cell viability, apoptosis, and inflammation in cigarette smoke extract (CSE)-stimulated 16HBE cells by regulating miR-410-3p and IL-13. OIP5-AS1 appeared to be a biomarker for distinguishing COPD patients from smokers. OIP5-AS1/miR-410-3p/IL-13 exerted function on the cell viability, apoptosis, and inflammation in CSE-steered cell models.

Systematic Review of Artificial Intelligence in Acute Respiratory Distress Syndrome for COVID-19 Lung Patients: A Biomedical Imaging Perspective

SARS-CoV-2 has infected over ∼165 million people worldwide causing Acute Respiratory Distress Syndrome (ARDS) and has killed ∼3.4 million people. Artificial Intelligence (AI) has shown to benefit in the biomedical image such as X-ray/Computed Tomography in diagnosis of ARDS, but there are limited AI-based systematic reviews (aiSR). The purpose of this study is to understand the Risk-of-Bias (RoB) in a non-randomized AI trial for handling ARDS using novel AtheroPoint-AI-Bias (AP(ai)Bias). Our hypothesis for acceptance of a study to be in low RoB must have a mean score of 80% in a study. Using the PRISMA model, 42 best AI studies were analyzed to understand the RoB. Using the AP(ai)Bias paradigm, the top 19 studies were then chosen using the raw-cutoff of 1.9. This was obtained using the intersection of the cumulative plot of "mean score vs. study" and score distribution. Finally, these studies were benchmarked against ROBINS-I and PROBAST paradigm. Our observation showed that AP(ai)Bias, ROBINS-I, and PROBAST had only 32%, 16%, and 26% studies, respectively in low-moderate RoB (cutoff>2.5), however none of them met the RoB hypothesis. Further, the aiSR analysis recommends six primary and six secondary recommendations for the non-randomized AI for ARDS. The primary recommendations for improvement in AI-based ARDS design inclusive of (i) comorbidity, (ii) inter-and intra-observer variability studies, (iii) large data size, (iv) clinical validation, (v) granularity of COVID-19 risk, and (vi) cross-modality scientific validation. The AI is an important component for diagnosis of ARDS and the recommendations must be followed to lower the RoB.

Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography

Background: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are evaluated. We propose a robust and stable inter-variability analysis of CT lung segmentation in COVID-19 to avoid the effect of bias. Methodology: The proposed inter-variability study consists of two GT tracers for lung segmentation on chest CT. Three AI models, PSP Net, VGG-SegNet, and ResNet-SegNet, were trained using GT annotations. We hypothesized that if AI models are trained on the GT tracings from multiple experience levels, and if the AI performance on the test data between these AI models is within the 5% range, one can consider such an AI model robust and unbiased. The K5 protocol (training to testing: 80%:20%) was adapted. Ten kinds of metrics were used for performance evaluation. Results: The database consisted of 5000 CT chest images from 72 COVID-19-infected patients. By computing the coefficient of correlations (CC) between the output of the two AI models trained corresponding to the two GT tracers, computing their differences in their CC, and repeating the process for all three AI-models, we show the differences as 0%, 0.51%, and 2.04% (all < 5%), thereby validating the hypothesis. The performance was comparable; however, it had the following order: ResNet-SegNet > PSP Net > VGG-SegNet. Conclusions: The AI models were clinically robust and stable during the inter-variability analysis on the CT lung segmentation on COVID-19 patients.

The Valuable Role of Imaging Modalities in the Diagnosis of the Uncommon Presentations of COVID-19: An Educative Case Series

