Quality assurance of late gadolinium enhancement cardiac magnetic resonance images: a deep learning classifier for confidence in the presence or absence of abnormality with potential to prompt real-time image optimization.

BACKGROUND: Late gadolinium enhancement (LGE) of the myocardium has significant diagnostic and prognostic implications, with even small areas of enhancement being important. Distinguishing between definitely normal and definitely abnormal LGE images is usually straightforward, but diagnostic uncertainty arises when reporters are not sure whether the observed LGE is genuine or not. This uncertainty might be resolved by repetition (to remove artifact) or further acquisition of intersecting images, but this must take place before the scan finishes. Real-time quality assurance by humans is a complex task requiring training and experience, so being able to identify which images have an intermediate likelihood of LGE while the scan is ongoing, without the presence of an expert is of high value. This decision-support could prompt immediate image optimization or acquisition of supplementary images to confirm or refute the presence of genuine LGE. This could reduce ambiguity in reports. METHODS: Short-axis, phase-sensitive inversion recovery late gadolinium images were extracted from our clinical cardiac magnetic resonance (CMR) database and shuffled. Two, independent, blinded experts scored each individual slice for "LGE likelihood" on a visual analog scale, from 0 (absolute certainty of no LGE) to 100 (absolute certainty of LGE), with 50 representing clinical equipoise. The scored images were split into two classes-either "high certainty" of whether LGE was present or not, or "low certainty." The dataset was split into training, validation, and test sets (70:15:15). A deep learning binary classifier based on the EfficientNetV2 convolutional neural network architecture was trained to distinguish between these categories. Classifier performance on the test set was evaluated by calculating the accuracy, precision, recall, F1-score, and area under the receiver operating characteristics curve (ROC AUC). Performance was also evaluated on an external test set of images from a different center. RESULTS: One thousand six hundred and forty-five images (from 272 patients) were labeled and split at the patient level into training (1151 images), validation (247 images), and test (247 images) sets for the deep learning binary classifier. Of these, 1208 images were "high certainty" (255 for LGE, 953 for no LGE), and 437 were "low certainty". An external test comprising 247 images from 41 patients from another center was also employed. After 100 epochs, the performance on the internal test set was accuracy = 0.94, recall = 0.80, precision = 0.97, F1-score = 0.87, and ROC AUC = 0.94. The classifier also performed robustly on the external test set (accuracy = 0.91, recall = 0.73, precision = 0.93, F1-score = 0.82, and ROC AUC = 0.91). These results were benchmarked against a reference inter-expert accuracy of 0.86. CONCLUSION: Deep learning shows potential to automate quality control of late gadolinium imaging in CMR. The ability to identify short-axis images with intermediate LGE likelihood in real-time may serve as a useful decision-support tool. This approach has the potential to guide immediate further imaging while the patient is still in the scanner, thereby reducing the frequency of recalls and inconclusive reports due to diagnostic indecision.

Accurate diagnosis of apical hypertrophic cardiomyopathy using explainable advanced electrocardiogram analysis.

AIMS: Typical electrocardiogram (ECG) features of apical hypertrophic cardiomyopathy (ApHCM) include tall R waves and deep or giant T-wave inversion in the precordial leads, but these features are not always present. The ECG is used as the gatekeeper to cardiac imaging for diagnosis. We tested whether explainable advanced ECG (A-ECG) could accurately diagnose ApHCM. METHODS AND RESULTS: Advanced ECG analysis was performed on standard resting 12-lead ECGs in patients with ApHCM [n = 75 overt, n = 32 relative (<15 mm hypertrophy); a subgroup of which underwent cardiovascular magnetic resonance (n = 92)], and comparator subjects (n = 2449), including healthy volunteers (n = 1672), patients with coronary artery disease (n = 372), left ventricular electrical remodelling (n = 108), ischaemic (n = 114) or non-ischaemic cardiomyopathy (n = 57), and asymmetrical septal hypertrophy HCM (n = 126). Multivariable logistic regression identified four A-ECG measures that together discriminated ApHCM from other diseases with high accuracy [area under the receiver operating characteristic (AUC) curve (bootstrapped 95% confidence interval) 0.982 (0.965-0.993)]. Linear discriminant analysis also diagnosed ApHCM with high accuracy [AUC 0.989 (0.986-0.991)]. CONCLUSION: Explainable A-ECG has excellent diagnostic accuracy for ApHCM, even when the hypertrophy is relative, with A-ECG analysis providing incremental diagnostic value over imaging alone. The electrical (ECG) and anatomical (wall thickness) disease features do not completely align, suggesting that future diagnostic and management strategies may incorporate both features.

Acute myocarditis: an overview of pathogenesis, diagnosis and management.

