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

Applications of Artificial Intelligence for the Prediction and Diagnosis of Cancer Therapy-Related Cardiac Dysfunction in Oncology Patients

Cardiovascular diseases and cancer are the leading causes of morbidity and mortality in modern society. Expanding cancer therapies that have improved prognosis may also be associated with cardiotoxicity, and extended life span after survivorship is associated with the increasing prevalence of cardiovascular disease. As such, the field of cardio-oncology has been rapidly expanding, with an aim to identify cardiotoxicity and cardiac disease early in a patient who is receiving treatment for cancer or is in survivorship. Artificial intelligence is revolutionizing modern medicine with its ability to identify cardiac disease early. This article comprehensively reviews applications of artificial intelligence specifically applied to electrocardiograms, echocardiography, cardiac magnetic resonance imaging, and nuclear imaging to predict cardiac toxicity in the setting of cancer therapies, with a view to reduce early complications and cardiac side effects from cancer therapies such as chemotherapy, radiation therapy, or immunotherapy.

Evolving Strategies in Cardiac Amyloidosis: From Mechanistic Discoveries to Diagnostic and Therapeutic Advances

Diagnosis and treatment of cardiac amyloidosis have rapidly evolved over the past decade by harnessing mechanisms of disease pathogenesis. Cardiac amyloidosis is caused by myocardial deposition of fibrils formed by misfolded proteins, namely transthyretin (ATTR) and immunoglobulin light chains (AL). Advances in noninvasive imaging have revolutionized diagnosis of ATTR cardiomyopathy (CM). Novel treatments for ATTR-CM utilize a range of therapeutic techniques, including protein stabilizers, interfering RNA, gene editing, and monoclonal antibodies. AL-CM, primarily driven by plasma cell dyscrasias, requires treatment with chemotherapy and consideration for autologous stem cell transplant. These incredible advances aim to improve patient outcomes in cardiac amyloidosis.

Echocardiographic Updates in the Assessment of Cardiomyopathy

PURPOSE OF REVIEW

This review aims to provide an updated overview of the role of echocardiography in the assessment of cardiomyopathies, highlighting recent findings and technological advancements.

RECENT FINDINGS

Over the past few years, significant advancements in echocardiographic techniques have improved diagnostic accuracy and provided important prognostic value in the assessment of cardiomyopathies. Cardiomyopathy is a group of diseases affecting the heart muscle. Echocardiography, a non-invasive imaging modality provides crucial information on cardiac structure, function, and hemodynamics. Recent advancements, including strain imaging, speckle-tracking, and 3D echocardiography enhance the precision of structural and functional assessments, while artificial intelligence integration improves diagnostic accuracy and workflow efficiency. These advancements not only refine diagnostic capabilities but also provide prognostic insights and facilitate better patient outcomes.

Revolutionizing Cardiology: The Role of Artificial Intelligence in Echocardiography

Background: Artificial intelligence (AI) in echocardiography represents a transformative advancement in cardiology, addressing longstanding challenges in cardiac diagnostics. Echocardiography has traditionally been limited by operator-dependent variability and subjective interpretation, which impact diagnostic reliability. This study evaluates the role of AI, particularly machine learning (ML), in enhancing the accuracy and consistency of echocardiographic image analysis and its potential to complement clinical expertise. Methods: A comprehensive review of existing literature was conducted to analyze the integration of AI into echocardiography. Key AI functionalities, such as image acquisition, standard view classification, cardiac chamber segmentation, structural quantification, and functional assessment, were assessed. Comparisons with traditional imaging modalities like computed tomography (CT), nuclear imaging, and magnetic resonance imaging (MRI) were also explored. Results: AI algorithms demonstrated expert-level accuracy in diagnosing conditions such as cardiomyopathies while reducing operator variability and enhancing diagnostic consistency. The application of ML was particularly effective in automating image analysis and minimizing human error, addressing the limitations of subjective operator expertise. Conclusions: The integration of AI into echocardiography marks a pivotal shift in cardiovascular diagnostics, offering enhanced accuracy, consistency, and reliability. By addressing operator variability and improving diagnostic performance, AI has the potential to elevate patient care and herald a new era in cardiology.