The outbreak of coronavirus disease 2019 (COVID-19) in late 2019 rapidly turned into a global pandemic. Although the symptoms of COVID-19 are mainly respiratory ones, the infection is associated with a wide range of clinical signs and symptoms. The main imaging modality in COVID-19 is lung computed tomography (CT) scanning, but the diagnosis of the vast spectrum of complications needs the application of various imaging modalities. Owing to the novelty of the disease and its presentations, its complications-particularly uncommon ones-can be easily missed. In this study, we describe some uncommon presentations of COVID-19 diagnosed by various imaging modalities. The first case presented herein was a man with respiratory distress, who transpired to suffer from pneumothorax and pneumomediastinum in addition to the usual pneumonia of COVID-19. The second patient was a hospitalized COVID-19 case, whose clinical condition suddenly deteriorated with the development of abdominal symptoms diagnosed as mesenteric ischemia by abdominal CT angiography. The third patient was a case of cardiac involvement in the COVID-19 course, detected as myocarditis by cardiac magnetic resonance imaging (MRI). The fourth and fifth cases were COVID-19-associated encephalitis whose diagnoses were established by brain MRI. COVID-19 is a multisystem disorder with a wide range of complications such as pneumothorax, pneumomediastinum, mesenteric ischemia, myocarditis, and encephalitis. Prompt diagnosis with appropriate imaging modalities can lead to adequate treatment and better survival.

Atrial Strain by Feature-Tracking Cardiac Magnetic Resonance Imaging in Takotsubo Cardiomyopathy. Features, Feasibility, and Reproducibility

OBJECTIVES

The purpose of this study was to investigate whether there may be a bi-atrial dysfunction in Takotsubo syndrome (TS) during the transient course of the disease, using cardiac magnetic resonance imaging feature tracking (CMR-FT) in analyzing bi-atrial strain.

METHOD

Eighteen TS patients and 13 healthy controls were studied. Reservoir, conduit, and booster bi-atrial functions were analyzed by CMR-FT. The correlation between LA and RA strain parameters was assessed. Intra- and inter-observer reproducibility was evaluated for all strain and strain rate (SR) parameters using intraclass correlation coefficients (ICCs) and Bland-Altman analysis.

RESULTS

Atrial strain were feasible in all patients and controls. Takotsubo patients showed an impaired LA Reservoir strain (∊_s), LA Reservoir strain rate (SRs), LA and RA Conduit strain(∊_e), LA and RA conduit strain rate (SRe) in comparison with controls (P < 0.001 for all of them), while no differences were found as to LA and RA booster deformation parameters (∊_a and SRa). Analysis of correlation showed that LA ∊_s, SRs, ∊_e, and SRe were positively correlated with corresponding RA strain measurements (P < 0.001, r = 0.61 and P = 0,03, r = 0,54, respectively). Reproducibility was good to excellent for all atrial strain and strain rate parameters (ICCs ranging from 0,50 to 0,96).

CONCLUSION

Atrial strain analysis using CMR-FT may be a useful tool to reveal new pathophysiological insights in Takotsubo cardiomyopathy. Additional studies, with a larger number of patients, are needed to confirm the possible role of these advanced CMR tools in characterizing TS patients.

Prognostic implications of biventricular strain measurement in COVID-19 patients by speckle-tracking echocardiography

BACKGROUND

Recent reports have indicated the beneficial role of strain measurement in COVID-19 patients.

HYPOTHESIS

To determine the association between right and left global longitudinal strain (RVGLS, LVGLS) and COVID-19 patients' outcomes.

METHODS

Hospitalized COVID-19 patients between June and August 2020 were included. Two-dimensional echocardiography and biventricular global longitudinal strain measurement were performed. The outcome measure was defined as mortality, ICU admission, and need for intubation. Appropriate statistical tests were used to compare different groups.

RESULTS

In this study 207 patients (88 females) were enrolled. During 64 ± 4 days of follow-up, 22 (10.6%) patients died. Mortality, ICU admission, and intubation were significantly associated with LVGLS and RVGLS tertiles. LVGLS tertiles could predict poor outcome with significant odds ratios in the total population (OR = 0.203, 95% CI: 0.088-0.465; OR = 0.350, 95% CI: 0.210-0.585; OR = 0.354, 95% CI: 0.170-0.736 for mortality, ICU admission, and intubation). Although odds ratios of LVGLS for the prediction of outcome were statistically significant among hypertensive patients, these odds ratios did not reach significance among non-hypertensive patients. RVGLS tertiles revealed significant odds ratios for the prediction of mortality (OR = 0.322, 95% CI: 0.162-0.640), ICU admission (OR = 0.287, 95% CI: 0.166-0.495), and need for intubation (OR = 0.360, 95% CI: 0.174-0.744). Odds ratios of RVGLS remained significant even after adjusting for hypertension when considering mortality and ICU admission.