Acute myocarditis encompasses a diverse presentation of inflammatory cardiomyopathies with infectious and non-infectious triggers. The clinical presentation is heterogeneous, from subtle symptoms like mild chest pain to life-threatening fulminant heart failure requiring urgent advanced hemodynamic support. This review provides a comprehensive overview of the current state of knowledge regarding the pathogenesis, diagnostic approach, management strategies, and directions for future research in acute myocarditis. The pathogenesis of myocarditis involves interplay between the inciting factors and the subsequent host immune response. Infectious causes, especially cardiotropic viruses, are the most frequently identified precipitants. However, autoimmune processes independent of microbial triggers, as well as toxic myocardial injury from drugs, chemicals or metabolic derangements also contribute to the development of myocarditis through diverse mechanisms. Furthermore, medications like immune checkpoint inhibitor therapies are increasingly recognized as causes of myocarditis. Elucidating the nuances of viral, autoimmune, hypersensitivity, and toxic subtypes of myocarditis is key to guiding appropriate therapy. The heterogeneous clinical presentation coupled with non-specific symptoms creates diagnostic challenges. A multifaceted approach is required, incorporating clinical evaluation, electrocardiography, biomarkers, imaging studies, and endomyocardial biopsy. Cardiovascular magnetic resonance imaging has become pivotal for non-invasive assessment of myocardial inflammation and fibrosis. However, biopsy remains the gold standard for histological classification and definitively establishing the underlying etiology. Management relies on supportive care, while disease-specific therapies are limited. Although some patients recover well with conservative measures, severe or fulminant myocarditis necessitates aggressive interventions such as mechanical circulatory support devices and transplantation. While immunosuppression is beneficial in certain histological subtypes, clear evidence supporting antiviral or immunomodulatory therapies for the majority of acute viral myocarditis cases remains insufficient. Substantial knowledge gaps persist regarding validated diagnostic biomarkers, optimal imaging surveillance strategies, evidence-based medical therapies, and risk stratification schema. A deeper understanding of the immunopathological mechanisms, rigorous clinical trials of targeted therapies, and longitudinal outcome studies are imperative to advance management and improve the prognosis across the myocarditis spectrum.

Myocardial perfusion in cardiac amyloidosis.

AIMS: Cardiac involvement is the main driver of clinical outcomes in systemic amyloidosis and preliminary studies support the hypothesis that myocardial ischaemia contributes to cellular damage. The aims of this study were to assess the presence and mechanisms of myocardial ischaemia using cardiovascular magnetic resonance (CMR) with multiparametric mapping and histopathological assessment. METHODS AND RESULTS: Ninety-three patients with cardiac amyloidosis (CA) (light-chain amyloidosis n = 42, transthyretin amyloidosis n = 51) and 97 without CA (three-vessel coronary disease [3VD] n = 47, unobstructed coronary arteries n = 26, healthy volunteers [HV] n = 24) underwent quantitative stress perfusion CMR with myocardial blood flow (MBF) mapping. Twenty-four myocardial biopsies and three explanted hearts with CA were analysed histopathologically. Stress MBF was severely reduced in patients with CA with lower values than patients with 3VD, unobstructed coronary arteries and HV (CA: 1.04 ± 0.51 ml/min/g, 3VD: 1.35 ± 0.50 ml/min/g, unobstructed coronary arteries: 2.92 ± 0.52 ml/min/g, HV: 2.91 ± 0.73 ml/min/g; CA vs. 3VD p = 0.011, CA vs. unobstructed coronary arteries p < 0.001, CA vs. HV p < 0.001). Myocardial perfusion abnormalities correlated with amyloid burden, systolic and diastolic function, structural parameters and blood biomarkers (p < 0.05). Biopsies demonstrated abnormal vascular endothelial growth factor staining in cardiomyocytes and endothelial cells, which may be related to hypoxia conditions. Amyloid infiltration in intramural arteries was associated with severe lumen reduction and severe reduction in capillary density. CONCLUSION: Cardiac amyloidosis is associated with severe inducible myocardial ischaemia demonstrable by histology and CMR stress perfusion mapping. Histological evaluation indicates a complex pathophysiology, where in addition to systolic and diastolic dysfunction, amyloid infiltration of the epicardial arteries and disruption and rarefaction of the capillaries play a role in contributing to myocardial ischaemia.

The Impact of Bariatric Surgery on Coronary Microvascular Function Assessed Using Automated Quantitative Perfusion CMR.

BACKGROUND: Coronary microvascular function is impaired in patients with obesity, contributing to myocardial dysfunction and heart failure. Bariatric surgery decreases cardiovascular mortality and heart failure, but the mechanisms are unclear. OBJECTIVES: The authors studied the impact of bariatric surgery on coronary microvascular function in patients with obesity and its relationship with metabolic syndrome. METHODS: Fully automated quantitative perfusion cardiac magnetic resonance and metabolic markers were performed before and 6 months after bariatric surgery. RESULTS: Compared with age- and sex-matched healthy volunteers, 38 patients living with obesity had lower stress myocardial blood flow (MBF) (P = 0.001) and lower myocardial perfusion reserve (P < 0.001). A total of 27 participants underwent paired follow-up 6 months post-surgery. Metabolic abnormalities reduced significantly at follow-up including mean body mass index by 11 ± 3 kg/m2 (P < 0.001), glycated hemoglobin by 9 mmol/mol (Q1-Q3: 4-19 mmol/mol; P < 0.001), fasting insulin by 142 ± 131 pmol/L (P < 0.001), and hepatic fat fraction by 5.6% (2.6%-15.0%; P < 0.001). Stress MBF increased by 0.28 mL/g/min (-0.02 to 0.75 mL/g/min; P = 0.003) and myocardial perfusion reserve by 0.13 (-0.25 to 1.1; P = 0.036). The increase in stress MBF was lower in those with preoperative type 2 diabetes mellitus (0.1 mL/g/min [-0.09 to 0.46 mL/g/min] vs 0.75 mL/g/min [0.31-1.25 mL/g/min]; P = 0.002). Improvement in stress MBF was associated with reduction in fasting insulin (beta = -0.45 [95% CI: -0.05 to 0.90]; P = 0.03). CONCLUSIONS: Coronary microvascular function is impaired in patients with obesity, but can be improved significantly with bariatric surgery. Improvements in microvascular function are associated with improvements in insulin resistance but are attenuated in those with preoperative type 2 diabetes mellitus.