New Cardiac Imaging Algorithms to Diagnose Constrictive Pericarditis Versus Restrictive Cardiomyopathy

Echocardiography-Based Deep Learning Model to Differentiate Constrictive Pericarditis and Restrictive Cardiomyopathy

BACKGROUND

Constrictive pericarditis (CP) is an uncommon but reversible cause of diastolic heart failure if appropriately identified and treated. However, its diagnosis remains a challenge for clinicians. Artificial intelligence may enhance the identification of CP.

OBJECTIVES

The authors proposed a deep learning approach based on transthoracic echocardiography to differentiate CP from restrictive cardiomyopathy.

METHODS

Patients with a confirmed diagnosis of CP and cardiac amyloidosis (CA) (as the representative disease of restrictive cardiomyopathy) at Mayo Clinic Rochester from January 2003 to December 2021 were identified to extract baseline demographics. The apical 4-chamber view from transthoracic echocardiography studies was used as input data. The patients were split into a 60:20:20 ratio for training, validation, and held-out test sets of the ResNet50 deep learning model. The model performance (differentiating CP and CA) was evaluated in the test set with the area under the curve. GradCAM was used for model interpretation.

RESULTS

A total of 381 patients were identified, including 184 (48.3%) CP, and 197 (51.7%) CA cases. The mean age was 68.7 ± 11.4 years, and 72.8% were male. ResNet50 had a performance with an area under the curve of 0.97 to differentiate the 2-class classification task (CP vs CA). The GradCAM heatmap showed activation around the ventricular septal area.

CONCLUSIONS

With a standard apical 4-chamber view, our artificial intelligence model provides a platform to facilitate the detection of CP, allowing for improved workflow efficiency and prompt referral for more advanced evaluation and intervention of CP.

Multimodality Imaging in Differentiating Constrictive Pericarditis From Restrictive Cardiomyopathy: A Comprehensive Overview for Clinicians and Imagers

In the evaluation of heart failure, 2 differential diagnostic considerations include constrictive pericarditis and restrictive cardiomyopathy. The often outwardly similar clinical presentation of these 2 pathologic entities routinely renders their clinical distinction difficult. Consequently, initial assessment requires a keen understanding of their separate pathophysiology, epidemiology, and hemodynamic effects. Following a detailed clinical evaluation, further assessment initially rests on comprehensive echocardiographic investigation, including detailed Doppler evaluation. With the combination of mitral inflow characterization, tissue Doppler assessment, and hepatic vein interrogation, initial differentiation of constrictive pericarditis and restrictive cardiomyopathy is often possible with high sensitivity and specificity. In conjunction with a compatible clinical presentation, successful differentiation enables both an accurate diagnosis and subsequent targeted management. In certain cases, however, the diagnosis remains unclear despite echocardiographic assessment, and additional evaluation is required. With advances in noninvasive tools, such evaluation can often continue in a stepwise, algorithmic fashion noninvasively, including both cross-sectional and nuclear imaging. Should this additional evaluation itself prove insufficient, invasive assessment with appropriate expertise may ultimately be necessary.

[Imagen multimodal de la pericarditis constrictiva: reporte de un caso de cirrosis cardiaca]

Constrictive pericarditis is a rare cause of ascites and cardiac cirrhosis. We present the case of a 36-year-old male patient with a history of cirrhosis of unknown etiology, who consulted for refractory ascites, dyspnea, and lower limb swelling. Echocardiography determined constrictive pericarditis, which was corroborated by the findings of computed tomography. The clinical and hemodynamic worsening of the patient led to an emergency pericardiectomy with satisfactory recovery. This report shows a severe clinical consequence of constrictive pericarditis, cardiac cirrhosis, which was reversible with pericardial extirpation. Multimodal imaging was essential in the diagnosis of constrictive pericarditis.

Echocardiographic Differentiation of Pericardial Constriction and Left Ventricular Restriction

PURPOSE OF REVIEW

Overlapping hemodynamics in constrictive pericarditis (CP) and restrictive cardiomyopathy (RCM) often pose difficulties in establishing accurate diagnosis. Echocardiography is the first-line imaging modality used for this purpose, but no single echocardiographic parameter is sufficiently robust for distinguishing between the two conditions. The newer developments may improve the diagnostic accuracy of echocardiography in this setting.

RECENT FINDINGS

Recent studies have validated multiparametric algorithms, based on conventional echocardiographic parameters, which enable high sensitivity and specificity for distinguishing between CP and RCM. In addition, myocardial deformation analysis using speckle-tracking echocardiography has revealed distinct pattern of abnormalities in the two conditions. CP is characterized by impaired left ventricular apical rotation with relatively preserved longitudinal strain, esp. of ventricular and atrial septum. In contrast, RCM results in global and marked impairment of left ventricular longitudinal strain with initially preserved circumferential mechanics. Combining multiple echocardiographic parameters into step-wise algorithms and incorporation of myocardial deformation analysis help improve the diagnostic accuracy of echocardiography for distinguishing between CP and RCM. The use of machine-learning may allow easy integration of a wide range of echocardiographic and clinical parameters to permit accurate, automated diagnosis, with less dependence on the user expertise.