CONCLUSION

RVGLS and LVGLS can be acceptable prognostic factors to predict mortality, ICU admission, and intubation in hospitalized COVID-19 patients. However, RVGLS seems more reliable, as it is not confounded by hypertension.

COVLIAS 1.0: Lung Segmentation in COVID-19 Computed Tomography Scans Using Hybrid Deep Learning Artificial Intelligence Models

BACKGROUND

COVID-19 lung segmentation using Computed Tomography (CT) scans is important for the diagnosis of lung severity. The process of automated lung segmentation is challenging due to (a) CT radiation dosage and (b) ground-glass opacities caused by COVID-19. The lung segmentation methodologies proposed in 2020 were semi- or automated but not reliable, accurate, and user-friendly. The proposed study presents a COVID Lung Image Analysis System (COVLIAS 1.0, AtheroPoint™, Roseville, CA, USA) consisting of hybrid deep learning (HDL) models for lung segmentation.

METHODOLOGY

The COVLIAS 1.0 consists of three methods based on solo deep learning (SDL) or hybrid deep learning (HDL). SegNet is proposed in the SDL category while VGG-SegNet and ResNet-SegNet are designed under the HDL paradigm. The three proposed AI approaches were benchmarked against the National Institute of Health (NIH)-based conventional segmentation model using fuzzy-connectedness. A cross-validation protocol with a 40:60 ratio between training and testing was designed, with 10% validation data. The ground truth (GT) was manually traced by a radiologist trained personnel. For performance evaluation, nine different criteria were selected to perform the evaluation of SDL or HDL lung segmentation regions and lungs long axis against GT.

RESULTS

Using the database of 5000 chest CT images (from 72 patients), COVLIAS 1.0 yielded AUC of ~0.96, ~0.97, ~0.98, and ~0.96 (p-value < 0.001), respectively within 5% range of GT area, for SegNet, VGG-SegNet, ResNet-SegNet, and NIH. The mean Figure of Merit using four models (left and right lung) was above 94%. On benchmarking against the National Institute of Health (NIH) segmentation method, the proposed model demonstrated a 58% and 44% improvement in ResNet-SegNet, 52% and 36% improvement in VGG-SegNet for lung area, and lung long axis, respectively. The PE statistics performance was in the following order: ResNet-SegNet > VGG-SegNet > NIH > SegNet. The HDL runs in <1 s on test data per image.

CONCLUSIONS

The COVLIAS 1.0 system can be applied in real-time for radiology-based clinical settings.

Imaging in the COVID-19 era: Lessons learned during a pandemic

The first year of the coronavirus disease 2019 (COVID-19) pandemic has been a year of unprecedented changes, scientific breakthroughs, and controversies. The radiology community has not been spared from the challenges imposed on global healthcare systems. Radiology has played a crucial part in tackling this pandemic, either by demonstrating the manifestations of the virus and guiding patient management, or by safely handling the patients and mitigating transmission within the hospital. Major modifications involving all aspects of daily radiology practice have occurred as a result of the pandemic, including workflow alterations, volume reductions, and strict infection control strategies. Despite the ongoing challenges, considerable knowledge has been gained that will guide future innovations. The aim of this review is to provide the latest evidence on the role of imaging in the diagnosis of the multifaceted manifestations of COVID-19, and to discuss the implications of the pandemic on radiology departments globally, including infection control strategies and delays in cancer screening. Lastly, the promising contribution of artificial intelligence in the COVID-19 pandemic is explored.

T2-mapping increase is the prevalent imaging biomarker of myocardial involvement in active COVID-19: a Cardiovascular Magnetic Resonance study

BACKGROUND

Early detection of myocardial involvement can be relevant in coronavirus disease 2019 (COVID-19) patients to timely target symptomatic treatment and decrease the occurrence of the cardiac sequelae of the infection. The aim of the present study was to assess the clinical value of cardiovascular magnetic resonance (CMR) in characterizing myocardial damage in active COVID-19 patients, through the correlation between qualitative and quantitative imaging biomarkers with clinical and laboratory evidence of myocardial injury.