A very rare phenotype of immunoglobulin G4-related disease that was manifested as constrictive pericarditis: a case report

Background

Constrictive pericarditis (CP) can arise from various causes, including post-operative degeneration, tuberculosis, and sequelae of pericarditis. Immunoglobulin (Ig) G4-related disease is a rare but recognized cause of CP. However, the specific mechanisms underlying these aetiologies and pathologies remain unclear.

Case summary

A 67-year-old man presented with a 6-month history of bilateral leg oedema, anorexia, and dyspnoea on exertion. Computed tomography (CT) revealed significant pericardial thickening without calcification, right pleural effusion, and ascites. Echocardiography demonstrated a reduced left ventricular ejection fraction and pericardial thickening. The early diastolic mitral annular tissue velocity (e') was preserved as 11.7 cm/s, despite inferior vena cava dilation. Respiratory variations in mitral inflow velocities and septal bounces were unremarkable. Cardiac catheterization further showed a 'dip and plateau' pattern with equalization of bilateral ventricular end-diastolic pressure. A preliminary diagnosis of CP was made, and pericardiectomy was performed, increasing the cardiac index from 2.0 to 3.0 L/min/m2. Pathological examination revealed marked IgG4-positive plasma cell infiltration and tissue fibrosis. Additionally, the patient's post-operative serum IgG4 level was 679 mg/dL. Given these findings, IgG4-related CP without involvement of other organs was determined as the definitive diagnosis. His clinical status improved without requiring corticosteroid therapy.

Discussion

Optimal therapy for IgG4-related CP remains elusive due to its rarity. Potential therapeutic options include pericardiectomy, pericardiotomy, and corticosteroid therapy. Further examination through the accumulation of similar cases is crucial to establish definitive treatment approaches for this condition.

Interoception, cardiac health, and heart failure: The potential for artificial intelligence (AI)-driven diagnosis and treatment

"I see, I forget, I read aloud, I remember, and when I do read purposefully by writing it, I do not forget it." This phenomenon is known as "interoception" and refers to the sensing and interpretation of internal body signals, allowing the brain to communicate with various body systems. Dysfunction in interoception is associated with cardiovascular disorders. We delve into the concept of interoception and its impact on heart failure (HF) by reviewing and exploring neural mechanisms underlying interoceptive processing. Furthermore, we review the potential of artificial intelligence (AI) in diagnosis, biomarker development, and HF treatment. In the context of HF, AI algorithms can analyze and interpret complex interoceptive data, providing valuable insights for diagnosis and treatment. These algorithms can identify patterns of disease markers that can contribute to early detection and diagnosis, enabling timely intervention and improved outcomes. These biomarkers hold significant potential in improving the precision/efficacy of HF. Additionally, AI-powered technologies offer promising avenues for treatment. By leveraging patient data, AI can personalize therapeutic interventions. AI-driven technologies such as remote monitoring devices and wearable sensors enable the monitoring of patients' health. By harnessing the power of AI, we should aim to advance the diagnosis and treatment strategies for HF. This review explores the potential of AI in diagnosing, developing biomarkers, and managing HF.

Non-invasive determination of gene expression in placental tissue using maternal plasma cell-free DNA fragmentation characters

BACKGROUND

The expression profiles of placental genes are crucial for understanding the pathogenesis of fetal development and placental-origin pregnancy syndromes. However, owing to ethical limitations and the risks of puncture sampling, it is difficult to obtain placental tissue samples repeatedly, continuously, multiple times, or in real time. Establishing a non-invasive method for predicting placental gene expression profiles through maternal plasma cell-free DNA (cfDNA) sequencing, which carries information about the source tissue and gene expression, can potentially solve this problem.