Intelligent diagnosis of left ventricular hypertrophy using transthoracic echocardiography videos

PURPOSE

Left ventricular hypertrophy (LVH) is an independent risk factor for cardiovascular events and mortality. Pathological LVH can be caused by various diseases. In this study, we explored the possibility of using time and frequency domain analysis of myocardial radiomics features for patients with LVH in differentiating hypertrophic cardiomyopathy (HCM), hypertensive heart disease (HHD) and uremic cardiomyopathy (UCM) based on transthoracic echocardiography (TTE). This was the first study to explore TTE myocardial time and frequency domain analyses for multiple LVH etiology differentiation.

MATERIALS AND METHODS

We proposed an artificially intelligent diagnosis system based on radiomics techniques for differentiating HCM, HHD and UCM on TTE videos of the apical four-chamber view, which mainly included interventricular septum (IVS) segmentation, feature extraction and classification. We used two independent cohorts, one with 150 patients, including 50 HHD, 50 HCM and 50 UCM, for segmentation training and testing, and another with 149 patients (namely the main cohort), including 50 HHD, 46 HCM and 53 UCM, for classification training and testing after segmentation and feature extraction. Firstly, the U-Net, Residual U-Net (ResUNet) and nnU-Net were trained and tested to segment the IVS on TTE still images in the first cohort. Then the trained model with the best segmentation performance was further used for IVS prediction of ordered TTE images in video sequences in the main cohort. The post-processing was used to eliminate the noisy debris by selecting the maximum connected region and smoothing the edges of the predicted IVS region. Secondly, static radiomics features were extracted from the IVS of ordered TTE images in each video sequence, and subsequently the time and frequency domain features were further extracted from each time series of a static radiomics feature in the video sequence. Finally, the point-wise gated Boltzmann machine (PGBM) was used to learn and fuse the time and frequency domain features, and the support vector machine was used to classify the learned features for LVH diagnosis. The classification was performed with five-fold cross validation.

RESULTS

The ResUNet showed the best segmentation performance, with Dice coefficient, sensitivity, specificity and accuracy of 0.817, 76.3%, 99.6% and 98.6%, respectively. With post-processing, the Dice coefficient, sensitivity, specificity and accuracy of the ResUNet were further improved to 0.839, 77.0%, 99.8%, and 98.8%, respectively. The classification areas under the receiver operating characteristic curves (AUCs) were 0.838 ± 0.049 for HHD vs. HCM, 0.868 ± 0.042 for HCM vs. UCM and 0.701 ± 0.140 for HHD vs. UCM.

CONCLUSION

In this work, we proposed an intelligent identification system for LVH etiology classification based on routine TTE video images with good diagnostic performance. This deep learning method is feasible in automatic TTE images interpretation and expected to assist clinicians in detecting the primary cause of LVH.

Big Data in cardiac surgery: real world and perspectives

Big Data, and the derived analysis techniques, such as artificial intelligence and machine learning, have been considered a revolution in the modern practice of medicine. Big Data comes from multiple sources, encompassing electronic health records, clinical studies, imaging data, registries, administrative databases, patient-reported outcomes and OMICS profiles. The main objective of such analyses is to unveil hidden associations and patterns. In cardiac surgery, the main targets for the use of Big Data are the construction of predictive models to recognize patterns or associations better representing the individual risk or prognosis compared to classical surgical risk scores. The results of these studies contributed to kindle the interest for personalized medicine and contributed to recognize the limitations of randomized controlled trials in representing the real world. However, the main sources of evidence for guidelines and recommendations remain RCTs and meta-analysis. The extent of the revolution of Big Data and new analytical models in cardiac surgery is yet to be determined.

The role of artificial intelligence in paediatric cardiovascular magnetic resonance imaging

Artificial intelligence (AI) offers the potential to change many aspects of paediatric cardiac imaging. At present, there are only a few clinically validated examples of AI applications in this field. This review focuses on the use of AI in paediatric cardiovascular MRI, using examples from paediatric cardiovascular MRI, adult cardiovascular MRI and other radiologic experience.

Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging

Artificial intelligence (AI) has transformed key aspects of human life. Machine learning (ML), which is a subset of AI wherein machines autonomously acquire information by extracting patterns from large databases, has been increasingly used within the medical community, and specifically within the domain of cardiovascular diseases. In this review, we present a brief overview of ML methodologies that are used for the construction of inferential and predictive data-driven models. We highlight several domains of ML application such as echocardiography, electrocardiography, and recently developed non-invasive imaging modalities such as coronary artery calcium scoring and coronary computed tomography angiography. We conclude by reviewing the limitations associated with contemporary application of ML algorithms within the cardiovascular disease field.