METHODS

In this retrospective observational cohort study, we enrolled 27 patients with diagnosis of active COVID-19 and suspected cardiac involvement, referred to our institution for CMR between March 2020 and January 2021. Clinical and laboratory characteristics, including high sensitivity troponin T (hs-cTnT), and CMR imaging data were obtained. Relationships between CMR parameters, clinical and laboratory findings were explored. Comparisons were made with age-, sex- and risk factor-matched control group of 27 individuals, including healthy controls and patients without other signs or history of myocardial disease, who underwent CMR examination between January 2020 and January 2021.

RESULTS

The median (IQR) time interval between COVID-19 diagnosis and CMR examination was 20 (13.5-31.5) days. Hs-cTnT values were collected within 24 h prior to CMR and resulted abnormally increased in 18 patients (66.6%). A total of 20 cases (74%) presented tissue signal abnormalities, including increased myocardial native T1 (n = 11), myocardial T2 (n = 14) and extracellular volume fraction (ECV) (n = 10), late gadolinium enhancement (LGE) (n = 12) or pericardial enhancement (n = 2). A CMR diagnosis of myocarditis was established in 9 (33.3%), pericarditis in 2 (7.4%) and myocardial infarction with non-obstructive coronary arteries in 3 (11.11%) patients. T2 mapping values showed a moderate positive linear correlation with Hs-cTnT (r = 0.58; p = 0.002). A high degree positive linear correlation between ECV and Hs-cTnT was also found (r 0.77; p < 0.001).

CONCLUSIONS

CMR allows in vivo recognition and characterization of myocardial damage in a cohort of selected COVID-19 individuals by means of a multiparametric scanning protocol including conventional imaging and T1-T2 mapping sequences. Abnormal T2 mapping was the most commonly abnormality observed in our cohort and positively correlated with hs-cTnT values, reflecting the predominant edematous changes characterizing the active phase of disease.

Complications in COVID-19 patients: Characteristics of pulmonary embolism

Objective

The purpose of this study is to evaluate chest CT imaging features, clinical characteristics, laboratory values of COVID-19 patients who underwent CTA for suspected pulmonary embolism. We also examined whether clinical, laboratory or radiological characteristics could be associated with a higher rate of PE.

Materials and methods

This retrospective study included 84 consecutive patients with laboratory-confirmed SARS-CoV-2 who underwent CTA for suspected PE. The presence and localization of PE as well as the type and extent of pulmonary opacities on chest CT exams were examined and correlated with the information on comorbidities and laboratory values for all patients.

Results

Of the 84 patients, pulmonary embolism was discovered in 24 patients. We observed that 87% of PE was found to be in lung parenchyma affected by COVID-19 pneumonia. Compared with no-PE patients, PE patients showed an overall greater lung involvement by consolidation (p = 0.02) and GGO (p < 0.01) and a higher level of D-Dimer (p < 0,01). Moreover, the PE group showed a lower level of saturation (p = 0,01) and required more hospitalization (p < 0,01).

Conclusion

Our study showed a high incidence of PE in COVID-19 pneumonia. In 87% of patients, PE was found in lung parenchyma affected by COVID-19 pneumonia with a worse CT severity score and a greater number of lung lobar involvement compared with non-PE patients. CT severity, lower level of saturation, and a rise in D-dimer levels could be an indication for a CTPA.

Advances in knowledge

Certain findings of non-contrast chest CT could be an indication for a CTPA.