METHODS

Peripheral blood and placental samples were collected simultaneously from pregnant women who underwent cesarean section. Deep sequencing was performed on the separated plasma cfDNA and single-cell sequencing was performed on peripheral blood mononuclear cells (PBMC), chorioamniotic membranes (CAM), placental villi (PV), and decidua basalis (DB). The aggregation of corresponding information for each gene was combined with the transcriptome of PBMCs and a differential resolution transcriptome of the placenta. This combined information was then utilized for the construction of gene expression prediction models. After training, all models evaluated the correlation between the predicted and actual gene expression levels using external test set data.

RESULTS

From five women, more than 20 million reads were obtained using deep sequencing for plasma cfDNA; PBMCs obtained 32,401 single-cell expression profiles; and placental tissue obtained 156,546 single-cell expression profiles (59,069, 44,921, and 52,556 for CAM, PV, and DB, respectively). The cells in the PBMC and placenta were clustered and annotated into five and eight cell types, respectively. A "DEPICT" gene expression prediction model was successfully constructed using deep neural networks. The predicted correlation coefficients were 0.75 in PBMCs, 0.84 in the placenta, and 0.78, 0.80, and 0.77 in CAM, BP, and PV respectively, and greater than 0.68 in different cell lines in the placenta.

CONCLUSION

The DEPICT model, which can noninvasively predict placental gene expression profiles based on maternal plasma cfDNA fragmentation characteristics, was constructed to overcome the limitation of the inability to obtain real-time placental gene expression profiles and to improve research on noninvasive prediction of placental origin pregnancy syndrome.

Advancements and applications of artificial intelligence in cardiovascular imaging: a comprehensive review

Artificial intelligence (AI) is transforming cardiovascular imaging by offering advancements across multiple modalities, including echocardiography, cardiac computed tomography (CCT), cardiovascular magnetic resonance (CMR), interventional cardiology, nuclear medicine, and electrophysiology. This review explores the clinical applications of AI within each of these areas, highlighting its ability to improve patient selection, reduce image acquisition time, enhance image optimization, facilitate the integration of data from different imaging modality and clinical sources, improve diagnosis and risk stratification. Moreover, we illustrate both the advantages and the limitations of AI across these modalities, acknowledging that while AI can significantly aid in diagnosis, risk stratification, and workflow efficiency, it cannot replace the expertise of cardiologists. Instead, AI serves as a powerful tool to streamline routine tasks, allowing clinicians to focus on complex cases where human judgement remains essential. By accelerating image interpretation and improving diagnostic accuracy, AI holds great potential to improve patient care and clinical decision-making in cardiovascular imaging.

Pericardial Diseases and Best Practices for Pericardiectomy: JACC State-of-the-Art Review

Remarkable advances have occurred in the understanding of the pathophysiology of pericardial diseases and the role of multimodality imaging in this field. Medical therapy and surgical options for pericardial diseases have also evolved substantially. Pericardiectomy is indicated for chronic or irreversible constrictive pericarditis, refractory recurrent pericarditis despite optimal medical therapy, or partial agenesis of the pericardium with a complication (eg, herniation). A multidisciplinary evaluation before pericardiectomy is essential for optimal patient outcomes. Overall, given the good outcomes reported, radical pericardiectomy on cardiopulmonary bypass, if feasible, is the preferred approach. Due to patient complexity, as well as the technical aspects of the surgery, pericardiectomy should be performed at high-volume centers that have the required expertise. The current review highlights the essential features of this multidisciplinary approach from diagnosis to recovery in patients undergoing pericardiectomy.