The Next Frontier in Pediatric Cardiology: Artificial Intelligence

Artificial intelligence (AI) in the last decade centered primarily around digitizing and incorporating the large volumes of patient data from electronic health records. AI is now poised to make the next step in health care integration, with precision medicine, imaging support, and development of individual health trends with the popularization of wearable devices. Future clinical pediatric cardiologists will use AI as an adjunct in delivering optimum patient care, with the help of accurate predictive risk calculators, continual health monitoring from wearables, and precision medicine. Physicians must also protect their patients' health information from monetization or exploitation.

Biventricular imaging markers to predict outcomes in non-compaction cardiomyopathy: a machine learning study

Aims

Left ventricular non‐compaction cardiomyopathy (LVNC) is a genetic heart disease, with heart failure, arrhythmias, and embolic events as main clinical manifestations. The goal of this study was to analyse a large set of echocardiographic (echo) and cardiac magnetic resonance imaging (CMRI) parameters using machine learning (ML) techniques to find imaging predictors of clinical outcomes in a long‐term follow‐up of LVNC patients.

Methods and results

Patients with echo and/or CMRI criteria of LVNC, followed from January 2011 to December 2017 in the heart failure section of a tertiary referral cardiologic hospital, were enrolled in a retrospective study. Two‐dimensional colour Doppler echocardiography and subsequent CMRI were carried out. Twenty‐four hour Holter monitoring was also performed in all patients. Death, cardiac transplantation, heart failure hospitalization, aborted sudden cardiac death, complex ventricular arrhythmias (sustained and non‐sustained ventricular tachycardia), and embolisms (i.e. stroke, pulmonary thromboembolism and/or peripheral arterial embolism) were registered and were referred to as major adverse cardiovascular events (MACEs) in this study. Recruited for the study were 108 LVNC patients, aged 38.3 ± 15.5 years, 48.1% men, diagnosed by echo and CMRI criteria. They were followed for 5.8 ± 3.9 years, and MACEs were registered. CMRI and echo parameters were analysed via a supervised ML methodology. Forty‐seven (43.5%) patients had at least one MACE. The best performance of imaging variables was achieved by combining four parameters: left ventricular (LV) ejection fraction (by CMRI), right ventricular (RV) end‐systolic volume (by CMRI), RV systolic dysfunction (by echo), and RV lower diameter (by CMRI) with accuracy, sensitivity, and specificity rates of 75.5%, 77%, 75%, respectively.

Conclusions

Our findings show the importance of biventricular assessment to detect the severity of this cardiomyopathy and to plan for early clinical intervention. In addition, this study shows that even patients with normal LV function and negative late gadolinium enhancement had MACE. ML is a promising tool for analysing a large set of parameters to stratify and predict prognosis in LVNC patients.

Clinical Applications of Echo Strain Imaging: a Current Appraisal

PURPOSE OF REVIEW

This article reviews recent advances in echocardiographic strain imaging, particularly in its ability to prognosticate in cardiovascular outcomes and impact clinical decision making.

RECENT FINDINGS

Strain has been proposed as a sensitive tool in detecting early ventricular dysfunction. Left ventricular global longitudinal strain (LV-GLS) detects subtle changes in myocardial function, often not quantifiable by ejection fraction alone. Thus, LV-GLS provides the opportunity for early decision-making, and the implementation of more effective treatments, improving outcomes in a variety of diseases such as valvular heart diseases, cardio-oncology, ischemic heart disease, cardiomyopathies, heart transplantation, and pericardial diseases and cardiomyopathies. Strain is a promising tool for the early detection of myocardial dysfunction in patients with preserved left ventricular ejection fraction and can prognosticate long-term outcomes.

Artificial intelligence and echocardiography

Echocardiography plays a crucial role in the diagnosis and management of cardiovascular disease. However, interpretation remains largely reliant on the subjective expertise of the operator. As a result inter-operator variability and experience can lead to incorrect diagnoses. Artificial intelligence (AI) technologies provide new possibilities for echocardiography to generate accurate, consistent and automated interpretation of echocardiograms, thus potentially reducing the risk of human error. In this review, we discuss a subfield of AI relevant to image interpretation, called machine learning, and its potential to enhance the diagnostic performance of echocardiography. We discuss recent applications of these methods and future directions for AI-assisted interpretation of echocardiograms. The research suggests it is feasible to apply machine learning models to provide rapid, highly accurate and consistent assessment of echocardiograms, comparable to clinicians. These algorithms are capable of accurately quantifying a wide range of features, such as the severity of valvular heart disease or the ischaemic burden in patients with coronary artery disease. However, the applications and their use are still in their infancy within the field of echocardiography. Research to refine methods and validate their use for automation, quantification and diagnosis are in progress. Widespread adoption of robust AI tools in clinical echocardiography practice should follow and have the potential to deliver significant benefits for patient outcome.