The importance of heart and brain imaging in children and adolescents with Multisystem Inflammatory Syndrome in Children (MIS-C)

Multisystem Inflammatory Syndrome in Children (MIS-C) recently reported in a minority of children affected by SARS-CoV-2, mimics Kawasaki disease (KD), a medium vessel vasculitis of unknown cause. In contrast to acute COVID-19 infection, which is usually mild in children, 68% of patients with MIS-C will need intensive care unit. Myocarditis and coronary artery ectasia/aneurysm are included between the main cardiovascular complications in MIS-C. Therefore, close clinical assessment is need it both at diagnosis and during follow-up. Echocardiography is the cornerstone modality for myocardial function and coronary artery evaluation in the acute phase. Cardiovascular magnetic resonance (CMR) detects diffuse myocardial inflammation including oedema/fibrosis, myocardial perfusion and coronary arteries anatomy during the convalescence and in adolescents, where echocardiography may provide inadequate images. Brain involvement in MIS-C is less frequent compared to cardiovascular disease. However, it is not unusual and should be monitored by clinical evaluation and brain magnetic resonance (MRI), as we still do not know its effect in brain development. Brain MRI in MIS-C shows T2-hyperintense lesions associated with restricted diffusion and bilateral thalamic lesions. To conclude, MIS-C is a multisystem disease affecting many vital organs, such as heart and brain. Clinical awareness, application of innovative, high technology imaging modalities and advanced treatment protocols including supportive and anti-inflammatory medication will help physicians to prevent the dreadful complications of MIS-C.

Multimodality Imaging for Cardiac Evaluation in Patients with COVID-19

Purpose of Review

A growing number of cardiovascular manifestations resulting from the novel SARS-CoV-2 coronavirus (COVID-19) have been described since the beginning of this global pandemic. Acute myocardial injury is common in this population and is associated with higher rates of morbidity and mortality. The focus of this review centers on the recent applications of multimodality imaging in the diagnosis and management of COVID-19-related cardiovascular conditions.

Recent Findings

In addition to standard cardiac imaging techniques such as transthoracic echocardiography, other modalities including computed tomography and cardiac magnetic resonance imaging have emerged as useful adjuncts in select patients with COVID-19 infection, particularly those with suspected ischemic and nonischemic myocardial injury. Data have also emerged suggesting lasting COVID-19 subclinical cardiac effects, which may have long-term prognostic implications.

Summary

With the spectrum of COVID-19 cardiovascular manifestations observed thus far, it is important for clinicians to recognize the role, strengths, and limitations of multimodality imaging techniques in this patient population.

A Novel Block Imaging Technique Using Nine Artificial Intelligence Models for COVID-19 Disease Classification, Characterization and Severity Measurement in Lung Computed Tomography Scans on an Italian Cohort

Computer Tomography (CT) is currently being adapted for visualization of COVID-19 lung damage. Manual classification and characterization of COVID-19 may be biased depending on the expert's opinion. Artificial Intelligence has recently penetrated COVID-19, especially deep learning paradigms. There are nine kinds of classification systems in this study, namely one deep learning-based CNN, five kinds of transfer learning (TL) systems namely VGG16, DenseNet121, DenseNet169, DenseNet201 and MobileNet, three kinds of machine-learning (ML) systems, namely artificial neural network (ANN), decision tree (DT), and random forest (RF) that have been designed for classification of COVID-19 segmented CT lung against Controls. Three kinds of characterization systems were developed namely (a) Block imaging for COVID-19 severity index (CSI); (b) Bispectrum analysis; and (c) Block Entropy. A cohort of Italian patients with 30 controls (990 slices) and 30 COVID-19 patients (705 slices) was used to test the performance of three types of classifiers. Using K10 protocol (90% training and 10% testing), the best accuracy and AUC was for DCNN and RF pairs were 99.41 ± 5.12%, 0.991 (p < 0.0001), and 99.41 ± 0.62%, 0.988 (p < 0.0001), respectively, followed by other ML and TL classifiers. We show that diagnostics odds ratio (DOR) was higher for DL compared to ML, and both, Bispecturm and Block Entropy shows higher values for COVID-19 patients. CSI shows an association with Ground Glass Opacities (0.9146, p < 0.0001). Our hypothesis holds true that deep learning shows superior performance compared to machine learning models. Block imaging is a powerful novel approach for pinpointing COVID-19 severity and is clinically validated.