Cardiac Amyloidosis: A Comprehensive Review of Pathophysiology, Diagnostic Approach, Applications of Artificial Intelligence, and Management Strategies

Cardiac amyloidosis (CA) is a serious and often fatal condition caused by the accumulation of amyloid fibrils in the heart, leading to progressive heart failure. It involves the misfolding of normally soluble proteins into insoluble amyloid fibrils, with transthyretin and light-chain amyloidosis being the most common forms affecting the heart. Advances in diagnostics, especially cardiac magnetic resonance imaging and non-invasive techniques, have improved early detection and disease management. Artificial intelligence has emerged as a diagnostic tool for cardiac amyloidosis, improving accuracy and enabling earlier intervention through advanced imaging analysis and pattern recognition. Management strategies include volume control, specific pharmacotherapies like tafamidis, and addressing arrhythmias and advanced heart failure. However, further research is needed for novel therapeutic approaches, the long-term effectiveness of emerging treatments, and the optimization of artificial intelligence applications in clinical practice for better patient outcomes. The article aims to provide an overview of CA, outlining its pathophysiology, diagnostic advancements, the role of artificial intelligence, management strategies, and the need for further research.

Artificial Intelligence for Cardiovascular Care-Part 1: Advances: JACC Review Topic of the Week

Recent artificial intelligence (AI) advancements in cardiovascular care offer potential enhancements in diagnosis, treatment, and outcomes. Innovations to date focus on automating measurements, enhancing image quality, and detecting diseases using novel methods. Applications span wearables, electrocardiograms, echocardiography, angiography, genetics, and more. AI models detect diseases from electrocardiograms at accuracy not previously achieved by technology or human experts, including reduced ejection fraction, valvular heart disease, and other cardiomyopathies. However, AI's unique characteristics necessitate rigorous validation by addressing training methods, real-world efficacy, equity concerns, and long-term reliability. Despite an exponentially growing number of studies in cardiovascular AI, trials showing improvement in outcomes remain lacking. A number are currently underway. Embracing this rapidly evolving technology while setting a high evaluation benchmark will be crucial for cardiology to leverage AI to enhance patient care and the provider experience.

How Artificial Intelligence Can Enhance the Diagnosis of Cardiac Amyloidosis: A Review of Recent Advances and Challenges

Cardiac amyloidosis (CA) is an underdiagnosed form of infiltrative cardiomyopathy caused by abnormal amyloid fibrils deposited extracellularly in the myocardium and cardiac structures. There can be high variability in its clinical manifestations, and diagnosing CA requires expertise and often thorough evaluation; as such, the diagnosis of CA can be challenging and is often delayed. The application of artificial intelligence (AI) to different diagnostic modalities is rapidly expanding and transforming cardiovascular medicine. Advanced AI methods such as deep-learning convolutional neural networks (CNNs) may enhance the diagnostic process for CA by identifying patients at higher risk and potentially expediting the diagnosis of CA. In this review, we summarize the current state of AI applications to different diagnostic modalities used for the evaluation of CA, including their diagnostic and prognostic potential, and current challenges and limitations.

Artificial Intelligence in the Differential Diagnosis of Cardiomyopathy Phenotypes

Artificial intelligence (AI) is rapidly being applied to the medical field, especially in the cardiovascular domain. AI approaches have demonstrated their applicability in the detection, diagnosis, and management of several cardiovascular diseases, enhancing disease stratification and typing. Cardiomyopathies are a leading cause of heart failure and life-threatening ventricular arrhythmias. Identifying the etiologies is fundamental for the management and diagnostic pathway of these heart muscle diseases, requiring the integration of various data, including personal and family history, clinical examination, electrocardiography, and laboratory investigations, as well as multimodality imaging, making the clinical diagnosis challenging. In this scenario, AI has demonstrated its capability to capture subtle connections from a multitude of multiparametric datasets, enabling the discovery of hidden relationships in data and handling more complex tasks than traditional methods. This review aims to present a comprehensive overview of the main concepts related to AI and its subset. Additionally, we review the existing literature on AI-based models in the differential diagnosis of cardiomyopathy phenotypes, and we finally examine the advantages and limitations of these AI approaches.